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2022-09-30

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Epic turmoil in the UK bond market. Why is the biggest "powder keg" pension?

2022-09-30

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Why is the biggest \"powder keg\" pension?","url":"https://stock-news.laohu8.com/highlight/detail?id=1110631384","media":"华尔街见闻","summary":"若养老基金大规模撤出英国国债，英国债市或面临灭顶之灾。","content":"<p><html><head></head><body>Author: Bu Shuqing</p><p>According to Wall Street News, one of the most important reasons for the Bank of England's intervention in the bond market on Wednesday is that British pension funds are facing large-scale margin calls. Investment banks and fund managers have warned the Bank of England in recent days that the margin call requirement could trigger a crash in the UK bond market, media cited people familiar with the matter.</p><p>The reason why pension funds can have a devastating power on the British bond market is that they hedge their debts with high leverage through debt-driven investment strategies. With the yield of gilt (Treasury Bond issued by the British government) continuing to soar, pension funds with trillion pounds of assets may sell gilt on a large scale to pay huge margin.</p><p>LDI + Highly Leveraged Hedges</p><p>Liability-driven investing (LDI) is the first big thunder laid by pension funds for the UK bond market.</p><p>Most pension funds in the UK have long used liability-driven investments to hedge liabilities (future payments to pension clients), a strategy through which pension funds can match their liabilities with the scheme's assets.</p><p>This can be illustrated in a simple model:</p><p>If a pension fund is required to pay a £1m pension to a client in 2046, the fund can buy gilt bonds that mature that year. If high-quality corporate bonds with higher yields are bought, the fund can use the extra money to invest in the stock market, real estate or other growth assets in addition to the bond purchase. Further,<b>Pension funds can use interest rate swaps or inflation swaps to match long-term liabilities.</b>This means that the pension fund only needs to invest the initial margin for trading, and the change direction of the Treasury Bond yield determines whether the fund pays or harvests the margin. Specifically, when the yield of Treasury Bond rises, the pension fund needs to pay a margin to the counterparty, and instead wins the margin of the counterparty. Therefore, pension funds that hedge through interest rate swaps face some leverage.</p><p>How big is this leverage?</p><p>Of the top 600 pension funds in the UK (with total assets of around £700bn), according to a 2019 survey conducted by the UK's pension regulator,<b>62% of pensions are leveraged through interest rate swaps, which account for 43% of all leveraged investments, and the maximum leverage level allowed is 1x to 7x!</b></p><p>Once yields go up, pension funds will face margin calls and be forced to offer more collateral.</p><p>Indeed, there were media reports as early as July that UK pension funds were facing margin calls. Over the past year, UK Treasury Bond yields have continued to rise, with the UK 30-year Treasury Bond yield soaring from 1.12% in October last year to 3.93% in July this year.</p><p><img src=\"https://static.tigerbbs.com/a7bfd9ed4efd73df53dfa7fe47cb6c4f\" tg-width=\"991\" tg-height=\"609\" referrerpolicy=\"no-referrer\" width=\"100%\" height=\"auto\"/></p><p>Britain's bond market may face destruction if pension funds withdraw on a large scale</p><p>The huge scale of funds is the second thunder laid by pension funds for the UK bond market.</p><p>While extremely risky, the LDI strategy helped UK pension funds expand rapidly in the days of falling gilt yields. UK assets held by UK pension funds through LDI strategies tripled to £1.5trillion in the 10 years to 2020, according to the Investment Association.</p><p>What concept is this number equivalent to?</p><p>After removing the Treasury Bond held by the Bank of England,<b>Assets held by pension funds account for 40% of the UK's institutional asset management market, two-thirds of the UK's GDP and the size of the entire gilt market.</b></p><p>It is unclear exactly how large the pension fund holds British Treasury Bond, but analysts say it is huge<b>If pension funds withdraw from the British Treasury Bond on a large scale, the British bond market may face a disaster.</b></p><p>Fuse: Britain's most aggressive tax cuts in half a century</p><p>On Friday, the UK announced its most aggressive tax cuts in half a century, which set off a sell-off wave in the UK gilt market, with the UK's 30-year Treasury Bond yield surging 120 basis points in just a few days.</p><p>Treasury Bond yields have soared, and the margin calls facing pension funds have risen.</p><p>Ben Gold, head of investments at pensions advisory firm XPS, estimates that UK pension funds have received at least £1bn of margin calls since the Government unveiled plans for tax cuts. He said about two-thirds of the 400 pension schemes his firm advises on have been affected.</p><p>In order to pay a huge deposit,<b>UK pension funds may have to dump large amounts of gilts and other liquid assets, further weighing on the UK bond market and pushing up Treasury Bond yields, which will cause the margin to be paid to snowball.</b></p><p>In this regard, Kerrin Rosenberg, chief investment officer of Cardano Investment Company, said:</p><p><b>Had the Bank of England not intervened today, gilt yields could have risen from 4.5% in the morning to 7-8%, in which case around 90% of UK pension funds would have run out of collateral and they would have been wiped out!</b>As previously analyzed by Wall Street, in order to prevent the large-scale escape of pension funds from the British Treasury Bond from bringing a fatal blow to the bond market, the Bank of England reversed its long-standing \"non-intervention\" strategy and announced unlimited purchase of British Treasury Bond.</p><p>The Bank of England issued a statement on Wednesday saying it would temporarily buy long-term UK Treasury Bond \"at any scale necessary\" to restore order to the UK bond market. Subsequently, the Bank of England said it would buy traditional British Treasury Bond with a remaining maturity of more than 20 years in the secondary market.</p><p>Affected by this, the British Treasury Bond skyrocketed, and the British 30-year Treasury Bond yield once fell by 100 basis points.</p><p></body></html></p>","source":"live_wallstreetcn","collect":0,"html":"<!DOCTYPE html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width,initial-scale=1.0,minimum-scale=1.0,maximum-scale=1.0,user-scalable=no\"/>\n<meta name=\"format-detection\" content=\"telephone=no,email=no,address=no\" />\n<title>Epic turmoil in the UK bond market. Why is the biggest \"powder keg\" pension?</title>\n<style type=\"text/css\">\na,abbr,acronym,address,applet,article,aside,audio,b,big,blockquote,body,canvas,caption,center,cite,code,dd,del,details,dfn,div,dl,dt,\nem,embed,fieldset,figcaption,figure,footer,form,h1,h2,h3,h4,h5,h6,header,hgroup,html,i,iframe,img,ins,kbd,label,legend,li,mark,menu,nav,\nobject,ol,output,p,pre,q,ruby,s,samp,section,small,span,strike,strong,sub,summary,sup,table,tbody,td,tfoot,th,thead,time,tr,tt,u,ul,var,video{ font:inherit;margin:0;padding:0;vertical-align:baseline;border:0 }\nbody{ font-size:16px; line-height:1.5; color:#999; background:transparent; }\n.wrapper{ overflow:hidden;word-break:break-all;padding:10px; }\nh1,h2{ font-weight:normal; line-height:1.35; margin-bottom:.6em; }\nh3,h4,h5,h6{ line-height:1.35; margin-bottom:1em; }\nh1{ font-size:24px; }\nh2{ font-size:20px; }\nh3{ font-size:18px; }\nh4{ font-size:16px; }\nh5{ font-size:14px; }\nh6{ font-size:12px; }\np,ul,ol,blockquote,dl,table{ margin:1.2em 0; }\nul,ol{ margin-left:2em; }\nul{ list-style:disc; }\nol{ list-style:decimal; }\nli,li p{ margin:10px 0;}\nimg{ max-width:100%;display:block;margin:0 auto 1em; }\nblockquote{ color:#B5B2B1; border-left:3px solid #aaa; padding:1em; }\nstrong,b{font-weight:bold;}\nem,i{font-style:italic;}\ntable{ width:100%;border-collapse:collapse;border-spacing:1px;margin:1em 0;font-size:.9em; }\nth,td{ padding:5px;text-align:left;border:1px solid #aaa; }\nth{ font-weight:bold;background:#5d5d5d; }\n.symbol-link{font-weight:bold;}\n/* header{ border-bottom:1px solid #494756; } */\n.title{ margin:0 0 8px;line-height:1.3;color:#ddd; }\n.meta {color:#5e5c6d;font-size:13px;margin:0 0 .5em; }\na{text-decoration:none; color:#2a4b87;}\n.meta .head { display: inline-block; overflow: hidden}\n.head .h-thumb { width: 30px; height: 30px; margin: 0; padding: 0; border-radius: 50%; float: left;}\n.head .h-content { margin: 0; padding: 0 0 0 9px; float: left;}\n.head .h-name {font-size: 13px; color: #eee; margin: 0;}\n.head .h-time {font-size: 12.5px; color: #7E829C; margin: 0;}\n.small {font-size: 12.5px; display: inline-block; transform: scale(0.9); -webkit-transform: scale(0.9); transform-origin: left; -webkit-transform-origin: left;}\n.smaller {font-size: 12.5px; display: inline-block; transform: scale(0.8); -webkit-transform: scale(0.8); transform-origin: left; -webkit-transform-origin: left;}\n.bt-text {font-size: 12px;margin: 1.5em 0 0 0}\n.bt-text p {margin: 0}\n</style>\n</head>\n<body>\n<div class=\"wrapper\">\n<header>\n<h2 class=\"title\">\nEpic turmoil in the UK bond market. Why is the biggest \"powder keg\" pension?\n</h2>\n<h4 class=\"meta\">\n<p class=\"head\">\n<strong class=\"h-name small\">华尔街见闻</strong><span class=\"h-time small\">2022-09-29 23:22</span>\n</p>\n</h4>\n</header>\n<article>\n<p><html><head></head><body>Author: Bu Shuqing</p><p>According to Wall Street News, one of the most important reasons for the Bank of England's intervention in the bond market on Wednesday is that British pension funds are facing large-scale margin calls. Investment banks and fund managers have warned the Bank of England in recent days that the margin call requirement could trigger a crash in the UK bond market, media cited people familiar with the matter.</p><p>The reason why pension funds can have a devastating power on the British bond market is that they hedge their debts with high leverage through debt-driven investment strategies. With the yield of gilt (Treasury Bond issued by the British government) continuing to soar, pension funds with trillion pounds of assets may sell gilt on a large scale to pay huge margin.</p><p>LDI + Highly Leveraged Hedges</p><p>Liability-driven investing (LDI) is the first big thunder laid by pension funds for the UK bond market.</p><p>Most pension funds in the UK have long used liability-driven investments to hedge liabilities (future payments to pension clients), a strategy through which pension funds can match their liabilities with the scheme's assets.</p><p>This can be illustrated in a simple model:</p><p>If a pension fund is required to pay a £1m pension to a client in 2046, the fund can buy gilt bonds that mature that year. If high-quality corporate bonds with higher yields are bought, the fund can use the extra money to invest in the stock market, real estate or other growth assets in addition to the bond purchase. Further,<b>Pension funds can use interest rate swaps or inflation swaps to match long-term liabilities.</b>This means that the pension fund only needs to invest the initial margin for trading, and the change direction of the Treasury Bond yield determines whether the fund pays or harvests the margin. Specifically, when the yield of Treasury Bond rises, the pension fund needs to pay a margin to the counterparty, and instead wins the margin of the counterparty. Therefore, pension funds that hedge through interest rate swaps face some leverage.</p><p>How big is this leverage?</p><p>Of the top 600 pension funds in the UK (with total assets of around £700bn), according to a 2019 survey conducted by the UK's pension regulator,<b>62% of pensions are leveraged through interest rate swaps, which account for 43% of all leveraged investments, and the maximum leverage level allowed is 1x to 7x!</b></p><p>Once yields go up, pension funds will face margin calls and be forced to offer more collateral.</p><p>Indeed, there were media reports as early as July that UK pension funds were facing margin calls. Over the past year, UK Treasury Bond yields have continued to rise, with the UK 30-year Treasury Bond yield soaring from 1.12% in October last year to 3.93% in July this year.</p><p><img src=\"https://static.tigerbbs.com/a7bfd9ed4efd73df53dfa7fe47cb6c4f\" tg-width=\"991\" tg-height=\"609\" referrerpolicy=\"no-referrer\" width=\"100%\" height=\"auto\"/></p><p>Britain's bond market may face destruction if pension funds withdraw on a large scale</p><p>The huge scale of funds is the second thunder laid by pension funds for the UK bond market.</p><p>While extremely risky, the LDI strategy helped UK pension funds expand rapidly in the days of falling gilt yields. UK assets held by UK pension funds through LDI strategies tripled to £1.5trillion in the 10 years to 2020, according to the Investment Association.</p><p>What concept is this number equivalent to?</p><p>After removing the Treasury Bond held by the Bank of England,<b>Assets held by pension funds account for 40% of the UK's institutional asset management market, two-thirds of the UK's GDP and the size of the entire gilt market.</b></p><p>It is unclear exactly how large the pension fund holds British Treasury Bond, but analysts say it is huge<b>If pension funds withdraw from the British Treasury Bond on a large scale, the British bond market may face a disaster.</b></p><p>Fuse: Britain's most aggressive tax cuts in half a century</p><p>On Friday, the UK announced its most aggressive tax cuts in half a century, which set off a sell-off wave in the UK gilt market, with the UK's 30-year Treasury Bond yield surging 120 basis points in just a few days.</p><p>Treasury Bond yields have soared, and the margin calls facing pension funds have risen.</p><p>Ben Gold, head of investments at pensions advisory firm XPS, estimates that UK pension funds have received at least £1bn of margin calls since the Government unveiled plans for tax cuts. He said about two-thirds of the 400 pension schemes his firm advises on have been affected.</p><p>In order to pay a huge deposit,<b>UK pension funds may have to dump large amounts of gilts and other liquid assets, further weighing on the UK bond market and pushing up Treasury Bond yields, which will cause the margin to be paid to snowball.</b></p><p>In this regard, Kerrin Rosenberg, chief investment officer of Cardano Investment Company, said:</p><p><b>Had the Bank of England not intervened today, gilt yields could have risen from 4.5% in the morning to 7-8%, in which case around 90% of UK pension funds would have run out of collateral and they would have been wiped out!</b>As previously analyzed by Wall Street, in order to prevent the large-scale escape of pension funds from the British Treasury Bond from bringing a fatal blow to the bond market, the Bank of England reversed its long-standing \"non-intervention\" strategy and announced unlimited purchase of British Treasury Bond.</p><p>The Bank of England issued a statement on Wednesday saying it would temporarily buy long-term UK Treasury Bond \"at any scale necessary\" to restore order to the UK bond market. Subsequently, the Bank of England said it would buy traditional British Treasury Bond with a remaining maturity of more than 20 years in the secondary market.</p><p>Affected by this, the British Treasury Bond skyrocketed, and the British 30-year Treasury Bond yield once fell by 100 basis points.</p><p></body></html></p>\n<div class=\"bt-text\">\n\n\n<p> source：<a href=\"https://wallstreetcn.com/articles/3671538\">华尔街见闻</a></p>\n\n\n</div>\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"https://static.tigerbbs.com/741a7a9152b273ef524b8d371b44958e","relate_stocks":{"EWU":"英国ETF-iShares MSCI"},"source_url":"https://wallstreetcn.com/articles/3671538","is_english":false,"share_image_url":"https://static.laohu8.com/cc96873d3d23ee6ac10685520df9c100","article_id":"1110631384","content_text":"作者：卜淑情据华尔街见闻此前提及，英国央行周三出手干预债市最重要的一个原因在于英国养老基金面临大规模追加保证金的要求。