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Can we make financial markets boring? Market Regulation, "Easy Money" and the Tragedy of FTX

The goal of this blog is to discuss how the rational choice model discussed in chapter 3 of Advanced Microeconomics (AM), along with some fundamental research on markets with asymmetric information can illustrates the need for continuous regulation of financial markets. Like previous financial crises, the recent fall of FTX, SVB and Signature Bank has resulted in calls for more regulation. This has led to a “I told you so” moment for Elizabeth Warren who said that “These recent bank failures are the direct result of leaders in Washington weakening the financial rules.” What is less discussed are the underlying market imperfections that motivates the need for these rules – this blog discusses the role of information and the lure of “easy money” to provide the basis for the regulation of these markets. It concludes that one goal of regulation is to make markets boring! Namely, create stable, efficient markets with low volatility that can help individuals with limited information make better decisions.


In Capitalism and Freedom, Milton Friedman argued for the general principle of free markets. He recognized the fact that markets are not perfect, but suggested that the combination of rational agents and reputation effects can solve this problem, particularly for large entities where reputation lost would be particularly costly. This point of view is still very influential, and continues to be the basis for the repeated calls for deregulation. It was in this spirit that Greenspan in his July 24, 1998 testimony to congress in 1998 stated that "A far more powerful incentive, however, is the fear of loss of the dealer's good reputation", essentially Friedman’s argument, to argue that the regulation of over the counter derivative securities is unnecessary. It should be pointed out that the reason for the testimony was that Brooksley Born, the chair of the U.S. Commodity Futures Trading Commission, wanted to increase the regulation of the over-the-counter market for securities due to troubling evidence regarding these markets.


This is an interesting example of theory versus evidence, where theory won round 1. However, in the wake of the 2008 financial crises, Greenspan would publicly recognize that he made a mistake, and so evidence won in round 2. The question then is why was bank regulation relaxed under Trump, and why are we are again seeing a call for new regulation?

In order to understand how highly skilled individuals can make these mistakes it is useful to get back to basics.


The modern rational choice model builds upon Savage(1972)'s brilliant work on the foundations of statistics that is reviewed in chapter 3 of AM. Savage viewed rational choice as a two step process. In the first step individuals build a model of the world, including all the future events that might occur, and how these events may affect utility. Savage explicitly recognizes that the world is complicated, and hence he calls this a “small world model”. Given their small world model, the decision maker then assesses the likelihood of the different future events occurring. Finally, they make choices consistent with their model and beliefs.


Early work on decision theory explained decision errors as a consequence of limited computational skill, or so called “bounded rationality”. While bounded rationality is important, it is not the fundamental issue. A good example of this are the recent AI models such as chatGPT. Anyone who has used them will notice that they often make mistakes – including elementary mathematics errors. Thus, it is not a problem of computation power per se, but the fact that the world is very complex, and hence any model of world built by an individual (or chatGPT for that matter) must be incomplete. Rational choice requires that we make choices that are consistent with some model of the world. The whole point of a model is to build a simplified representation of the complex world around us that helps one make better decisions, not necessarily perfect decisions.


An extreme example of this is the January 6, 2021 uprising where many individuals believed that Donald Trump won the election. As we saw from their court testimony, many were surprised by the fact that rather than being praised for their defense of the president, they found themselves facing jail time. These individuals were guided in part by an incorrect model of the world.


This example illustrates the basic empirical fact that individuals can hold very diverse views about the same event, a point that is highly relevant for financial markets. The fundamental goal of financial markets is two fold. First, they allows individuals to reallocate resources over time. If today I earn more than I need, then I can put my savings into a financial asset and delay consumption to a future date. The flip side is that financial markets also allow individuals to fund consumption today that they will repay with future earnings.

The second important goal is to provide funding for projects that have social value. Entrepreneurs with promising ideas receive funding to pursue these projects, while investors realize in advance that a significant fraction of these ideas will fail. It worth emphasizing that getting these projects off the ground is hard work. It make take several years to learn the true value of a project. In contrast modern financial markets are repricing assets continuously in real time, and hence much of the information they are revealing may be unrelated to the true value of the real assets they represent.

The repricing process can only work well if the value of the asset can be accurately measured. If the asset is a large complex corporation, then the true value is very difficult, if not impossible to know.


A goal of a modern financial markets is to aggregate information from many sources to help ensure that the asset price reflects its true value (the discounted present value of future expected earnings). In practice information transmission is imperfect. This turns out to be where the lure of easy money plays a role. Information imperfections imply that the trade price of an asset may be different from its true value. Warren Buffett famously made his fortune by finding under valued firms. That is not easy money – it takes a great deal of work to measure value of a firm, and his efforts not only made him wealthy, but helped ensure that markets reflect the true value of an asset.


In contrast, easy money arises via arbitrage pricing, the foundation