媒体援引知情人士透露，投资银行和基金经理最近几天警告英国央行，追加保证金的要求可能引发英国债市崩盘。养老基金之所以能对英国债市产生摧枯拉朽的破坏力，是因为它通过负债驱动型投资策略对其债务进行了高杠杆对冲，在金边债券（英国政府发行的国债）收益率持续飙升的情况下，为支付巨额保证金，持有万亿英镑资产的养老基金可能会大规模抛售金边债券。LDI+高杠杆对冲负债驱动型投资（LDI）是养老基金为英国债市埋下的第一颗大雷。长期以来，英国多数养老基金一直采用负债驱动型投资来对冲负债（未来支付给养老金客户的款项），通过该策略，养老基金可以将其负债与计划的资产相匹配。这可以通过一个简单的模型中说明：如果养老基金需要在2046年向客户支付100万英镑的养老金，那么基金可以购买于该年到期的金边债券。如果买到优质的收益率更高的企业债券，基金可以在购买债券之余使用额外的资金投资股市、房地产或其他成长型资产。更进一步，养老基金可以使用利率掉期或通胀掉期来匹配长期负债。这就意味着，养老基金只需要投入初始保证金进行交易，而国债收益率变动方向决定该基金是缴纳还是收获保证金。具体来看，在国债收益率上行时，养老基金需要向交易对手缴纳保证金，相反则赢得交易对手的保证金。因此，通过利率掉期进行对冲的养老基金面临一定的杠杆。这个杠杆有多大？根据英国养老金监管机构2019年进行的调查，在英国前600家养老基金（总资产约为7000亿英镑）中，有62%的养老金通过利率掉期进行杠杆投资，利率掉期投资规模占所有杠杆投资的43%，允许的最大杠杆水平为1倍至7倍！一旦收益率上行，养老基金将面临追加保证金的要求，被迫提供更多抵押品。实际上，早在7月就有媒体报道，英国养老基金面临追加保证金通知。在过去一年中，英国国债收益率持续上升，其中英国30年期国债收益率从去年10月的1.12%飙升至今年七月的3.93%。若养老基金大规模撤出，英国债市或面临灭顶之灾庞大的资金规模是养老基金为英国债市埋下的第二颗雷。虽然风险极高，LDI策略在金边债券收益率下降的日子里仍帮助英国养老基金迅速扩张。投资协会（Investment Association）的数据显示，在截至2020年的10年中，英国养老基金通过LDI策略持有的英国资产增加了两倍，达到1.5万亿英镑。这一数字相当于什么概念？除去英格兰银行持有的国债后，养老基金持有的资产占英国机构资产管理市场的40%，占英国GDP的三分之二以及整个金边债券市场的规模。目前尚不清楚养老基金到底持有多大规模的英国国债，但分析认为这一规模十分巨大，如果养老基金大规模撤出英国国债，英国债市或面临灭顶之灾。导火索：英国半世纪以来最激进的减税计划上周五，英国宣布了半个世纪以来最激进的减税计划，该计划在英国金边债券市场掀起了抛售浪潮，英国30年期国债收益率在短短几天内飙升了120个基点。国债收益率飙升，养老基金面临的追加保证金水涨船高。养老金咨询公司XPS投资主管Ben Gold估计，自英国政府公布减税计划以来，英国养老基金已收到至少10亿英镑的追加保证金通知。他表示，在其公司提供咨询服务的400个养老金计划中，大约有三分之二受到了影响。为支付巨额保证金，英国养老基金可能不得不抛售大量金边债券和其他流动资产，进一步使英国债市承压，推高国债收益率，这将导致需缴纳的保证金像滚雪球一样越来越大。对此，卡尔达诺投资公司首席投资官Kerrin Rosenberg表示：如果英国央行今天没有干预，金边债券收益率可能会从早上的4.5%上升到7-8%，在这种情况下，大约90%的英国养老基金将用完抵押品，他们会被消灭！正如华尔街见闻此前分析，为防止养老基金大规模出逃英国国债给债市带来致命打击，英国央行扭转了长期以来的“不干预”策略，并宣布无限量购买英国国债。英国央行周三发布声明称，将“以任何必要的规模”临时购买英国长期国债，以恢复英国债券市场秩序。随后，英国央行表示将在二级市场购买剩余期限超过20年的传统英国国债。受此影响，英国国债暴涨，英国30年期国债收益率一度下行100个基点。","news_type":1,"symbols_score_info":{"EWU":0.9}},"isVote":1,"tweetType":1,"viewCount":1267,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0},{"id":668841219,"gmtCreate":1664549891736,"gmtModify":1676537476026,"author":{"id":"3560270530016088","authorId":"3560270530016088","name":"DKTED","avatar":"https://static.laohu8.com/default-avatar.jpg","crmLevel":1,"crmLevelSwitch":0,"followedFlag":false,"authorIdStr":"3560270530016088","idStr":"3560270530016088"},"themes":[],"htmlText":"1","listText":"1","text":"1","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":0,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/668841219","repostId":"1146232184","repostType":4,"isVote":1,"tweetType":1,"viewCount":691,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0},{"id":663050865,"gmtCreate":1662698970459,"gmtModify":1676537122763,"author":{"id":"3560270530016088","authorId":"3560270530016088","name":"DKTED","avatar":"https://static.laohu8.com/default-avatar.jpg","crmLevel":1,"crmLevelSwitch":0,"followedFlag":false,"authorIdStr":"3560270530016088","idStr":"3560270530016088"},"themes":[],"htmlText":"1","listText":"1","text":"1","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":0,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/663050865","repostId":"1159026938","repostType":4,"repost":{"id":"1159026938","kind":"news","pubTimestamp":1662693287,"share":"https://ttm.financial/m/news/1159026938?lang=en_US&edition=fundamental","pubTime":"2022-09-09 11:14","market":"us","language":"zh","title":"Howard Marks Latest Memo: The Illusion of Cognition","url":"https://stock-news.laohu8.com/highlight/detail?id=1159026938","media":"橡树资本Oaktree Capital","summary":"世界上有两类预言家：一类对未来并无所知，而另一类不知道自己并无所知。","content":"<p><html><head></head><body>Writing the first memo since February 1993, \"The Value of Forecasting, Where the Rain Comes from\" (The Value of Projections, or Where did All This Rain Come From) In the beginning, I kept saying that I ignored \"forecasting\".</p><p>In the years since, I have explained at length why I am not interested in forecasting — and some of the quotes I really like in the following chapters echo my disdain for forecasting — but I have never written another memo specifically to explain why it is so difficult to make beneficial macro forecasts. Hence the memo.</p><p><b>Introduce thoughtful things</b></p><p>There are two kinds of seers in the world: one who knows nothing about the future, and the other who knows nothing about themselves. — Shortly after John Kenneth Galbraith finalized his memo, \"I Beg to Differ,\" I attended a luncheon with some experienced investors and those outside the investment circle. This is not a social event, but an opportunity for those present to exchange views on the investment environment.</p><p>During the period, the host asked a series of questions: How do you expect inflation to develop? Will there be a recession, and if so, how bad will it be? How will the Russia-Ukraine conflict end? What are the likely impacts of the 2022 and 2024 U.S. elections? I've heard a variety of points of view on this.</p><p>Readers who have tracked my memo for a long time should be able to imagine my thoughts at the time: \"No one in this room is an expert in foreign affairs or politics. No one present has particularly in-depth insight into these topics, and certainly no more than the average person reading this morning's news.\" The ideas conveyed, even on economic issues, do not seem more convincing than others, and I absolutely believe that no one can improve investment outcomes. That's the key.</p><p>It was that luncheon that got me thinking about writing another memo about the unhelpful macro outlook. Soon after, I found some extra material — a book, an article from<i>Bloomberg View (Bloomberg Opinion)</i>This material supports my argument (and perhaps my \"confirmation bias\"-the tendency of people to accept and believe information and arguments that prove their previous opinions). Together, that luncheon and the material inspired the theme of the memo:<b>Many reasons for predicting little benefit.</b></p><p>In order to obtain something useful — whether in manufacturing, academia, or even the arts — there must be a reliable<b>Process</b>Which enables the desired<b>Inputs</b>Convert to desired<b>Output</b>。 In short, the problem is that I don't think there is a process that consistently translates a large number of variables (inputs) related to the economy and financial markets into useful macro forecasts (outputs).</p><p><b>Models</b></p><p>The greatest enemy of cognition is not ignorance, but the delusion of cognition. — Daniel Burstin About my First decade at First National City Bank, there was a word that was hot at the time but has not been heard for a long time now: econometrics. Specifically, it refers to a practice of finding correlations in economic data to produce effective forecasts. Or in short, econometrics studies how to build mathematical models of the economy. In the 1970s, econometricians were hot, but I don't think they are. I think that means their models are not working.</p><p><b>Whether the model is complex and sophisticated or scribbled simple, mathematically based or intuitive, forecasters have no choice but to make judgments based on the model.</b>Models are by definition made up of assumptions: \"If A occurs, then B occurs.\" In other words, the model states relationships and responses. But if we are willing to adopt the output of the model, we must be convinced that the model is reliable. But when I thought of modeling the economy, my first thought was how complicated it would be.</p><p>For example, the United States has a population of approximately 330 million people. Except for the particularly young and some particularly old, the rest are participants in the economy. So there are hundreds of millions of consumers, and millions of workers, producers, and middlemen (many meeting multiple classifications). To predict the path of the economy, it is necessary to predict the behavior of these people — if not every participant, at least the total group size.</p><p>A real simulation of the U.S. economy must deal with billions of interactions or nodes, including with suppliers, customers, and other market participants across the globe. Is it possible to do this? For example, is it possible to predict the behavior of consumers in the following situations: (i) if they earn an extra dollar of income (what is the \"marginal propensity to spend\"?); (ii) if energy prices rise, squeezing other categories in household budgets; (iii) If the price of one commodity increases relative to other commodities (will there be a \"substitution effect\"?); And (iv) if the geopolitical arena is stirred up by events on other continents?</p><p>Clearly, this level of complexity requires frequent use of simplified assumptions. For example, it would be easier to model if it could be assumed that consumers would not buy B in place of A in the event that B is not better or cheaper (or both). It would also help if the producer would not price X below Y if it was assumed that the cost of producing X was no less than Y.</p><p>But despite the higher price of B (or even because of it), consumers are still attracted to the brand effect of B. What happens? What would happen if X was produced and developed by entrepreneurs willing to use losses for several years to gain market share? Is it possible for models to predict the decision of consumers willing to spend more and entrepreneurs willing to make less (or even lose)?</p><p>Furthermore, the model must predict the behavior of each group of participants in the economy in various environments. But the vagaries are multifaceted. For example, a consumer may behave in one way at one moment and in a different way at another similar moment. Given the large number of variables involved, it seems unlikely that two \"similar\" moments will occur in exactly the same way, and it is unlikely that we will see economic participants exhibiting the same behavior.</p><p>Beyond that, participants' behavior will be influenced by their psychology (or should I say their emotions?), and their psychology may be influenced by qualitative, non-economic developments. How can these be modeled?</p><p>How can an economic model be comprehensive enough to deal with situations that have never been encountered before, or that have not been seen in modern times (i.e., in comparable situations)? This is yet another example of how models cannot simply replicate something as complex as the economy.</p><p>A prime example of this, of course, is COVID. It has shut down most of the world's economies, upended consumer behavior, and inspired massive government money-out bailouts. Which aspect of the existing model can predict the impact of the pandemic? Yes, the world experienced a pandemic in 1918, but the situation was so different (there were no iPhones, Zoom calls, etc) that the economic state of affairs of that period had little comparison to 2020.</p><p>In addition to factors such as complexity and psychological fluctuations and dynamic processes that are difficult to capture, consider that attempting to make predictions about things that cannot be expected to remain the same is inherently limiting. Shortly after starting writing this memo, I received Morgan Housel's always wonderful weekly. One of the articles describes a lot of observations in other areas related to our economy and investment.</p><p>The following two are borrowed from the field of statistics, and I think they are relevant to the discussion of economic models and forecasts (\"Small Ways the World Works (Little Ways the World Works) \", Morgan Housel,<i>Collaborative Fund</i>July 20, 2022):</p><p>Stationarity: This is the assumption that history can be used as a guide for future statistics based on the fact that the main factors affecting the system do not change over time. If you want to know how high a levee to build, look at flood data over the past 100 years and assume that the next 100 years will be the same. Stationarity is a fantastic, science-based concept that works until it fails. It is a major driver of important events in economy and politics. [But in our world,] \"things that have never happened before are happening all the time,\" says Stanford professor Scott Sagan. Cromwell's Rule: Never say something won't happen.... Even if there is only a one in a billion chance that something will come true and you will interact with billions of things in your lifetime, therefore you will almost certainly experience some shocking contingency and should always be open to the possibility of the unthinkable becoming a reality. Stationarity may be a reasonable assumption in the field of physical sciences. For example, because of the law of universal gravity, objects can always fall at the same acceleration under established atmospheric conditions. It always turned out that way, and it always will. But few processes in our field are smooth, especially given psychological, emotional, and human behavior, and they change over time.</p><p>Take, for example, the relationship between unemployment and inflation. For the past 60 years or so, economists have relied on the Phillips curve, which argues that wage inflation will rise as unemployment falls because when there are fewer unemployed workers, employees gain bargaining power and can successfully negotiate higher wages. For decades, the unemployment rate of 5.5 percent was also considered to indicate \"full employment\".</p><p>But the unemployment rate fell below 5.5% in March 2015 (and reached a 50-year low of 3.5% in September 2019), but inflation (wages or otherwise) did not rise significantly until 2021. The important relationship described by the Phillips curve has been applied to various economic models built over the decades, but it does not seem to be applicable for much of the past decade.</p><p>Cromwell's Law is just as important. Unlike the physical sciences, there are very few things that absolutely must or must not happen in markets and economics. Therefore, in<i>\"Cycle\"</i>（<i>Mastering the Market Cycle</i>In this book, I list seven terms that investors should remove from their glossary: \"never,\"\"always,\"\"never,\"\"can't,\"\"won't,\"\"will,\" and \"must.\" But if these words really have to be discarded, then the idea of building models that reliably predict the macro future must also be discarded. In other words, almost nothing is immutable in our field.</p><p>The unpredictability of behavior is my favorite topic. The famous physicist Richard Feynman once said, \"Imagine how difficult physics would be if electrons felt.” The rules of physics are reliable precisely because electrons always do what they are supposed to do. They never forget to perform their duties. They never resist. They never strike. They never innovate. They never behave in the opposite way.</p><p><b>But none of this applies to the participants in the economy, and it is because it does not apply that the participants' behavior is unpredictable. How to model the operation of an economy if actors' behavior is unpredictable?</b></p><p><b>We are talking about the future, and there is no one way to predict the future without making assumptions.</b>Small mistakes in assumptions about the economic environment and subtle changes in participants' behavior can both cause serious problems. As mathematician and meteorologist Edward Lorenz famously said, \"A Brazilian butterfly flapping its wings could cause a tornado in Texas.\" (Historian Niall Ferguson mentions this in an article discussed below.)</p><p>In summary, can we consider the economic model to be reliable? Can models replicate reality? Can it describe the behavior of millions of participants and their interactions? Is the process trying to model reliable? Can these processes be reduced to mathematics? Can mathematics capture the qualitative nuances of people and their behavior? Can models predict changes in consumer preferences, changes in corporate behavior, and how participants respond to innovation? In other words, can we trust the output of the model?</p><p>Clearly, economic relations are not static, and the economy is not governed by schematics (schematics that the model is trying to simulate). So the bottom line for me is that the model's output points in the right direction most of the time without violating the assumptions. But it can't always be accurate, especially at critical moments like inflection points…and that's when accurate predictions are most valuable.</p><p><b>Inputs</b></p><p>One thing that cannot be ignored is the fact that all your knowledge is about the past and all your decisions are about the future. — Ian H. Wilson (Former General Electric Executive) Having considered the incredible complexity of the economy and the need to make simplified assumptions (which would reduce the accuracy of any economic model), let's now consider the input required for a model — the raw materials for manufacturing predictions. Are estimated inputs valid? Can we understand them in depth enough to make meaningful predictions?<b>Or does it simply remind us of the ultimate truth about models: \"The input is garbage, the output is still garbage\"?</b>Obviously, no prediction will have a better quality than the quality of the input on which it is based.</p><p>Here's what Neil Ferguson said on July 17 at<i>Bloomberg View (Bloomberg Opinion)</i>Content written:</p><p>Consider what we really want to ask when we ask the question, \"Has inflation peaked?\" We're not just asking about the supply and demand of 94,000 different goods, manufactured goods and services. We are also concerned about the future path of interest rates set by the Federal Reserve, which, aside from the much-touted \"forward guidance\", is far from clear where it will go. What we are asking is how long the strength of the dollar will continue, as it is currently depressing the price of U.S. imports. But there are more questions to be answered. At the same time, the above questions are also indirectly asking how long the Russia-Ukraine conflict will continue, because the chaos caused by the Russia-Ukraine conflict has significantly increased the inflation of energy and food prices since February. We are asking whether oil-producing countries such as Saudi Arabia will respond to Western governments' requests to increase crude oil production... We should probably also ask ourselves what impact the latest coronavirus, Omicron BA.5, will have on the Western labor market. British data show that BA.5 is 35% more contagious than its predecessor, BA.2, which in turn is more than 20% more contagious than the original Omicron. If you want to add all these variables to your model, then I wish you good luck. In fact, the future path of inflation, like the future direction of the Russia-Ukraine conflict and the transmission path of the COVID-19 epidemic, is uncertain. I found Ferguson's article so relevant to the topic of this memo that I am attaching a link to the article here. The article makes a lot of important points, although I beg to differ in one respect. Ferguson mentioned above, \"In fact, the future path of inflation, like the future direction of the Russia-Ukraine conflict and the spread path of the new coronavirus epidemic, is as uncertain.\"</p><p>I think accurate predictions of inflation are \"less likely\" to be achieved than predictions of the other two problems (if they are predictable), because accurate predictions of inflation require correct predictions of both events and a thousand other influential factors. How can anyone get all these things right?</p><p>Here I briefly introduce the forecasting process mentioned in The Value of Forecasting:</p><p>I guess for most fund managers, the process looks like this: \"I predict that the economy will do A. If A happens, interest rates should present B. If interest rates are B, the stock market should present C. In this environment, the best performing sector should be D, and stock E should rise the most.\" Then build the portfolio accordingly, with a view to achieving the best performance in this scenario. But anyway, how likely is E? Keep in mind that E is conditional on A, B, C, and D. In the field of forecasting, a two-thirds accuracy rate would be a great achievement. But if there is a 67 percent chance that each of the five predictions is correct, the result is a 13 percent chance that all five predictions are correct and the stock will perform as expected.<b>Predicting event E based on assumptions about A, B, C, and D is what I call single-scenario prediction.</b>In other words, if the result of the hypothesis regarding A, B, C, or D turns out to be wrong, the prediction result of E is unlikely to be achieved. Only if all potential predictions are correct can E get consistent results as predicted, but this is extremely rare. No one can make an informed investment without considering (i) the other possible outcomes of each element, (ii) the possibility of other scenarios emerging, (iii) what are the prerequisites to make one of the hypotheses a reality, and (iv) what are the effects on E.</p><p>Ferguson's article raises an interesting question about economic modeling: What assumptions should we make about what macro environment economic participants are in? The<b>This question just shows an endless loop: in order to predict the overall performance of the economy, we need to make assumptions about consumer behavior and so on. But to predict consumer behavior, don't we need to make assumptions about the overall economic environment?</b></p><p>In my first memo on the pandemic, Nobody Knows II (March 2020), I mentioned that in discussing the coronavirus, Harvard epidemiologist Marc Lipsitch once said: (i) facts; (ii) well-founded inferences derived from analogies with other viruses, and (iii) opinions or speculations. This is our standard practice when dealing with uncertain events. In economic or market forecasts, we have a lot of history and many similar past events to infer (but none of COVID). But even if these things are used as inputs by a well-constructed prediction model, they are still unlikely to predict the future. They can be useful fodder or they can be garbage.</p><p>To illustrate this, people often ask me which cycle I have experienced in the past is most similar to the current one. My answer is that current developments bear short-lived similarities to some past cycles, but no absolute similarities.<b>In each case, the differences are vast and outweigh the similarities.</b></p><p><b>Even if we can find an identical previous period, to what extent should we rely on this single sample? I guess the answer is not much. Investors rely on historical references (and the forecasts they make based on them) because they fear they will act blindly without them. But that does not mean that the information is reliable.</b></p><p><b>Unpredictable effects</b></p><p>Prediction creates the mirage that the future is knowable. — Peter Bernstein<b>We cannot consider the plausibility of predictions without first determining whether our world is orderly or random.</b>In short, is it completely predictable, completely unpredictable, or somewhere in between? For me, the conclusion is somewhere in between, but more inclined to be unpredictable, to the point where most predictions don't help. Since our world is predictable at some times and unpredictable at others, what's the use of prediction if we can't distinguish when it is predictable and when it is unpredictable?</p><p>I learned a new word from reading Ferguson's article: \"deterministic\". The Oxford Dictionary defines it as \"causally determined by a previous event or natural law\". The world is a lot simpler when we handle things by the rules...just like Feynman's electrons. But it is clear that economies and markets are not governed by the laws of nature – thanks to human involvement – and that events before it may be \"foreshadowing\" or \"tending to repeat\", but events rarely happen twice in the same way. Therefore, I believe that the processes that make up the operation of economies and markets are not deterministic, which means that they are unpredictable.</p><p>Furthermore, the input is clearly unreliable. Many are random, such as weather, earthquakes, accidents and deaths. Others involve political and geopolitical issues – some we know, some that have not yet surfaced.</p><p>In the Bloomberg Opinion article, Ferguson referred to the British author G.K. Chesterton. This reminds me of my quote from Chesterton in Risk Revisited Again (June 2015):</p><p>The real problem with our world today is not that the world is irrational, nor that it is a rational world. The most common question is: The world is almost rational, but not quite. Life is not a contradiction, but it is a trap for logicians.<b>It looks slightly more precise and regular than it really is; Its precision is obvious, but its imprecise side is hidden; Its wildness is also lurking.</b>(Bold is added by the author) Returning to the luncheon introduced on the first page, the host's opening remarks are roughly as follows: \"In recent years, we have experienced events such as the COVID-19 pandemic, the astonishingly successful Federal Reserve bailout policy, and the Russia-Ukraine conflict. This is a very challenging environment because all of this comes out of the blue.\"</p><p>For him, I suppose, this means that attendees should let themselves off the trouble of inaccurate predictions for 2020-2022, continue to predict the future and bet on their own judgment. But my reaction was completely different: \"There are many events that affect the current environment.<b>And isn't the fact that no one can predict any of them enough to convince those present that they should give up their predictions? \"</b></p><p>For another example, let's think back to the fall of 2016. There are two things that almost everyone is convinced of: (i) Hillary Clinton will be elected president; (2) If for some reason Donald Trump is elected, the market will collapse. Still, it turned out that Trump won and the market soared.</p><p>The past six years have had a profound impact on the economy and markets,<b>I believe that any prediction of the 2016 election in the conventional view at the time would not have been correct.</b>Isn't that enough to convince people that (i) we don't know what will happen in the future, and (ii) we can't understand how the market will react to what happens?</p><p><b>Can forecasting bring excess?</b></p><p>It is not ignorance that keeps us in trouble, but fallacious assertions that seem correct. — Mark Twain As I mentioned in my recent memo, \"Thinking About Macro,\" in the 1970s, we used to describe economists as \"investment directors who never enter the market.\" In other words, economists make many predictions; The actual situation will prove them right or wrong; Then they proceed to make new predictions; But they do not track the frequency of the prediction (or, they do not publish statistics).</p><p>Can you imagine hiring a fund manager without reference to your track record (or if you were a fund manager, could you imagine being hired in this scenario)? But economists and strategists don't lose their jobs by not releasing statistics, perhaps because there are always clients willing to pay for their forecasts.</p><p>Are you a consumer of these predicted outcomes? Are forecasters and economists employees of your company? Or do you subscribe to their publications and invite them to give briefings, like my previous employer? If so, do you know how often everyone predicts correctly? Have you found a way to rigorously determine which of these predictions can be relied on and which to ignore? Is there a way to quantify how these projections contribute to your return on investment?</p><p>I ask this litany of questions because I haven't seen or heard of any research in this area. It is unimaginable that information on whether macro forecasts will yield excess returns is scarce globally, especially disproportionately compared to the number of people who need such information.</p><p>Despite the lack of evidence to prove its worth, macro forecasts continue. Many forecasters are part of, or are providing advice and forecasts to, equity fund management teams.</p><p>One thing we can be sure of is that actively managed equity funds have been losing market share for decades due to the poor performance of actively managed funds, which now account for less than half of the U.S. equity mutual fund market, being replaced by index funds and other passive investment vehicles. Is it the reason that macro forecasting is inherently unhelpful for investment?</p><p>As far as I know, the only thing to find quantitative information about this issue is the performance of so-called macro hedge funds. The Hedge Fund Research Organization (HFR) has released the Hedge Fund Weighted Composite Index as well as some sub-strategy indices. Here's a look at the long-term performance of the Hedge Fund Weighted Composite Index, the Macro Hedge Sub-Strategy Index, and the S&P 500.<img src=\"https://static.tigerbbs.com/79717be010acf96241bf0336e5ac3381\" tg-width=\"963\" tg-height=\"272\" referrerpolicy=\"no-referrer\" width=\"100%\" height=\"auto\"/>* Performance as of July 31, 2022. The hedge fund index shown is a weighted composite index of each fund.</p><p>In the table above, according to HFR data, the average performance of hedge funds during the study period was much lower than that of the S&P 500, while the average performance of macro hedge sub-strategy funds was much worse (especially between 2012 and 2017). Given that investors continue to entrust roughly $4.5 trillion to hedge fund managers, these funds must provide some benefit beyond returns, but it's unclear what that will be. This seems to be especially true for macro hedge funds.</p><p>To confirm my view of forecasting, I will next give a rare example of self-assessment: a seven-page feature in the July 24 New York Times \"Sunday Opinion\" column entitled \"I Were Wrong.\" In the article, eight New York Times \"Opinion\" columnists opened up about their misguided predictions and biased advice.</p><p>Most relevant here is a confession written by Paul Krugman entitled \"I Was Wrong About Inflation\". I excerpted and stringed some of these:</p><p>In early 2021, there was a heated debate among economists about the possible consequences of the \"American Rescue Plan\"…I was on the [side of being less worried about the impact of inflation]. Of course, it turned out to be a very bad decision… … …History couldn't have enabled us to anticipate such overheated inflation. So something went wrong with my model…one possible reason is that history is misleading…and also, the perturbations created to adapt to the pandemic and its aftermath may still be playing a big role. Of course, the Russia-Ukraine conflict and the epidemic prevention and control measures in major Chinese cities have undoubtedly pushed this level of interference to a whole new level…In any case, the whole thing has become a lesson of humility. Incredibly, the standard economic model has been working fairly well after the 2008 financial crisis, and I thought at the time there was no problem applying the same model in 2021. In retrospect, I should have realized at the time that this extrapolation was inherently risky in the new world trends that emerged post-Covid. (Bold added by the author) I admire Krugman for showing such astonishing candor (although I have to say that I don't recall many market forecasts between 2009 and 2010 that were optimistic enough to portray the reality of the following decade).<b>Krugman's explanation of his mistakes is good in itself, but I don't see him mention abandoning modeling, inference, or forecasting in the future.</b></p><p>This humility may even permeate the Federal Reserve, one of the world's largest economic forecasters, with more than 400 PhDs in economics. Here's what economist Gary Shilling wrote in Bloomberg Opinion on August 22:</p><p>The Fed's forward guidance became a disaster, leaving its own credibility challenged. Chairman Jerome Powell seems to take the same view, and the outside world should stop speculating about what the Fed thinks about interest rates, economic growth and inflation at different points in the future...<b>The fundamental problem with forward guidance is that it relies on data, which itself comes from the Fed's previous track record of poor forecasts.</b>The Federal Reserve has been overly optimistic about the economic recovery after the Great Recession of 2007-2009. In September 2014, policymakers predicted real GDP growth of 3.40 percent in 2015, but by September 2015 they were forced to keep cutting their estimates to 2.10 percent.<b>Federal Funds rate is not a market-determined interest rate, but is set and controlled by the Federal Reserve, and no one challenges the authority of the Federal Reserve. Furthermore, Federal Open Market Committee (FOMC) members are notoriously bad at predicting what actions they themselves will take...</b>In 2015, their average forecast for the federal funds rate was 0.90% in 2016 and 3.30% in 2019. The actual numbers are 0.38% and 2.38% respectively…To be sure, many of the ongoing events are creating uncertainty in the markets, but forward guidance from the Fed has been highly sought after and important. Recall that earlier this year, the Fed also viewed inflation from the pandemic and friction to restart the economy after supply chain disruptions as temporary. Only later did the Fed discover things weren't good and reversed course, raising interest rates and signaling further sharp rate hike. The Fed's erroneous forecasts led to wrong forward guidance, adding to financial market volatility. (Bold added by the author) One last point on this question, that is, where are the people who make a name for themselves (and become rich) by profiting from the macro view? Of course, I can't know everyone in the investment community, but among those I know or know, I think there are only a few very successful \"macro investors\". When there are few instances of something, as my mother once said, \"the exception just confirms the law\".</p><p><b>The rule in this example is that macro forecasts rarely lead to outstanding performance. To me, the extraordinary success story just proves that this statement is universal truth.</b></p><p><b>Forecasted needs of practitioners</b></p><p>Forecasting reveals the forecaster more than it does the future. -Warren Buffett How many people can make macro forecasts that are valuable most of the time? Not much, I think. And how many investment managers, economists and forecasters have tried? Thousands to say the least. Which raises an interesting question: why predict? If macro forecasting won't help investment succeed over time, why do so many practitioners in the investment management industry believe in forecasting and rush to forecast results? I think the typical reasons for this might be:</p><p><ul><li>It's part of the job.</p><p></li><li>Investors always do this.</p><p></li><li>Everyone I know does this, especially my competitors.</p><p></li><li>I've been doing this all the time – I can't leave it at that now.</p><p></li><li>If I don't, I won't be able to attract customers.</p><p></li><li>Since investing involves deploying capital in order to benefit from future events, how can a good job be expected without a view of those events? We need predictions, even if they aren't perfect.</p><p></li></ul>This summer, my son Andrew recommended a very interesting book: Mistakes Made (But Not Mine): Why We Make Excuses for Stupid Beliefs, Bad Decisions, and Hurting Behavior (Mistakes Were Made (but Not by Me): Why We Justify Foolish Beliefs, Bad Decisions, and Hurtful Acts), It was written by psychologists Carol Tavris and Elliot Aronson.<b>The theme of the book is self-justification.</b></p><p><b>The authors explain that \"cognitive dissonance\" occurs when people are faced with new evidence to question their previous positions, and when this happens, the subconscious mind causes them to struggle hard to prove and maintain their previous positions.</b>Here are some curated snippets:</p><p>If you hold a set of beliefs that guide your practice, and you learn that some of them are incorrect, you must either admit that you were wrong and change your approach, or reject new evidence. Most people, when confronted directly with evidence that they have done wrong, do not change their views or plan of action, but argue it more stubbornly. Once we have identified a belief and proved its wisdom, it is obviously hard work to change our mind. It is much easier to put new evidence into an existing framework for mental argumentation for acceptance than to change the framework. The mechanisms that people typically employ in responding to evidence that calls their beliefs into question include these (paraphrasing the author):</p><p><ul><li>Unwillingness to listen to discordant messages;</p><p></li><li>Selectively remember parts of their lives, focusing on those that support their views; as well as</p><p></li><li>Acting under a cognitive bias that allows people to only see what they want to see and seek some kind of confirmation for what they already believe.</p><p></li></ul>These are, I believe, factors that cause people to consistently make predictions and rely on them. So in this case, what kind of manifestation will there be?</p><p><ul><li>Think of macro forecasts as an integral part of investing;</p><p></li><li>A passion for recalling correct predictions, especially those that are bold, non-market consensus;</p><p></li><li>Overestimate the accuracy of forecasts;</p><p></li><li>Forgetting or downplaying false predictions;</p><p></li><li>Failure to keep records of the accuracy of forecasts or failure to calculate the average success rate;</p><p></li><li>Value rewarding good returns for accurate forecasts;</p><p></li><li>Emphasizing that \"everyone does this\"; as well as</p><p></li><li>Perhaps most importantly, blame unsuccessful predictions on being blinded by random or exogenous events. (But, as I said before, that's the point: why make predictions if they become inaccurate so easily?)</p><p></li></ul>Most people — even honest people with good hearts — take positions or actions that serve their own interests, sometimes at the expense of others or objective truth. Instead of perceiving it themselves, they think that what they do is the right thing; They also seek a lot of valid reasons. As Charlie Munger often quotes Demosthenes, \"Nothing is easier than deceiving oneself. For man always believes what he wants.\"</p><p>I don't think predictors are liars or charlatans. Most of them are intelligent intellectuals who think they are doing something useful.<b>But self-interest causes them to behave in a certain way, and self-defense causes them to stick to their guns in the face of evidence to the contrary.</b>As Morgan Housel put it in a recent current affairs briefing:</p><p>The inability to predict the past has no effect on our intention to predict the future. Certainty is so precious that we never give up on the pursuit of it, and if people are honest about how unpredictable the future can be, most people can't get out of bed early in the morning. (From \"Big Faith,\" Collaborative Fund, August 24, 2022) On my birthday a few years ago, Richard Masson, co-founder of Oak, gave me a fun gift that matched his style. The gift that time was<i>The New York Times</i>A bound copy of. I've been hoping for the opportunity to write about my favorite subheadline for the October 30, 1929 issue, where the Dow Jones Industrial Average fell almost 23% in just two days.</p><p><b>The headline read, \"Bankers Optimistic.\"</b>(However, in the following three years, the Dow fell about 85%). Most bankers and fund managers seem inherently optimistic about the future. Other than that, it is in their best interest as it helps them do more business. But their optimism certainly contributes to their predictive views and the resulting behavior.</p><p><b>Can or can't?</b></p><p>\"I never think about the future — because it is coming.\" — Albert Einstein considers the following aspects of macro forecasting:</p><p><ul><li>The number of assumptions/inputs required,</p><p></li><li>Number of processes/relationships to be included,</p><p></li><li>The inherent unreliability and instability of these processes, and</p><p></li><li>The role of randomness and the possibility of accidents.</p><p></li></ul>The most important thing for me is that forecasts cannot always be correct to the point of value. I've mentioned this many times, but for the sake of completeness, I'll reiterate my views on the utility (or rather, futility) of macro forecasting:</p><p><ul><li>Most forecasts consist of extrapolations about past performance.</p><p></li><li>Since macro developments typically do not deviate from previous trends, inference is usually successful.</p><p></li><li>On this basis, most of the predictions are correct. However, because inferences are usually anticipated by the price of the security, those who base their expectations on inferences do not enjoy excess benefits when the inferences are established.</p><p></li><li>Occasionally, economic behavior does materially deviate from past patterns. Since this deviation is unexpected to most investors, its appearance affects the market, meaning that an accurate forecast of the deviation will lead to lucrative profits.</p><p></li><li>However, because the economy does not often deviate from past performance, few deviations can be accurately predicted, and most deviation forecasts prove to be wrong after the fact.</p><p></li><li>So we have (i) inferential forecasts, most of which are correct but do not generate excess benefits, and (ii) potentially profitable biased forecasts that will rarely be correct and therefore generally do not generate excess benefits either.</p><p></li><li>Argued: Most forecasts do not increase returns.</p><p></li></ul>During the luncheon mentioned at the beginning of this memo, people were asked about expectations for things like Fed policy and how that affected their investment stance. One person replied, \"I think the Fed will remain highly concerned about inflation, so there will be a big rate hike, which will lead to a recession. So I choose to hedge.\" Another said, \"I expect inflation to slow in the fourth quarter, while the Fed will turn dovish in January next year. Start cutting interest rates and stimulate the economy. I'm very bullish on 2023.\"</p><p>We hear it all the time.<b>But it is important to recognize that these people are using the one-factor model:</b>The speaker's prediction is based on a single variable. Speaking of simplifying assumptions: these forecasters implicitly believe that everything is the same except for Fed policy. When three-dimensional chess is needed, they are still playing flat checkers.</p><p>Leaving aside the impossibility of predicting the Fed's behavior, the influence of inflation on that behavior, and the market's response to inflation, what about other important considerations? If there are a thousand things that play a role in determining the future direction of the economy and markets, what are the other 999 things? What about the impact of wage negotiations, midterm elections, Russia-Ukraine conflict and oil prices?</p><p>The truth is that people can only remember very limited things in their minds at any one time.<b>It is difficult to take a large number of factors into account, and it is even harder to understand how a large number of things will interact with each other (correlation is always the real thinking puzzle).</b></p><p>Even if you somehow manage to get the economic forecast right, that's only half the battle. You still need to predict how economic activity will translate into market outcomes. This requires an entirely different prediction and also involves a myriad of variables, many of which relate to psychological factors and are therefore almost unknowable.</p><p>According to student Warren Buffett, Ben Graham once said, \"In the short term, the market is a voting machine, but in the long term, it is a weighing machine.\" How do you predict investors' short-term choices? Some economic forecasters have concluded that the actions announced by the Federal Reserve and Treasury Department in March 2020 will rescue the U.S. economy and help the economic recovery. But I don't know anyone who predicted a hot bull market before the recovery began.</p><p>As I mentioned earlier, in 2016 Buffett shared with me his thoughts on macro forecasts. \"For a piece of information to be useful, it must meet two criteria: first, it must<b>Important</b>And secondly must<b>It can be seen</b>。\"</p><p><ul><li><b>Of course, the macro outlook matters.</b>Today, investors seem to have caught on to what every forecaster is saying, macro events, and signals from the Fed's intermittent tightening action. Unlike my early days in the industry, it seems that macro factors are everything today, and corporate development is less of a concern.</p><p></li><li><b>But I strongly agree with Buffett that the macro future is unknowable,</b>Or at least almost no one consistently knows more than the vast array of investors, which is the key to trying to gain cognitive advantage and make excellent investment decisions.</p><p></li></ul>Obviously, Buffett, whose name tops the list of successful investors, avoids macro forecasts and focuses more on the \"micro\" areas than others: companies, industries and securities to succeed.</p><p>I introduced the concepts of the \"cognitive” and\" agnostic” schools in a 2001 memo entitled \"What's It All About, Alpha?” and elaborated on Them in 2004 in Us and Them. As a close to the current memo, I'll insert some of what I've written about the two genres in the latter:</p><p>Most of the investors I've met over the years belong to the \"cognitive\" school. This was the case when I analyzed stocks from 1968 to 1978, and even when I turned to non-mainstream investing but still worked in equity-centric investment managers from 1978 to 1995.</p><p>It is easy to identify members of the \"cognitive\" school:</p><p><ul><li>They believe that understanding the future direction of the economy, interest rates, markets, and mainstream stocks that are receiving widespread attention, is essential to investment success.</p><p></li><li>They are confident that they can achieve this.</p><p></li><li>They knew they could do it.</p><p></li><li>They know that many people are trying to do this too, but they think that either (i) everyone can succeed at the same time, or (ii) only a few people can do it, but they are one of them.</p><p></li><li>They are willing to invest based on their view of the future.</p><p></li><li>They are also happy to share their opinions with others, although correct predictions should be worth a thousand dollars and no one will give them away for free.</p><p></li><li>They rarely look back and seriously review their achievements as forecasters.</p><p></li></ul>\"Confidence\" is the key word to describe members of the genre. On the other hand, for the \"agnostic\" genre, the term, especially when looking at the macro future, should be \"cautious\".<b>Its believers usually believe that the future cannot be predicted; Nor do you have to anticipate the future; The right goal should be to do your best to invest on the basis of acknowledging that you do not have this perception.</b></p><p>As a member of the \"cognitive\" school, you can have an opinion on the future (and perhaps someone else hereby takes notes). You may be sought after and seen as the ideal dinner guest…especially when the stock market is rising.</p><p>If you add the \"agnostic\" genre, the results are more complicated. You quickly get tired of expressing \"agnostic\" to friends and strangers. It won't be long before even relatives stop asking you what you think about the market movement. You'll never enjoy the one-in-a-thousand surprise moment when a prediction comes true, or the joy of the Wall Street Journal publishing your photo.</p><p>On the other hand, you are also protected from forecasting errors and from losses that result from investing based on overconfidence in the future.<b>But what do you think it will feel like when a prospect asks you about your investment prospects and you have to say \"I don't know\"?</b></p><p>For me, the bottom-line judgment of which genre is the best comes from the quote of the late Stanford behavioral scientist Amos Tversky:<b>\"It's scary to realize that you may not know something, but even more scary is to realize that, in general, the world is run by people who firmly believe they know exactly what is happening.\"</b></p><p>In the investment management business, it is certainly standard practice to put forward macro forecasts, share them upon request, and use them as a basis for entrusted investments for clients. It also seems to be customary for fund managers to trust forecasts, especially their own. As mentioned above, not doing so seems out of place. But are their beliefs matter of fact? I'd love to hear everyone's opinion.</p><p>Years ago, a well-respected sell-side economist (an old acquaintance of mine from my time at Citi) called me: \"You have changed my life,\" he said. \"I've long since stopped making predictions. Instead, I'm just telling people what happened today and the impact I think it might have in the future. Life has been a better place ever since.\" Can I help you reach the same state of happiness?</p><p>This article is written by: Howard Marks</p><p></body></html></p>","source":"lsy1658993322555","collect":0,"html":"<!DOCTYPE html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width,initial-scale=1.0,minimum-scale=1.0,maximum-scale=1.0,user-scalable=no\"/>\n<meta name=\"format-detection\" content=\"telephone=no,email=no,address=no\" />\n<title>Howard Marks Latest Memo: The Illusion of Cognition</title>\n<style type=\"text/css\">\na,abbr,acronym,address,applet,article,aside,audio,b,big,blockquote,body,canvas,caption,center,cite,code,dd,del,details,dfn,div,dl,dt,\nem,embed,fieldset,figcaption,figure,footer,form,h1,h2,h3,h4,h5,h6,header,hgroup,html,i,iframe,img,ins,kbd,label,legend,li,mark,menu,nav,\nobject,ol,output,p,pre,q,ruby,s,samp,section,small,span,strike,strong,sub,summary,sup,table,tbody,td,tfoot,th,thead,time,tr,tt,u,ul,var,video{ font:inherit;margin:0;padding:0;vertical-align:baseline;border:0 }\nbody{ font-size:16px; line-height:1.5; color:#999; background:transparent; }\n.wrapper{ overflow:hidden;word-break:break-all;padding:10px; }\nh1,h2{ font-weight:normal; line-height:1.35; margin-bottom:.6em; }\nh3,h4,h5,h6{ line-height:1.35; margin-bottom:1em; }\nh1{ font-size:24px; }\nh2{ font-size:20px; }\nh3{ font-size:18px; }\nh4{ font-size:16px; }\nh5{ font-size:14px; }\nh6{ font-size:12px; }\np,ul,ol,blockquote,dl,table{ margin:1.2em 0; }\nul,ol{ margin-left:2em; }\nul{ list-style:disc; }\nol{ list-style:decimal; }\nli,li p{ margin:10px 0;}\nimg{ max-width:100%;display:block;margin:0 auto 1em; }\nblockquote{ color:#B5B2B1; border-left:3px solid #aaa; padding:1em; }\nstrong,b{font-weight:bold;}\nem,i{font-style:italic;}\ntable{ width:100%;border-collapse:collapse;border-spacing:1px;margin:1em 0;font-size:.9em; }\nth,td{ padding:5px;text-align:left;border:1px solid #aaa; }\nth{ font-weight:bold;background:#5d5d5d; }\n.symbol-link{font-weight:bold;}\n/* header{ border-bottom:1px solid #494756; } */\n.title{ margin:0 0 8px;line-height:1.3;color:#ddd; }\n.meta {color:#5e5c6d;font-size:13px;margin:0 0 .5em; }\na{text-decoration:none; color:#2a4b87;}\n.meta .head { display: inline-block; overflow: hidden}\n.head .h-thumb { width: 30px; height: 30px; margin: 0; padding: 0; border-radius: 50%; float: left;}\n.head .h-content { margin: 0; padding: 0 0 0 9px; float: left;}\n.head .h-name {font-size: 13px; color: #eee; margin: 0;}\n.head .h-time {font-size: 12.5px; color: #7E829C; margin: 0;}\n.small {font-size: 12.5px; display: inline-block; transform: scale(0.9); -webkit-transform: scale(0.9); transform-origin: left; -webkit-transform-origin: left;}\n.smaller {font-size: 12.5px; display: inline-block; transform: scale(0.8); -webkit-transform: scale(0.8); transform-origin: left; -webkit-transform-origin: left;}\n.bt-text {font-size: 12px;margin: 1.5em 0 0 0}\n.bt-text p {margin: 0}\n</style>\n</head>\n<body>\n<div class=\"wrapper\">\n<header>\n<h2 class=\"title\">\nHoward Marks Latest Memo: The Illusion of Cognition\n</h2>\n<h4 class=\"meta\">\n<p class=\"head\">\n<strong class=\"h-name small\">橡树资本Oaktree Capital</strong><span class=\"h-time small\">2022-09-09 11:14</span>\n</p>\n</h4>\n</header>\n<article>\n<p><html><head></head><body>Writing the first memo since February 1993, \"The Value of Forecasting, Where the Rain Comes from\" (The Value of Projections, or Where did All This Rain Come From) In the beginning, I kept saying that I ignored \"forecasting\".</p><p>In the years since, I have explained at length why I am not interested in forecasting — and some of the quotes I really like in the following chapters echo my disdain for forecasting — but I have never written another memo specifically to explain why it is so difficult to make beneficial macro forecasts. Hence the memo.</p><p><b>Introduce thoughtful things</b></p><p>There are two kinds of seers in the world: one who knows nothing about the future, and the other who knows nothing about themselves. — Shortly after John Kenneth Galbraith finalized his memo, \"I Beg to Differ,\" I attended a luncheon with some experienced investors and those outside the investment circle. This is not a social event, but an opportunity for those present to exchange views on the investment environment.</p><p>During the period, the host asked a series of questions: How do you expect inflation to develop? Will there be a recession, and if so, how bad will it be? How will the Russia-Ukraine conflict end? What are the likely impacts of the 2022 and 2024 U.S. elections? I've heard a variety of points of view on this.</p><p>Readers who have tracked my memo for a long time should be able to imagine my thoughts at the time: \"No one in this room is an expert in foreign affairs or politics. No one present has particularly in-depth insight into these topics, and certainly no more than the average person reading this morning's news.\" The ideas conveyed, even on economic issues, do not seem more convincing than others, and I absolutely believe that no one can improve investment outcomes. That's the key.</p><p>It was that luncheon that got me thinking about writing another memo about the unhelpful macro outlook. Soon after, I found some extra material — a book, an article from<i>Bloomberg View (Bloomberg Opinion)</i>This material supports my argument (and perhaps my \"confirmation bias\"-the tendency of people to accept and believe information and arguments that prove their previous opinions). Together, that luncheon and the material inspired the theme of the memo:<b>Many reasons for predicting little benefit.</b></p><p>In order to obtain something useful — whether in manufacturing, academia, or even the arts — there must be a reliable<b>Process</b>Which enables the desired<b>Inputs</b>Convert to desired<b>Output</b>。 In short, the problem is that I don't think there is a process that consistently translates a large number of variables (inputs) related to the economy and financial markets into useful macro forecasts (outputs).</p><p><b>Models</b></p><p>The greatest enemy of cognition is not ignorance, but the delusion of cognition. — Daniel Burstin About my First decade at First National City Bank, there was a word that was hot at the time but has not been heard for a long time now: econometrics. Specifically, it refers to a practice of finding correlations in economic data to produce effective forecasts. Or in short, econometrics studies how to build mathematical models of the economy. In the 1970s, econometricians were hot, but I don't think they are. I think that means their models are not working.</p><p><b>Whether the model is complex and sophisticated or scribbled simple, mathematically based or intuitive, forecasters have no choice but to make judgments based on the model.</b>Models are by definition made up of assumptions: \"If A occurs, then B occurs.\" In other words, the model states relationships and responses. But if we are willing to adopt the output of the model, we must be convinced that the model is reliable. But when I thought of modeling the economy, my first thought was how complicated it would be.</p><p>For example, the United States has a population of approximately 330 million people. Except for the particularly young and some particularly old, the rest are participants in the economy. So there are hundreds of millions of consumers, and millions of workers, producers, and middlemen (many meeting multiple classifications). To predict the path of the economy, it is necessary to predict the behavior of these people — if not every participant, at least the total group size.</p><p>A real simulation of the U.S. economy must deal with billions of interactions or nodes, including with suppliers, customers, and other market participants across the globe. Is it possible to do this? For example, is it possible to predict the behavior of consumers in the following situations: (i) if they earn an extra dollar of income (what is the \"marginal propensity to spend\"?); (ii) if energy prices rise, squeezing other categories in household budgets; (iii) If the price of one commodity increases relative to other commodities (will there be a \"substitution effect\"?); And (iv) if the geopolitical arena is stirred up by events on other continents?</p><p>Clearly, this level of complexity requires frequent use of simplified assumptions. For example, it would be easier to model if it could be assumed that consumers would not buy B in place of A in the event that B is not better or cheaper (or both). It would also help if the producer would not price X below Y if it was assumed that the cost of producing X was no less than Y.</p><p>But despite the higher price of B (or even because of it), consumers are still attracted to the brand effect of B. What happens? What would happen if X was produced and developed by entrepreneurs willing to use losses for several years to gain market share? Is it possible for models to predict the decision of consumers willing to spend more and entrepreneurs willing to make less (or even lose)?</p><p>Furthermore, the model must predict the behavior of each group of participants in the economy in various environments. But the vagaries are multifaceted. For example, a consumer may behave in one way at one moment and in a different way at another similar moment. Given the large number of variables involved, it seems unlikely that two \"similar\" moments will occur in exactly the same way, and it is unlikely that we will see economic participants exhibiting the same behavior.</p><p>Beyond that, participants' behavior will be influenced by their psychology (or should I say their emotions?), and their psychology may be influenced by qualitative, non-economic developments. How can these be modeled?</p><p>How can an economic model be comprehensive enough to deal with situations that have never been encountered before, or that have not been seen in modern times (i.e., in comparable situations)? This is yet another example of how models cannot simply replicate something as complex as the economy.</p><p>A prime example of this, of course, is COVID. It has shut down most of the world's economies, upended consumer behavior, and inspired massive government money-out bailouts. Which aspect of the existing model can predict the impact of the pandemic? Yes, the world experienced a pandemic in 1918, but the situation was so different (there were no iPhones, Zoom calls, etc) that the economic state of affairs of that period had little comparison to 2020.</p><p>In addition to factors such as complexity and psychological fluctuations and dynamic processes that are difficult to capture, consider that attempting to make predictions about things that cannot be expected to remain the same is inherently limiting. Shortly after starting writing this memo, I received Morgan Housel's always wonderful weekly. One of the articles describes a lot of observations in other areas related to our economy and investment.</p><p>The following two are borrowed from the field of statistics, and I think they are relevant to the discussion of economic models and forecasts (\"Small Ways the World Works (Little Ways the World Works) \", Morgan Housel,<i>Collaborative Fund</i>July 20, 2022):</p><p>Stationarity: This is the assumption that history can be used as a guide for future statistics based on the fact that the main factors affecting the system do not change over time. If you want to know how high a levee to build, look at flood data over the past 100 years and assume that the next 100 years will be the same. Stationarity is a fantastic, science-based concept that works until it fails. It is a major driver of important events in economy and politics. [But in our world,] \"things that have never happened before are happening all the time,\" says Stanford professor Scott Sagan. Cromwell's Rule: Never say something won't happen.... Even if there is only a one in a billion chance that something will come true and you will interact with billions of things in your lifetime, therefore you will almost certainly experience some shocking contingency and should always be open to the possibility of the unthinkable becoming a reality. Stationarity may be a reasonable assumption in the field of physical sciences. For example, because of the law of universal gravity, objects can always fall at the same acceleration under established atmospheric conditions. It always turned out that way, and it always will. But few processes in our field are smooth, especially given psychological, emotional, and human behavior, and they change over time.</p><p>Take, for example, the relationship between unemployment and inflation. For the past 60 years or so, economists have relied on the Phillips curve, which argues that wage inflation will rise as unemployment falls because when there are fewer unemployed workers, employees gain bargaining power and can successfully negotiate higher wages. For decades, the unemployment rate of 5.5 percent was also considered to indicate \"full employment\".</p><p>But the unemployment rate fell below 5.5% in March 2015 (and reached a 50-year low of 3.5% in September 2019), but inflation (wages or otherwise) did not rise significantly until 2021. The important relationship described by the Phillips curve has been applied to various economic models built over the decades, but it does not seem to be applicable for much of the past decade.</p><p>Cromwell's Law is just as important. Unlike the physical sciences, there are very few things that absolutely must or must not happen in markets and economics. Therefore, in<i>\"Cycle\"</i>（<i>Mastering the Market Cycle</i>In this book, I list seven terms that investors should remove from their glossary: \"never,\"\"always,\"\"never,\"\"can't,\"\"won't,\"\"will,\" and \"must.\" But if these words really have to be discarded, then the idea of building models that reliably predict the macro future must also be discarded. In other words, almost nothing is immutable in our field.</p><p>The unpredictability of behavior is my favorite topic. The famous physicist Richard Feynman once said, \"Imagine how difficult physics would be if electrons felt.” The rules of physics are reliable precisely because electrons always do what they are supposed to do. They never forget to perform their duties. They never resist. They never strike. They never innovate. They never behave in the opposite way.</p><p><b>But none of this applies to the participants in the economy, and it is because it does not apply that the participants' behavior is unpredictable. How to model the operation of an economy if actors' behavior is unpredictable?</b></p><p><b>We are talking about the future, and there is no one way to predict the future without making assumptions.</b>Small mistakes in assumptions about the economic environment and subtle changes in participants' behavior can both cause serious problems. As mathematician and meteorologist Edward Lorenz famously said, \"A Brazilian butterfly flapping its wings could cause a tornado in Texas.\" (Historian Niall Ferguson mentions this in an article discussed below.)</p><p>In summary, can we consider the economic model to be reliable? Can models replicate reality? Can it describe the behavior of millions of participants and their interactions? Is the process trying to model reliable? Can these processes be reduced to mathematics? Can mathematics capture the qualitative nuances of people and their behavior? Can models predict changes in consumer preferences, changes in corporate behavior, and how participants respond to innovation? In other words, can we trust the output of the model?</p><p>Clearly, economic relations are not static, and the economy is not governed by schematics (schematics that the model is trying to simulate). So the bottom line for me is that the model's output points in the right direction most of the time without violating the assumptions. But it can't always be accurate, especially at critical moments like inflection points…and that's when accurate predictions are most valuable.</p><p><b>Inputs</b></p><p>One thing that cannot be ignored is the fact that all your knowledge is about the past and all your decisions are about the future. — Ian H. Wilson (Former General Electric Executive) Having considered the incredible complexity of the economy and the need to make simplified assumptions (which would reduce the accuracy of any economic model), let's now consider the input required for a model — the raw materials for manufacturing predictions. Are estimated inputs valid? Can we understand them in depth enough to make meaningful predictions?<b>Or does it simply remind us of the ultimate truth about models: \"The input is garbage, the output is still garbage\"?</b>Obviously, no prediction will have a better quality than the quality of the input on which it is based.</p><p>Here's what Neil Ferguson said on July 17 at<i>Bloomberg View (Bloomberg Opinion)</i>Content written:</p><p>Consider what we really want to ask when we ask the question, \"Has inflation peaked?\" We're not just asking about the supply and demand of 94,000 different goods, manufactured goods and services. We are also concerned about the future path of interest rates set by the Federal Reserve, which, aside from the much-touted \"forward guidance\", is far from clear where it will go. What we are asking is how long the strength of the dollar will continue, as it is currently depressing the price of U.S. imports. But there are more questions to be answered. At the same time, the above questions are also indirectly asking how long the Russia-Ukraine conflict will continue, because the chaos caused by the Russia-Ukraine conflict has significantly increased the inflation of energy and food prices since February. We are asking whether oil-producing countries such as Saudi Arabia will respond to Western governments' requests to increase crude oil production... We should probably also ask ourselves what impact the latest coronavirus, Omicron BA.5, will have on the Western labor market. British data show that BA.5 is 35% more contagious than its predecessor, BA.2, which in turn is more than 20% more contagious than the original Omicron. If you want to add all these variables to your model, then I wish you good luck. In fact, the future path of inflation, like the future direction of the Russia-Ukraine conflict and the transmission path of the COVID-19 epidemic, is uncertain. I found Ferguson's article so relevant to the topic of this memo that I am attaching a link to the article here. The article makes a lot of important points, although I beg to differ in one respect. Ferguson mentioned above, \"In fact, the future path of inflation, like the future direction of the Russia-Ukraine conflict and the spread path of the new coronavirus epidemic, is as uncertain.\"</p><p>I think accurate predictions of inflation are \"less likely\" to be achieved than predictions of the other two problems (if they are predictable), because accurate predictions of inflation require correct predictions of both events and a thousand other influential factors. How can anyone get all these things right?</p><p>Here I briefly introduce the forecasting process mentioned in The Value of Forecasting:</p><p>I guess for most fund managers, the process looks like this: \"I predict that the economy will do A. If A happens, interest rates should present B. If interest rates are B, the stock market should present C. In this environment, the best performing sector should be D, and stock E should rise the most.\" Then build the portfolio accordingly, with a view to achieving the best performance in this scenario. But anyway, how likely is E? Keep in mind that E is conditional on A, B, C, and D. In the field of forecasting, a two-thirds accuracy rate would be a great achievement. But if there is a 67 percent chance that each of the five predictions is correct, the result is a 13 percent chance that all five predictions are correct and the stock will perform as expected.<b>Predicting event E based on assumptions about A, B, C, and D is what I call single-scenario prediction.</b>In other words, if the result of the hypothesis regarding A, B, C, or D turns out to be wrong, the prediction result of E is unlikely to be achieved. Only if all potential predictions are correct can E get consistent results as predicted, but this is extremely rare. No one can make an informed investment without considering (i) the other possible outcomes of each element, (ii) the possibility of other scenarios emerging, (iii) what are the prerequisites to make one of the hypotheses a reality, and (iv) what are the effects on E.</p><p>Ferguson's article raises an interesting question about economic modeling: What assumptions should we make about what macro environment economic participants are in? The<b>This question just shows an endless loop: in order to predict the overall performance of the economy, we need to make assumptions about consumer behavior and so on. But to predict consumer behavior, don't we need to make assumptions about the overall economic environment?</b></p><p>In my first memo on the pandemic, Nobody Knows II (March 2020), I mentioned that in discussing the coronavirus, Harvard epidemiologist Marc Lipsitch once said: (i) facts; (ii) well-founded inferences derived from analogies with other viruses, and (iii) opinions or speculations. This is our standard practice when dealing with uncertain events. In economic or market forecasts, we have a lot of history and many similar past events to infer (but none of COVID). But even if these things are used as inputs by a well-constructed prediction model, they are still unlikely to predict the future. They can be useful fodder or they can be garbage.</p><p>To illustrate this, people often ask me which cycle I have experienced in the past is most similar to the current one. My answer is that current developments bear short-lived similarities to some past cycles, but no absolute similarities.<b>In each case, the differences are vast and outweigh the similarities.</b></p><p><b>Even if we can find an identical previous period, to what extent should we rely on this single sample? I guess the answer is not much. Investors rely on historical references (and the forecasts they make based on them) because they fear they will act blindly without them. But that does not mean that the information is reliable.</b></p><p><b>Unpredictable effects</b></p><p>Prediction creates the mirage that the future is knowable. — Peter Bernstein<b>We cannot consider the plausibility of predictions without first determining whether our world is orderly or random.</b>In short, is it completely predictable, completely unpredictable, or somewhere in between? For me, the conclusion is somewhere in between, but more inclined to be unpredictable, to the point where most predictions don't help. Since our world is predictable at some times and unpredictable at others, what's the use of prediction if we can't distinguish when it is predictable and when it is unpredictable?</p><p>I learned a new word from reading Ferguson's article: \"deterministic\". The Oxford Dictionary defines it as \"causally determined by a previous event or natural law\". The world is a lot simpler when we handle things by the rules...just like Feynman's electrons. But it is clear that economies and markets are not governed by the laws of nature – thanks to human involvement – and that events before it may be \"foreshadowing\" or \"tending to repeat\", but events rarely happen twice in the same way. Therefore, I believe that the processes that make up the operation of economies and markets are not deterministic, which means that they are unpredictable.</p><p>Furthermore, the input is clearly unreliable. Many are random, such as weather, earthquakes, accidents and deaths. Others involve political and geopolitical issues – some we know, some that have not yet surfaced.</p><p>In the Bloomberg Opinion article, Ferguson referred to the British author G.K. Chesterton. This reminds me of my quote from Chesterton in Risk Revisited Again (June 2015):</p><p>The real problem with our world today is not that the world is irrational, nor that it is a rational world. The most common question is: The world is almost rational, but not quite. Life is not a contradiction, but it is a trap for logicians.<b>It looks slightly more precise and regular than it really is; Its precision is obvious, but its imprecise side is hidden; Its wildness is also lurking.</b>(Bold is added by the author) Returning to the luncheon introduced on the first page, the host's opening remarks are roughly as follows: \"In recent years, we have experienced events such as the COVID-19 pandemic, the astonishingly successful Federal Reserve bailout policy, and the Russia-Ukraine conflict. This is a very challenging environment because all of this comes out of the blue.\"</p><p>For him, I suppose, this means that attendees should let themselves off the trouble of inaccurate predictions for 2020-2022, continue to predict the future and bet on their own judgment. But my reaction was completely different: \"There are many events that affect the current environment.<b>And isn't the fact that no one can predict any of them enough to convince those present that they should give up their predictions? \"</b></p><p>For another example, let's think back to the fall of 2016. There are two things that almost everyone is convinced of: (i) Hillary Clinton will be elected president; (2) If for some reason Donald Trump is elected, the market will collapse. Still, it turned out that Trump won and the market soared.</p><p>The past six years have had a profound impact on the economy and markets,<b>I believe that any prediction of the 2016 election in the conventional view at the time would not have been correct.</b>Isn't that enough to convince people that (i) we don't know what will happen in the future, and (ii) we can't understand how the market will react to what happens?</p><p><b>Can forecasting bring excess?</b></p><p>It is not ignorance that keeps us in trouble, but fallacious assertions that seem correct. — Mark Twain As I mentioned in my recent memo, \"Thinking About Macro,\" in the 1970s, we used to describe economists as \"investment directors who never enter the market.\" In other words, economists make many predictions; The actual situation will prove them right or wrong; Then they proceed to make new predictions; But they do not track the frequency of the prediction (or, they do not publish statistics).</p><p>Can you imagine hiring a fund manager without reference to your track record (or if you were a fund manager, could you imagine being hired in this scenario)? But economists and strategists don't lose their jobs by not releasing statistics, perhaps because there are always clients willing to pay for their forecasts.</p><p>Are you a consumer of these predicted outcomes? Are forecasters and economists employees of your company? Or do you subscribe to their publications and invite them to give briefings, like my previous employer? If so, do you know how often everyone predicts correctly? Have you found a way to rigorously determine which of these predictions can be relied on and which to ignore? Is there a way to quantify how these projections contribute to your return on investment?</p><p>I ask this litany of questions because I haven't seen or heard of any research in this area. It is unimaginable that information on whether macro forecasts will yield excess returns is scarce globally, especially disproportionately compared to the number of people who need such information.</p><p>Despite the lack of evidence to prove its worth, macro forecasts continue. Many forecasters are part of, or are providing advice and forecasts to, equity fund management teams.</p><p>One thing we can be sure of is that actively managed equity funds have been losing market share for decades due to the poor performance of actively managed funds, which now account for less than half of the U.S. equity mutual fund market, being replaced by index funds and other passive investment vehicles. Is it the reason that macro forecasting is inherently unhelpful for investment?</p><p>As far as I know, the only thing to find quantitative information about this issue is the performance of so-called macro hedge funds. The Hedge Fund Research Organization (HFR) has released the Hedge Fund Weighted Composite Index as well as some sub-strategy indices. Here's a look at the long-term performance of the Hedge Fund Weighted Composite Index, the Macro Hedge Sub-Strategy Index, and the S&P 500.<img src=\"https://static.tigerbbs.com/79717be010acf96241bf0336e5ac3381\" tg-width=\"963\" tg-height=\"272\" referrerpolicy=\"no-referrer\" width=\"100%\" height=\"auto\"/>* Performance as of July 31, 2022. The hedge fund index shown is a weighted composite index of each fund.</p><p>In the table above, according to HFR data, the average performance of hedge funds during the study period was much lower than that of the S&P 500, while the average performance of macro hedge sub-strategy funds was much worse (especially between 2012 and 2017). Given that investors continue to entrust roughly $4.5 trillion to hedge fund managers, these funds must provide some benefit beyond returns, but it's unclear what that will be. This seems to be especially true for macro hedge funds.</p><p>To confirm my view of forecasting, I will next give a rare example of self-assessment: a seven-page feature in the July 24 New York Times \"Sunday Opinion\" column entitled \"I Were Wrong.\" In the article, eight New York Times \"Opinion\" columnists opened up about their misguided predictions and biased advice.</p><p>Most relevant here is a confession written by Paul Krugman entitled \"I Was Wrong About Inflation\". I excerpted and stringed some of these:</p><p>In early 2021, there was a heated debate among economists about the possible consequences of the \"American Rescue Plan\"…I was on the [side of being less worried about the impact of inflation]. Of course, it turned out to be a very bad decision… … …History couldn't have enabled us to anticipate such overheated inflation. So something went wrong with my model…one possible reason is that history is misleading…and also, the perturbations created to adapt to the pandemic and its aftermath may still be playing a big role. Of course, the Russia-Ukraine conflict and the epidemic prevention and control measures in major Chinese cities have undoubtedly pushed this level of interference to a whole new level…In any case, the whole thing has become a lesson of humility. Incredibly, the standard economic model has been working fairly well after the 2008 financial crisis, and I thought at the time there was no problem applying the same model in 2021. In retrospect, I should have realized at the time that this extrapolation was inherently risky in the new world trends that emerged post-Covid. (Bold added by the author) I admire Krugman for showing such astonishing candor (although I have to say that I don't recall many market forecasts between 2009 and 2010 that were optimistic enough to portray the reality of the following decade).<b>Krugman's explanation of his mistakes is good in itself, but I don't see him mention abandoning modeling, inference, or forecasting in the future.</b></p><p>This humility may even permeate the Federal Reserve, one of the world's largest economic forecasters, with more than 400 PhDs in economics. Here's what economist Gary Shilling wrote in Bloomberg Opinion on August 22:</p><p>The Fed's forward guidance became a disaster, leaving its own credibility challenged. Chairman Jerome Powell seems to take the same view, and the outside world should stop speculating about what the Fed thinks about interest rates, economic growth and inflation at different points in the future...<b>The fundamental problem with forward guidance is that it relies on data, which itself comes from the Fed's previous track record of poor forecasts.</b>The Federal Reserve has been overly optimistic about the economic recovery after the Great Recession of 2007-2009. In September 2014, policymakers predicted real GDP growth of 3.40 percent in 2015, but by September 2015 they were forced to keep cutting their estimates to 2.10 percent.<b>Federal Funds rate is not a market-determined interest rate, but is set and controlled by the Federal Reserve, and no one challenges the authority of the Federal Reserve. Furthermore, Federal Open Market Committee (FOMC) members are notoriously bad at predicting what actions they themselves will take...</b>In 2015, their average forecast for the federal funds rate was 0.90% in 2016 and 3.30% in 2019. The actual numbers are 0.38% and 2.38% respectively…To be sure, many of the ongoing events are creating uncertainty in the markets, but forward guidance from the Fed has been highly sought after and important. Recall that earlier this year, the Fed also viewed inflation from the pandemic and friction to restart the economy after supply chain disruptions as temporary. Only later did the Fed discover things weren't good and reversed course, raising interest rates and signaling further sharp rate hike. The Fed's erroneous forecasts led to wrong forward guidance, adding to financial market volatility. (Bold added by the author) One last point on this question, that is, where are the people who make a name for themselves (and become rich) by profiting from the macro view? Of course, I can't know everyone in the investment community, but among those I know or know, I think there are only a few very successful \"macro investors\". When there are few instances of something, as my mother once said, \"the exception just confirms the law\".</p><p><b>The rule in this example is that macro forecasts rarely lead to outstanding performance. To me, the extraordinary success story just proves that this statement is universal truth.</b></p><p><b>Forecasted needs of practitioners</b></p><p>Forecasting reveals the forecaster more than it does the future. -Warren Buffett How many people can make macro forecasts that are valuable most of the time? Not much, I think. And how many investment managers, economists and forecasters have tried? Thousands to say the least. Which raises an interesting question: why predict? If macro forecasting won't help investment succeed over time, why do so many practitioners in the investment management industry believe in forecasting and rush to forecast results? I think the typical reasons for this might be:</p><p><ul><li>It's part of the job.</p><p></li><li>Investors always do this.</p><p></li><li>Everyone I know does this, especially my competitors.</p><p></li><li>I've been doing this all the time – I can't leave it at that now.</p><p></li><li>If I don't, I won't be able to attract customers.</p><p></li><li>Since investing involves deploying capital in order to benefit from future events, how can a good job be expected without a view of those events? We need predictions, even if they aren't perfect.</p><p></li></ul>This summer, my son Andrew recommended a very interesting book: Mistakes Made (But Not Mine): Why We Make Excuses for Stupid Beliefs, Bad Decisions, and Hurting Behavior (Mistakes Were Made (but Not by Me): Why We Justify Foolish Beliefs, Bad Decisions, and Hurtful Acts), It was written by psychologists Carol Tavris and Elliot Aronson.<b>The theme of the book is self-justification.</b></p><p><b>The authors explain that \"cognitive dissonance\" occurs when people are faced with new evidence to question their previous positions, and when this happens, the subconscious mind causes them to struggle hard to prove and maintain their previous positions.</b>Here are some curated snippets:</p><p>If you hold a set of beliefs that guide your practice, and you learn that some of them are incorrect, you must either admit that you were wrong and change your approach, or reject new evidence. Most people, when confronted directly with evidence that they have done wrong, do not change their views or plan of action, but argue it more stubbornly. Once we have identified a belief and proved its wisdom, it is obviously hard work to change our mind. It is much easier to put new evidence into an existing framework for mental argumentation for acceptance than to change the framework. The mechanisms that people typically employ in responding to evidence that calls their beliefs into question include these (paraphrasing the author):</p><p><ul><li>Unwillingness to listen to discordant messages;</p><p></li><li>Selectively remember parts of their lives, focusing on those that support their views; as well as</p><p></li><li>Acting under a cognitive bias that allows people to only see what they want to see and seek some kind of confirmation for what they already believe.</p><p></li></ul>These are, I believe, factors that cause people to consistently make predictions and rely on them. So in this case, what kind of manifestation will there be?</p><p><ul><li>Think of macro forecasts as an integral part of investing;</p><p></li><li>A passion for recalling correct predictions, especially those that are bold, non-market consensus;</p><p></li><li>Overestimate the accuracy of forecasts;</p><p></li><li>Forgetting or downplaying false predictions;</p><p></li><li>Failure to keep records of the accuracy of forecasts or failure to calculate the average success rate;</p><p></li><li>Value rewarding good returns for accurate forecasts;</p><p></li><li>Emphasizing that \"everyone does this\"; as well as</p><p></li><li>Perhaps most importantly, blame unsuccessful predictions on being blinded by random or exogenous events. (But, as I said before, that's the point: why make predictions if they become inaccurate so easily?)</p><p></li></ul>Most people — even honest people with good hearts — take positions or actions that serve their own interests, sometimes at the expense of others or objective truth. Instead of perceiving it themselves, they think that what they do is the right thing; They also seek a lot of valid reasons. As Charlie Munger often quotes Demosthenes, \"Nothing is easier than deceiving oneself. For man always believes what he wants.\"</p><p>I don't think predictors are liars or charlatans. Most of them are intelligent intellectuals who think they are doing something useful.<b>But self-interest causes them to behave in a certain way, and self-defense causes them to stick to their guns in the face of evidence to the contrary.</b>As Morgan Housel put it in a recent current affairs briefing:</p><p>The inability to predict the past has no effect on our intention to predict the future. Certainty is so precious that we never give up on the pursuit of it, and if people are honest about how unpredictable the future can be, most people can't get out of bed early in the morning. (From \"Big Faith,\" Collaborative Fund, August 24, 2022) On my birthday a few years ago, Richard Masson, co-founder of Oak, gave me a fun gift that matched his style. The gift that time was<i>The New York Times</i>A bound copy of. I've been hoping for the opportunity to write about my favorite subheadline for the October 30, 1929 issue, where the Dow Jones Industrial Average fell almost 23% in just two days.</p><p><b>The headline read, \"Bankers Optimistic.\"</b>(However, in the following three years, the Dow fell about 85%). Most bankers and fund managers seem inherently optimistic about the future. Other than that, it is in their best interest as it helps them do more business. But their optimism certainly contributes to their predictive views and the resulting behavior.</p><p><b>Can or can't?</b></p><p>\"I never think about the future — because it is coming.\" — Albert Einstein considers the following aspects of macro forecasting:</p><p><ul><li>The number of assumptions/inputs required,</p><p></li><li>Number of processes/relationships to be included,</p><p></li><li>The inherent unreliability and instability of these processes, and</p><p></li><li>The role of randomness and the possibility of accidents.</p><p></li></ul>The most important thing for me is that forecasts cannot always be correct to the point of value. I've mentioned this many times, but for the sake of completeness, I'll reiterate my views on the utility (or rather, futility) of macro forecasting:</p><p><ul><li>Most forecasts consist of extrapolations about past performance.</p><p></li><li>Since macro developments typically do not deviate from previous trends, inference is usually successful.</p><p></li><li>On this basis, most of the predictions are correct. However, because inferences are usually anticipated by the price of the security, those who base their expectations on inferences do not enjoy excess benefits when the inferences are established.</p><p></li><li>Occasionally, economic behavior does materially deviate from past patterns. Since this deviation is unexpected to most investors, its appearance affects the market, meaning that an accurate forecast of the deviation will lead to lucrative profits.</p><p></li><li>However, because the economy does not often deviate from past performance, few deviations can be accurately predicted, and most deviation forecasts prove to be wrong after the fact.</p><p></li><li>So we have (i) inferential forecasts, most of which are correct but do not generate excess benefits, and (ii) potentially profitable biased forecasts that will rarely be correct and therefore generally do not generate excess benefits either.</p><p></li><li>Argued: Most forecasts do not increase returns.</p><p></li></ul>During the luncheon mentioned at the beginning of this memo, people were asked about expectations for things like Fed policy and how that affected their investment stance. One person replied, \"I think the Fed will remain highly concerned about inflation, so there will be a big rate hike, which will lead to a recession. So I choose to hedge.\" Another said, \"I expect inflation to slow in the fourth quarter, while the Fed will turn dovish in January next year. Start cutting interest rates and stimulate the economy. I'm very bullish on 2023.\"</p><p>We hear it all the time.<b>But it is important to recognize that these people are using the one-factor model:</b>The speaker's prediction is based on a single variable. Speaking of simplifying assumptions: these forecasters implicitly believe that everything is the same except for Fed policy. When three-dimensional chess is needed, they are still playing flat checkers.</p><p>Leaving aside the impossibility of predicting the Fed's behavior, the influence of inflation on that behavior, and the market's response to inflation, what about other important considerations? If there are a thousand things that play a role in determining the future direction of the economy and markets, what are the other 999 things? What about the impact of wage negotiations, midterm elections, Russia-Ukraine conflict and oil prices?</p><p>The truth is that people can only remember very limited things in their minds at any one time.<b>It is difficult to take a large number of factors into account, and it is even harder to understand how a large number of things will interact with each other (correlation is always the real thinking puzzle).</b></p><p>Even if you somehow manage to get the economic forecast right, that's only half the battle. You still need to predict how economic activity will translate into market outcomes. This requires an entirely different prediction and also involves a myriad of variables, many of which relate to psychological factors and are therefore almost unknowable.</p><p>According to student Warren Buffett, Ben Graham once said, \"In the short term, the market is a voting machine, but in the long term, it is a weighing machine.\" How do you predict investors' short-term choices? Some economic forecasters have concluded that the actions announced by the Federal Reserve and Treasury Department in March 2020 will rescue the U.S. economy and help the economic recovery. But I don't know anyone who predicted a hot bull market before the recovery began.</p><p>As I mentioned earlier, in 2016 Buffett shared with me his thoughts on macro forecasts. \"For a piece of information to be useful, it must meet two criteria: first, it must<b>Important</b>And secondly must<b>It can be seen</b>。\"</p><p><ul><li><b>Of course, the macro outlook matters.</b>Today, investors seem to have caught on to what every forecaster is saying, macro events, and signals from the Fed's intermittent tightening action. Unlike my early days in the industry, it seems that macro factors are everything today, and corporate development is less of a concern.</p><p></li><li><b>But I strongly agree with Buffett that the macro future is unknowable,</b>Or at least almost no one consistently knows more than the vast array of investors, which is the key to trying to gain cognitive advantage and make excellent investment decisions.</p><p></li></ul>Obviously, Buffett, whose name tops the list of successful investors, avoids macro forecasts and focuses more on the \"micro\" areas than others: companies, industries and securities to succeed.</p><p>I introduced the concepts of the \"cognitive” and\" agnostic” schools in a 2001 memo entitled \"What's It All About, Alpha?” and elaborated on Them in 2004 in Us and Them. As a close to the current memo, I'll insert some of what I've written about the two genres in the latter:</p><p>Most of the investors I've met over the years belong to the \"cognitive\" school. This was the case when I analyzed stocks from 1968 to 1978, and even when I turned to non-mainstream investing but still worked in equity-centric investment managers from 1978 to 1995.</p><p>It is easy to identify members of the \"cognitive\" school:</p><p><ul><li>They believe that understanding the future direction of the economy, interest rates, markets, and mainstream stocks that are receiving widespread attention, is essential to investment success.</p><p></li><li>They are confident that they can achieve this.</p><p></li><li>They knew they could do it.</p><p></li><li>They know that many people are trying to do this too, but they think that either (i) everyone can succeed at the same time, or (ii) only a few people can do it, but they are one of them.</p><p></li><li>They are willing to invest based on their view of the future.</p><p></li><li>They are also happy to share their opinions with others, although correct predictions should be worth a thousand dollars and no one will give them away for free.</p><p></li><li>They rarely look back and seriously review their achievements as forecasters.</p><p></li></ul>\"Confidence\" is the key word to describe members of the genre. On the other hand, for the \"agnostic\" genre, the term, especially when looking at the macro future, should be \"cautious\".<b>Its believers usually believe that the future cannot be predicted; Nor do you have to anticipate the future; The right goal should be to do your best to invest on the basis of acknowledging that you do not have this perception.</b></p><p>As a member of the \"cognitive\" school, you can have an opinion on the future (and perhaps someone else hereby takes notes). You may be sought after and seen as the ideal dinner guest…especially when the stock market is rising.</p><p>If you add the \"agnostic\" genre, the results are more complicated. You quickly get tired of expressing \"agnostic\" to friends and strangers. It won't be long before even relatives stop asking you what you think about the market movement. You'll never enjoy the one-in-a-thousand surprise moment when a prediction comes true, or the joy of the Wall Street Journal publishing your photo.</p><p>On the other hand, you are also protected from forecasting errors and from losses that result from investing based on overconfidence in the future.<b>But what do you think it will feel like when a prospect asks you about your investment prospects and you have to say \"I don't know\"?</b></p><p>For me, the bottom-line judgment of which genre is the best comes from the quote of the late Stanford behavioral scientist Amos Tversky:<b>\"It's scary to realize that you may not know something, but even more scary is to realize that, in general, the world is run by people who firmly believe they know exactly what is happening.\"</b></p><p>In the investment management business, it is certainly standard practice to put forward macro forecasts, share them upon request, and use them as a basis for entrusted investments for clients. It also seems to be customary for fund managers to trust forecasts, especially their own. As mentioned above, not doing so seems out of place. But are their beliefs matter of fact? I'd love to hear everyone's opinion.</p><p>Years ago, a well-respected sell-side economist (an old acquaintance of mine from my time at Citi) called me: \"You have changed my life,\" he said. \"I've long since stopped making predictions. Instead, I'm just telling people what happened today and the impact I think it might have in the future. Life has been a better place ever since.\" Can I help you reach the same state of happiness?</p><p>This article is written by: Howard Marks</p><p></body></html></p>\n<div class=\"bt-text\">\n\n\n<p> source：<a href=\"https://mp.weixin.qq.com/s/Ipkl8u3LTMsZu6vV05TfWg\">橡树资本Oaktree Capital</a></p>\n\n\n</div>\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"https://static.tigerbbs.com/ec9d546348d262a35388609835ccbfe2","relate_stocks":{".DJI":"道琼斯"},"source_url":"https://mp.weixin.qq.com/s/Ipkl8u3LTMsZu6vV05TfWg","is_english":false,"share_image_url":"https://static.laohu8.com/e9f99090a1c2ed51c021029395664489","article_id":"1159026938","content_text":"自1993年2月开始写第一篇备忘录，《预测的价值，雨源自何处》（The Value of Projections, or Where'd All This Rain Come From）开始，我一直在表达我将\"预测\"置之度外。自那之后的多年里，我详细地解释了为何我对预测不感兴趣——以下章节中一些我非常喜欢的引述也呼应了我对预测之不屑一顾——但我从来没有再专门写一篇备忘录来解释过为何作出有益的宏观预测如此之难。因此就有了这篇备忘录。引入深思的事情世界上有两类预言家：一类对未来并无所知，而另一类不知道自己并无所知。——约翰·肯尼斯·加尔布雷思最后润色备忘录《敢于另辟蹊径》（I Beg to Differ）后不久，我与一些经验丰富的投资者以及投资圈外的人士一同出席了一次午餐会。这并非一项社交活动，而是为在场的人提供了就投资环境交流彼此观点的机会。期间，主持人提出了一系列问题：您预期通胀会如何发展？会不会出现经济衰退，如果会，情况有多严重？俄乌冲突将以何种方式结束？2022年和2024年美国大选可能会产生什么影响？对此，我听到了各种各样的观点。长期追踪我备忘录的读者应该可以想象到我当时的想法：\"这个房间里没有人是外交事务或政治方面的专家。在场没有人对这些话题有特别深入的见解，当然也不会比阅读今天早上新闻的普通人知道得更多。\"所传达的思想，即使是针对经济问题，似乎也没有比其他人更具说服力，而且我绝对相信，没有人能够改善投资结果。这就是关键。正是那次午餐会让我开始考虑写另一篇关于宏观展望无益的备忘录。不久之后，我发现一些额外的素材——一本书、一篇来自彭博观点（Bloomberg Opinion）的文章和一篇报纸上的文章——这些素材都支持我的论点（也可能是我的\"证实偏差\"——即人们倾向于接受和相信能够证明自己先前已有观点的信息和论据）。那次午餐会和这些素材共同启发了这份备忘录的主题：预测鲜有益处的诸多原因。为了获得有用的东西——无论是在制造业、学术界，甚至是艺术领域——必须有一个可靠的过程，能够将所需的输入转换为期望的输出。简言之，问题在于，我认为没有一个过程能够始终如一地将大量与经济和金融市场相关的变量（输入）转化为有用的宏观预测（输出）。模型认知最大的敌人并非无知，而是对认知的错觉。——丹尼尔·布尔斯廷大约在我任职于第一花旗银行（First National City Bank）的头十年，有一个在当时很热门但现在已经很久没听到过的词：计量经济学。具体是指在经济数据中寻找关联从而产生有效预测的一种做法。或者简言之，计量经济学研究如何建立经济的数学模型。在上世纪70年代，计量经济学者们炙手可热，但我觉得他们现已风光不再。我认为这意味着他们的模型不起作用。无论模型是复杂精密的还是潦草简单的、基于数学的还是出于直觉的，预测者都别无选择只能根据模型做出判断。模型从定义而言是由假设组成的：\"如果A发生，那么B就会发生。\"换句话说，模型陈述了关系与响应。但要我们愿意采纳模型的输出结果，就必须让我们相信这个模型是可靠的。可当我想到要为经济建模时，我的第一反应是这会多么的复杂。例如，美国大约有3.3亿人口。除去特别年幼的和一些特别年老的，其余的人都是经济的参与者。因此，有数以亿计的消费者，以及数以百万计的工人、生产商和中间商（许多人满足多个分类）。要预测经济的发展路径，就必须预测这些人的行为——就算不预测每位参与者，至少也要预测群体总量。美国经济的真实模拟必须处理数十亿的互动或节点，包括与全球各地的供应商、客户和其他市场参与者的互动。是否有可能做到这一点？例如，是否能预测消费者在下列情况下做出的行为：（一）如果他们获得额外一美元的收入（\"边际消费倾向\"是多少？）；（二）如果能源价格上涨，挤压了家庭预算中的其他类别；（三）如果一种商品的价格相对于其他商品上涨（是否会产生\"替代效应\"？）；以及（四）如果地缘政治舞台被其他大洲的事件搅动？显然，这种复杂程度需要频繁使用经简化的假设。例如，如果可以假设在B并非更好或更便宜（或两者兼而有之）的情况下，消费者不会购买B来代替A，那么建模会更容易。如果假设生产X的成本不低于Y，那么生产者不会将X定价低于Y，这也会有所帮助。但尽管B的价格更高（甚至正因如此），消费者仍被B的品牌效应所吸引，结果会怎样？如果X是由愿意用亏损几年以获取市场份额的企业家生产开发的，结果会如何？模型是否有可能预测消费者愿意多花钱和企业家愿意少赚钱（甚至亏损）的决定？此外，模型必须预测经济中每组参与者在各种环境中的行为。但变幻莫测的因素是多方面的。例如，消费者可能在某一时刻是一种行为方式，而在另一类似时刻则是不同的行为方式。考虑到所涉及的大量变量，两个\"相似\"时刻似乎不可能以完全相同的方式发生，而我们也不太可能看到经济参与者表现出相同的行为。除此之外，参与者的行为将受到他们的心理（或者我应该说他们的情绪？）的影响，而且他们的心理可能会受到定性的、非经济发展的影响。这些如何建模？一个经济模型如何能全面到足以处理以前从未遇到过的情况，或者在现代（即在可比情况下）未曾出现过的情况？这是又一个例证，说明模型无法简单复制像经济这样复杂的事物。当然，其中一个典型例子就是新冠疫情。它导致全球大部分经济体停摆，颠覆了消费者行为，并激发了政府大规模的发钱纾困政策。已有模型的哪个方面能够预测疫情影响？是的，世界曾在1918年经历过一场疫情，但情况截然不同（当时没有iPhone、Zoom通话等等），以至于那个时期的经济事态与2020年几乎没有任何可比性。除了复杂程度和难以捕捉的心理波动和动态过程等因素外，还要考虑到试图对不能预期保持不变的事物进行预测本身就具有局限性。在开始撰写本备忘录后不久，我收到了Morgan Housel一贯精彩的周刊。其中一篇文章描述了很多与我们的经济和投资相关的其他领域的观察结果。以下两个是从统计学领域借用的，我认为它们与经济模型和预测的讨论有关（\"世界运作的小方法 （Little Ways the World Works ）\"，Morgan Housel，Collaborative Fund，2022年7月20日）：平稳性：这是一种假设，基于影响系统的主要因素不会随时间推移而变化，该假设认为历史可以作为未来统计的指导。如果想知道要建造多高的堤坝，就查看过去100年的洪水数据，并假设未来100年也会相同。平稳性是一个奇妙的、基于科学的概念，且在它失效之前一直有效。它是经济和政治中重要事件的主要驱动力。[但在我们的世界中，]\"以前从未发生过的事情一直在发生，\"斯坦福大学教授Scott Sagan说。克伦威尔法则：永远不要说某事不会发生。……即使某件事仅有十亿分之一的可能性成真，而你在一生中会与数十亿件事物互动，因此你几乎肯定会经历一些令人震惊的意外事件，并应该始终对不可思议的事情成为现实的可能性持开放态度。平稳性在物理科学领域可能是合理的假设。例如，因万有引力定律，在既定的大气条件下，物体总是能以相同的加速度下降。结果总是这样，而且将永远这样。但在我们的领域里，很少有过程是平稳的，特别是考虑到心理、情感和人类行为，并且它们会随着时间的推移而变化。以失业率和通胀之间的关系为例。在过去约60年里，经济学家依赖菲利普斯曲线，该曲线认为工资通胀将随着失业率的下降而上升，因为当未就业的工人减少时，员工获得议价能力，并可成功地通过谈判获得更高的工资。几十年来，人们还认为5.5%的失业率表明\"充分就业\"。但失业率在2015年3月降至5.5%以下（并在2019年9月达到3.5%的50年以来低位），但直到2021年通胀（工资或其他方面）都没有显著上升。菲利普斯曲线描述的重要关系应用在了几十年来建立的各种经济模型中，但它在过去十年的大部分时间里似乎并不适用。克伦威尔法则也同样重要。与物理科学不同，在市场和经济领域里，很少有绝对必须发生或绝对不能发生的事情。因此，在《周期》（Mastering the Market Cycle） 一书中，我列出了投资者应该从词汇表中清除的七个术语：\"从不\"、\"总是\"、\"永远\"、\"不能\"、\"不会\"、\"将\" 和 \"必须\"。但如果这些词真的必须被摒弃，那么也必须摒弃能建立可靠地预测宏观未来的模型的想法。换言之，在我们的领域里，几乎没有什么是不可变的。行为的不可预测性是我最喜欢的话题。著名物理学家理查德·费曼 （Richard Feynman） 曾经说过：\"想象一下，如果电子有感觉，物理学将会多难。\"物理规则是可靠的，正是因为电子总是做它们应该做的事情。它们永远不会忘记履行自己的职责。它们从不反抗。它们从不罢工。它们从不创新。它们从不以相反的方式行事。但这些都不适用于经济中的参与者，正是因为不适用才导致参与者的行为是不可预测的。如果参与者的行为是不可预测的，那么如何对经济的运行进行建模?我们在谈论未来，没有任何一种方法可在不需要做出假设的情况下预测未来。有关经济环境假设的小错误和参与者行为的细微变化都可能造成严重问题。正如数学与气象学家爱德华·洛伦茨 （Edward Lorenz） 的名言：\"一只巴西的蝴蝶扇动翅膀就可能在美国德克萨斯州引发龙卷风。\"（历史学家尼尔·弗格森 （Niall Ferguson） 在下文讨论的一篇文章中提到了这一点。）综上所述，我们能否认为经济模型是可靠的？模型可否复制现实？它能否描述数以百万计的参与者行为及他们之间的互动？试图建模的过程是否可靠？这些过程可否简化为数学？数学能否捕捉人及其行为的定性细微差别？模型能否预测消费者偏好的变化、企业行为的变化以及参与者对创新的反应？换言之，我们能否相信模型的输出结果？显然，经济关系并非一成不变，经济也不受示意图（模型试图模拟的示意图）所支配。因此，对我来说，底线是，在不违反假设的情况下，模型的输出结果大部分时间指向正确方向。但它不可能总是准确的，尤其是在拐点等关键时刻……而这正是准确预测最有价值的时候。输入无法忽略的一个事实是，你所有的知识都是关于过去的，你所有的决定都是关乎未来的。——伊恩·威尔逊 （Ian H.Wilson ）（通用电气前高管）在考虑了经济不可思议的复杂性，以及需要做出经简化的假设（这将降低任何经济模型的准确性），现在让我们来考虑一个模型所需的输入——制造预测的原材料。预估的输入是否有效？我们能否对它们有足够深入的了解，从而得出有意义的预测？还是让我们简单地想起关于模型的终极真理：\"输入垃圾，输出的还是垃圾\"？显然，没有任何预测的质量会比它所基于的输入的质量更好。以下是尼尔·弗格森7月17日在彭博观点 （Bloomberg Opinion） 撰写的内容：考虑一下当我们提出\"通胀是否已见顶？\"这个问题时真正想问的。我们在问的不仅仅是94,000种不同商品、制成品和服务的供需情况。我们还在关心美联储设定的未来利率路径，撇开备受吹捧的\"前瞻性指引\"不谈，其去向何方仍远未明确。我们在问的是美元强势还会持续多久，因为它目前正在压低美国进口商品的价格。但还有更多的问题有待解答。与此同时，以上问题也在间接地询问，俄乌冲突还会持续多久，因为自2月份以来，俄乌冲突造成的混乱已经显著加剧了能源和食品价格的通胀。我们是在问沙特阿拉伯等产油国是否会回应西方政府增加原油产量的请求......我们可能还应该问问自己，最新的新冠病毒奥密克戎BA.5将对西方劳动力市场产生什么影响。英国数据表明，BA.5的传染性比其前身BA.2高35%，而BA.2的传染性又比原始奥密克戎高20%以上。如果要将所有这些变量添加到你的模型中，那我祝你好运。事实上，通胀的未来路径，如同俄乌冲突的未来走向和新冠疫情的传播路径一样，都无法确定。我发现弗格森的文章与本备忘录的主题非常相关，因此我在此处附上该文章的链接。该文章提出了很多重要的观点，尽管我在某一方面不敢苟同。弗格森在上文提到，\"事实上，通胀的未来路径，如同俄乌冲突的未来走向和新冠疫情的传播路径一样，都无法确定。\"我认为准确预测通胀比预测其他两个问题\"更不可能\"实现（如果真可以预测的话），因为准确预测通胀需要对这两个事件以及其他一千个影响因素的预判都是正确的。怎么可能有人把所有这些事情都做对呢?我在此粗略地介绍一下《预测的价值》中提及的预测过程：我想，对于大多数基金管理人来说，该过程是这样的：\"我预测经济会做A。如果A发生，利率应该会呈现B。如果利率为B，股市应该呈现C。在此环境下，表现最好的板块应该是D，而股票E应该上涨最多。\"然后据此构建投资组合，以期在这种情况下实现最好的表现。但无论如何，E的可能性有多大？请记住，E以A、B、C和D为条件。在预测领域中，三分之二的正确率将是了不得的成就。但如果五个预测中，每一个都有67%的可能性是正确的，则结果是，所有五个预测都是正确的并且股票将按预期表现的可能性为13%。基于对A、B、C和D的假设来预测事件E，就是我所说的单情景预测。换言之，如果关于A、B、C或D的假设结果证明是错误的，则E的预测结果就不太可能实现。只有所有潜在的预测都是正确的，E才能得到如预测一致的结果，但这是极罕见的。如果不考虑（一）每个要素的其他可能结果，（二）其他场景出现的可能性，（三）让其中一个假设成为现实的前提条件是什么，以及（四）对E的影响是什么，则任何人都无法进行明智的投资。弗格森的文章提出了一个关于经济建模的有趣问题：关于经济参与者身处何种宏观环境，我们应该作出什么假设？这个问题恰好展示了一个死循环：为了预测经济的整体表现，我们需要对消费者行为等方面做出假设。但要预测消费者行为，难道我们不需要对整体经济环境做出假设吗？在我首份关于疫情的备忘录《无人知晓（二）》（Nobody Knows II）（2020年3月）中，我提到在讨论冠状病毒时，哈佛流行病学家马克·利普希奇 （Marc Lipsitch） 曾说过：（一）事实；（二）类比其他病毒所得出的有根据的推论，以及（三）观点或推测。这是我们处理不确定事件时的标准做法。在经济或市场预测中，我们有大量的历史和许多类似的过去事件可以推断（但新冠疫情都没有）。但即使这些东西被一个构造良好的预测模型用作输入，它们仍不太可能预测未来。它们可能是有用的素材，也可能是垃圾。为了说明这一点，人们经常问我过去所经历的哪个周期与当前最相似。我的回答是，当前的发展与过去的一些周期有短暂的相似之处，但没有绝对的相似之处。在每种情况下，差异都是巨大的，并且超过了相似之处。即使我们可以找到一个相同的前一时期，我们应该在多大程度上依赖于这个单一样本？我想答案是不多。投资者依赖历史参考资料（以及他们据此提出的预测），因为他们担心如果没有这些参考资料，他们会盲目行事。但这并不意味着这些资料是可靠的。不可预测的影响预测创造了未来是可知的海市蜃楼。——彼得·伯恩斯坦如果不首先确定我们的世界是有序的还是随机的，我们就无法考虑预测的合理性。简言之，它是完全可预测的、完全不可预测的，还是介于两者之间？对我来说，结论是介于两者之间，但更倾向于无法预测，以至于大多数预测都无济于事。既然我们的世界在某些时候是可以预测的，而在另一些时候是不可预测的，那么如果我们不能区分什么时候是可预测的，什么时候是不可预测的，预测又有什么用呢？我从阅读弗格森的文章中学到了一个新词：\"确定性的 （deterministic）\"。牛津词典 将其定义为\"由先前的事件或自然规律因果决定的\"。当我们按照规则处理事情时，世界就简单多了……就像费曼的电子一样。但很明显，经济和市场不受自然规律支配——这要归功于人类的参与——之前的事件可能是\"铺垫\"或\"倾向于重复\"，但事件很少会以同样的方式发生两次。因此，我认为构成经济和市场运行的过程不是确定性的，这意味着它们是不可预测的。此外，输入显然是不可靠的。很多都是随机的，例如天气、地震、事故和死亡。其他的则涉及政治和地缘政治问题——一些我们已知，一些还没有浮出水面。在彭博观点 （Bloomberg Opinion） 的文章中，弗格森提到了英国作家G.K.切斯特顿 （G.K.Chesterton G.K. Chesterton）。这让我想起了我在《重新再谈风险》（Risk Revisited Again）（2015年6月）中引用的切斯特顿名言：我们如今这世界真正的问题不是这个世界不理性，也并非这是个理性的世界。最常见的问题是：这个世界几近理性，但却不完全是。生活不是一个矛盾，但却是逻辑学家的陷阱。它看起来比实际上要略微精准和有规律；其精准显而易见，但其不精准的一面却隐藏了起来；其野性也在潜伏以待。（粗体为笔者所加）回到第一页所介绍的午餐会，主持人的开场白大致如下：\"近年来，我们经历了新冠疫情、取得惊人成功的美联储救市政策以及俄乌冲突等事件。这是一个非常富有挑战性的环境，因为所有这些都突如其来。\"我想，对他来说，这意味着与会者应该让自己摆脱对2020年-2022年预测不准确的困扰，继续预测未来，并押注于自己的判断。但我的反应完全不同：\"影响当前环境的事件有很多。而没有人能够预测其中任何一件，这一事实难道不足以让在场的人相信他们应该放弃预测吗?\"再举一个例子，让我们回想一下2016年的秋天。有两件事几乎每个人都深信不疑：（一）希拉里·克林顿将当选总统；（二）若出于某种原因唐纳德·特朗普当选，市场将会崩溃。尽管如此，结果是特朗普赢了，市场飙升。过去六年对经济和市场影响深远，我相信，当时任何对2016年大选持传统观点的预测都不会是正确的。这难道还不足以让人们相信：（一）我们不知道未来会发生什么，（二）我们无法了解市场将如何对所发生的事情做出反应？预测能否带来超额？让我们陷入困境的不是无知，而是看似正确的谬误论断。——马克·吐温正如我在最近的备忘录《关于宏观问题的思考》（Thinking About Macro） 中提到的，在1970年代，我们曾经将经济学家描述为\"从不入市的投资总监。\"换言之，经济学家做出众多预测；实际情况会证明他们是对还是错；然后他们继续做新的预测；但他们并不对预测正确的频率进行追踪（或者，他们并没有发布统计数据）。您能否想象不参考业绩记录就聘请一位基金经理（或换做您是一位基金经理，您能否想象在此情境下受到聘任）？但，经济学家和策略师却不会因为不发布统计数据而丢了工作，原因可能是总有客户愿意为他们的预测买单。您是这些预测结果的消费者吗？预测者和经济学家是否是贵司的员工？或者您是否订阅他们的出版物并邀请他们进行简报，就像我以前的雇主一样？如果是这样，您是否知道每个人预测正确的频率？您有没有找到一种方法来严格确定这些预测当中，哪些是可以依赖的，哪些是要忽略的？是否有方法可以量化这些预测对您投资回报的贡献？我问出了这一连串的问题，因为我尚未看到或听说过任何这方面的研究。令人难以想象的是，全球有关宏观预测是否会带来超额收益的信息十分匮乏，尤其是与需要这类信息的人数相比极不相称。尽管缺乏证明其价值的证据，但宏观预测却仍在继续。许多预测者是股票基金管理团队中的一员，或者在为这些团队提供建议和预测。我们可以肯定的一点是，由于主动管理的业绩不佳，主动管理型股票基金几十年来一直在失去市场份额，被指数型基金和其他被动投资型工具所取代，主动管理型基金现在在美国股票共同基金市场中所占份额少于一半。宏观预测在本质上对投资并无帮助，是否是其中的原因？据我所知，有关这个问题，唯一可以找到量化信息的是所谓的宏观对冲基金的表现。对冲基金研究组织 （HFR） 发布了对冲基金加权综合指数以及一些子策略指数。以下是对冲基金加权综合指数、宏观对冲子策略指数和标普500指数的长期表现。*业绩表现截至2022年7月31日。所显示的对冲基金指数为各基金的加权综合指数。上表中，根据HFR的数据，在研究期间，对冲基金的平均表现远低于标准普尔500指数，而宏观对冲子策略基金的平均表现更是差得多（尤其是在2012年至2017年期间）。鉴于投资者继续将大约4.5万亿美元的资金委托给对冲基金管理人，这些基金必须提供回报以外的一些利益，但目前尚不清楚这会是什么。对于宏观对冲基金来说，似乎尤其如此。为了证实我对于预测的看法，接下来我要举一个很少见的有关自我评估的例子：7月24日《纽约时报》\"周日观点\"专栏刊出一篇长达七页的专题文章，题为\"我错了\"。文章中，八位《纽约时报》\"观点\"专栏作者公开了他们曾做过的错误预测以及给出的有失偏颇的建议。这里最相关的是保罗·克鲁格曼 （Paul Krugman） 所写的一篇题为\" 我看错了通胀 （I Was Wrong About Inflation）\"的自白书。我把其中的一些内容摘录并串连起来：2021年初，经济学家们就\"美国救助计划\"的可能后果展开了激烈的辩论……我当时站在[支持不太担忧通胀影响的一边]。当然，事实证明，这是一个非常糟糕的决定…………历史无法让我们预料到会有如此过热的通胀。所以我的模型出了问题……一种可能的原因是历史具有误导性……此外，为适应疫情及其后果而产生的扰动可能仍在发挥很大角色。当然，俄乌冲突以及中国各大城市的疫情防控措施无疑将这种干扰程度推升至一个全新层面……无论如何，整件事都成了一场谦逊的教训。令人难以置信的是，在2008年金融危机之后，标准经济模型一直运作得相当好，我当时认为在2021年运用同样的模型没有问题。现在回想起来，我当时就该意识到在新冠疫情后所呈现的新世界趋势中，这种推断本身就存在风险。（粗体为笔者所加）我很钦佩克鲁格曼能表现出如此惊人的坦率（虽然我不得不说，我并不记得在2009年到2010年间有很多市场预测乐观到足以描绘随后十年实际情况的程度）。克鲁格曼对他的错误的解释就其本身而言是很好的，但我并未看到他提及在未来放弃建模、推断或预测。这种谦逊甚至可能渗透到世界上最大经济预测机构之一的美联储，那里有400多名经济学博士。以下是经济学家加里·席林 （Gary Shilling） 于8月22日在\"彭博观点 （Bloomberg Opinion）\"中所写的：美联储的前瞻性指引成了一场灾难，导致其本身的公信力面临挑战。主席杰罗姆·鲍威尔 （Jerome Powell） 似乎也持相同看法，外界应该停止揣测美联储在未来不同时间节点有关利率、经济增长和通胀的看法……前瞻性指引的根本问题在于其依赖于数据，而数据本身来自美联储以往那些糟糕的预测记录。美联储一直对2007年-2009年大衰退后的经济复苏过于乐观。2014年9月，政策制定者预测2015年实际GDP增长率为3.40%，但到2015年9月却被迫不断将预期值下调至2.10%。联邦基金利率不是市场决定的利率，而是由美联储设定并管控的，并且无人挑战美联储的权威。此外，联邦公开市场委员会 （FOMC） 成员在预测他们自身将会采取何种行动方面也是出了名的糟糕……2015年，他们对2016年联邦基金利率的平均预测为0.90%，2019年为3.30%。实际数字分别为0.38%和2.38%……可以肯定的是，许多正在发生的事件都造成了市场的不确定性，但美联储的前瞻性指引一直备受追捧且具有重要性。回想一下，今年早些时候，美联储还认为疫情和供应链中断后重启经济的摩擦造成的通胀是暂时性的。直到后来美联储才发现情势不妙而调转方向，提高利率，并发出了进一步大幅加息的信号。美联储的错误预测导致错误的前瞻性指引，加剧了金融市场波动。（粗体为笔者所加）关于这个问题我想最后再提一点，即那些通过宏观观点获利而成名（和致富）的人究竟在哪里？我当然不可能认识投资界的每个人，但在我了解或知道的人里面，我认为只有很少几位堪称非常成功的\"宏观投资者\"。当某件事的实例很少时，正如我母亲曾经说过的那样，\"例外恰恰印证了规律\"。这个例子中的规律就是，宏观预测很少能带来出色的业绩表现。对我来说，成功案例的非比寻常，恰好证明了这一说法是普遍真理。从业者的预测需求相比于揭示未来，预测更能揭示预测者。——沃伦·巴菲特有多少人能够作出大多数时候都有价值的宏观预测？我认为并不多。又有多少投资管理人、经济学家和预测者尝试过？少说也数以千计。这就产生了一个有趣的问题：为什么要预测？如果宏观预测不会随着时间的推移助力投资成功，为什么投资管理行业有这么多从业者信奉预测并对预测结果趋之若鹜？我认为其中典型的原因可能是：这是工作的一部分。投资者向来这样做。我认识的每个人都这样做，尤其是我的竞争对手。我一直都在这样做——我现在不能就此罢手。如果我不这样做，我将无法吸引客户。既然投资涉及部署资本以便从未来事件中受益，那么如果没有对这些事件的看法，怎么能指望做好工作？我们需要预测，即使它们并不完美。今年夏天，我儿子安德鲁推荐我读了一本非常有趣的书：《犯了错误（但错不在我）：为什么我们要为愚蠢的信仰、糟糕的决定和伤害行为找借口》（Mistakes Were Made （but Not by Me）: Why We Justify Foolish Beliefs, Bad Decisions, and Hurtful Acts），该书由心理学家卡罗尔·塔维斯 （Carol Tavris） 和艾略特·阿伦森 （Elliot Aronson） 撰写。书的主题是自我辩护。作者解释说，当人们面对新的证据来质疑他们先前所秉持的立场时，就会出现\"认知失调\"，而当这种情况发生时，潜意识会使他们极力去证明和维护先前的立场。以下是一些精选片段：如果您持有一套指导您实践的信念，并且您了解到其中一些是不正确的，您必须要么承认自己错了并改变您的方法，要么拒绝新的证据。大多数人，当直接面对他们做错的证据时，不会改变他们的观点或行动计划，而是更加顽固地予以辩驳。一旦我们认定某个信念，并证明了它的智慧，要想改变我们的想法显然是一项艰苦的工作。将新证据放入现有框架中进行心理论证以便接纳，比改变框架要容易得多。人们在回应使他们的信念受到质疑的证据时通常采用的机制包括这些（转述作者的话）：不愿意听从不和谐的信息；有选择地记住他们生活的一部分，专注于那些支持自己观点的部分；以及在认知偏见下行事，让人们只看到他们想看到的事物，并为他们已经相信的内容寻求某种确认。我相信，这些都是导致人们持续做出预测并依赖预测的因素。那在这种情况下，会有什么样的表现形式？将宏观预测视为投资不可或缺的一部分；热衷于回忆正确的预测，尤其是那些大胆的、非市场共识的预测；高估预测的正确率；遗忘或淡化错误的预测；不去保留有关预测准确性的记录或未能计算平均成功率；重视奖励给准确预测的丰厚回报；强调\"每个人都这样做\"；以及也许最重要的是，将不成功的预测归咎于被随机事件或外生事件所蒙蔽。（但是，正如我之前所说，这就是问题的关键：如果预测如此轻易地变得不准确，为什么要进行预测？）大多数人——即便是心地善良的老实人——都会采取符合自己利益的立场或行动，有时以牺牲他人或客观真理为代价。他们自己无法察觉这种情景，反而认为自身所做皆为正确的事情；他们也寻求了很多正当理由。正如查理·芒格 （Charlie Munger） 经常引用狄摩西尼的名言，\"没有什么比自欺欺人更容易。因为人总是相信自己所希望的。\"我不认为预测者是骗子或江湖术士。他们之中大多数都是聪明的知识分子，他们认为自己正在做有用的事情。但是，自我利益使他们以某种方式行事，而自我辩护使他们在面对相反的证据时坚持己见。正如摩根·豪斯尔 （Morgan Housel） 在最近的一份时事简报中所说：无法预测过去对我们预测未来的意愿并无影响。确定性是如此宝贵，以至于我们永远不会放弃对它的追求，如果人们诚实地面对未来是多么难以预料，那大多数人都无法在清晨从床上爬起来。（摘自\"大信念\"，联合基金（Collaborative Fund），2022年8月24日）几年前我过生日时，橡树的联合创始人理查德·马森 （Richard Masson） 给了我一件符合他风格的有趣礼物。那次的礼物是《纽约时报》的合订本。我一直希望有机会写一写我最喜欢的1929年10月30日那一期的小标题，道琼斯工业指数刚于两天内下跌了近23%。标题是这样写的，\"银行家表示乐观 （Bankers Optimistic）\"（然而之后的三年内，道琼斯指数大约下跌了85%）。大多数银行家和基金经理似乎先天就对未来持乐观态度。除此之外，这符合他们的最佳利益，因为这有助于他们做更多的生意。但他们的乐观态度肯定造就了他们的预测观点和由此产生的行为。能还是不能？\"我从不考虑未来——因为它马上就要来临了。\"——艾尔伯特·爱因斯坦考虑宏观预测的以下方面：所需假设／输入的数量，须纳入的过程／关系的数量，这些过程固有的不可靠性和不稳定性，以及随机性的作用及发生意外的可能性。对我来说，最重要的是，预测不可能经常正确以达到具备价值的程度。我已经提过很多次了，但为了完整起见，我还是要重申我对宏观预测效用（或者更确切地说，徒劳）的看法：大多数预测由对过去表现的推断组成。由于宏观发展通常不会偏离先前的趋势，因此推断通常是成功的。在这个基础上，大多数预测都是正确的。但是，由于推断通常是由证券价格来预期的，那些基于推断预期的人在推断成立时并不会享受到超额利益。偶尔，经济行为确实会在实质上偏离过去的模式。由于这种偏离出乎大多数投资者的意料，它的出现会影响市场，这意味着对偏离的准确预测将带来丰厚的利润。然而，由于经济不会经常偏离过去的表现，因此能对偏离作出准确预测的很少，并且大多数偏离预测事后被证明是错误的。因此，我们有（一）推断预测，其中大部分是正确的，但不会产生超额利益，以及（二）潜在的有利可图的偏差预测，这些预测很少会是正确的，因此通常也不会产生超额利益。经论证：大多数预测不会增加回报。在本备忘录开头提到的午餐会中，人们被问及对美联储政策等方面的预期，以及这对他们的投资立场有何影响。有一个人回答说：\"我认为美联储仍将高度担心通胀，因此将大幅加息，从而导致经济衰退。所以我选择避险。\"另一位说：\"我预计通胀将在第四季度放缓，而美联储将在明年一月份转向鸽派。开始降息并刺激经济发展。我非常看好2023年。\"我们经常听到这样的说法。但必须认识到，这些人正在运用单因素模型：说话者的预测基于单个变量。说到简化假设：这些预测者隐含地认为，除了美联储的政策之外，一切都是不变的。当需要下三维国际象棋时，他们却还在玩平面跳棋。撇开预测美联储行为的不可能性、通胀对这种行为的影响以及市场对通胀的反应，还有其他重要的考虑因素呢？如果有一千件事情在决定经济和市场的未来方向方面发挥了作用，那么其他999件事情是什么？工资谈判、中期选举、俄乌冲突和石油价格的影响又将如何？事实是，人们在任何时候只能在脑海中记住非常有限的事物。很难将大量的因素纳入考虑，就更难理解大量事物将如何相互作用（相关性始终是真正的思考难题）。即使您以某种方式设法得到正确的经济预测，那只是成功的一半。您仍然需要预测经济活动将如何转化为市场结果。这需要一个完全不同的预测，也涉及无数的变量，其中许多与心理因素有关，因此几乎是不可知的。根据学生沃伦·巴菲特回忆，本·格雷厄姆 （Ben Graham） 曾表示：\"从短期看，市场是一台投票机，但从长远来看，它是一台称重机。\"如何预测投资者的短期选择？一些经济预测人士得出的结论是，美联储和财政部在2020年3月宣布的行动将拯救美国经济并助力经济复苏。但我不知道有谁预测到了在复苏开始之前就已掀起炙手可热的牛市。正如我之前所述，2016年巴菲特与我分享了他对宏观预测的看法。\"要使一条信息有效用，它必须满足两个标准：首先必须重要，其次必须可知。\"当然，宏观前景很重要。如今，投资者似乎把握住了每位预测者的言论、宏观事件以及美联储间歇性紧缩行动的信号。与我从事这个行业的早期不同，现今似乎宏观因素就是一切，而企业发展没那么受关注。但我强烈同意巴菲特的观点，即宏观未来是不可知的，或者至少几乎没人能始终如一地比广大投资者了解更多，而这才是试图获得认知优势并作出卓越投资决策的关键。显然，巴菲特的名字在成功投资者名单中名列前茅，他回避宏观预测，比其他人更注重\"微观\"领域：公司、行业和证券，从而获得成功。我于2001年的一篇名为《阿尔法究竟是什么？》（What’s It All About, Alpha?） 的备忘录中，引入了\"可知论\"流派和\"不可知论\"流派的概念，并于2004年，在《我们和他们》（Us and Them） 一文中对此进行了详细阐述。作为当前备忘录的收尾，我将插入我在后者中所撰写的关于这两种流派的一些内容：这些年来，我遇到的大多数投资者都属于\"可知论\"流派。在1968年-1978年期间，我分析股票时如此，甚至在1978年-1995年期间，我转向非主流投资、但仍在以股票为中心的投资管理公司工作时，情况亦是如此。识别\"可知论\"流派的成员很容易：他们认为，了解经济的未来方向、利率、市场和受广泛关注的主流股票，对投资成功至关重要。他们有信心可以实现这点。他们知道自己能做到这点。他们知道很多人也在努力做到这点，但他们认为要么（一）每个人都可以同时成功，要么（二）只有少数人能做到，但他们就是其中之一。他们愿意根据自己对未来的看法进行投资。他们也很乐意与他人分享自己的观点，尽管正确的预测应价值千金，没有人会免费赠送。他们很少回顾过去，认真复盘他们作为预测者的成绩。\"自信\"是形容该流派成员的关键词。另一方面，对于\"不可知论\"流派而言，这个词，尤其是在看待宏观未来时，应是\"谨慎\"。其信奉者通常认为无法预知未来；也不必预知未来；正确的目标应是在承认不具备这种认知的基础上，尽最大努力做好投资。作为\"可知论\"流派的一员，您可以对未来发表意见（也许还有人特此做笔记）。您可能会受人追捧，并被视为理想的晚宴嘉宾……特别是在股市上涨的时候。如果加入\"不可知论\"流派，结果则更加复杂。您很快就会厌倦对朋友和陌生人表达\"不可知论\"。过不了多久，即使是亲戚也不再问您关于市场走势的看法。您永远不会享受到预测成真时那千分之一的惊喜时刻，也享受不到《华尔街日报》刊载您照片的喜悦。另一方面，您也免于面对预测错误，也免于遭受基于对未来过度确信进行投资而导致的损失。但是，当潜在客户询问您投资前景，而您不得不说\"我不知道\"时，您认为这会是什么感觉？对我来说，哪种流派最好的底线评判标准，出自已故的斯坦福大学行为科学家阿莫斯·特沃斯基 （Amos Tversky） 的名言：\"意识到您可能不知道某些事情是可怕的，但更可怕的是意识到，总的来说，世界是由那些坚信自己确切知道所发生的事情的人来管理的。\"在投资管理业务中，提出宏观预测，应要求进行分享，并以此为据受托为客户投资，这当然是标准做法。基金经理相信预测，尤其是自己的预测，似乎也是惯例。如上所述，不这样做显得格格不入。但他们的信念实事求是吗？我很想听听大家的看法。多年前，一位备受尊敬的卖方经济学家（我在花旗任职时的一位旧识）打电话给我：\"您改变了我的人生，\"他说。\"我早已不再做预测了。取而代之的是，我只是告诉人们今天发生了什么，以及我认为可能会对未来产生的影响。生活从此变得更加美好。\"我能帮您达到同样的幸福状态吗？本文作者：霍华德·马克斯","news_type":1,"symbols_score_info":{".DJI":0.9}},"isVote":1,"tweetType":1,"viewCount":719,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0}],"lives":[]}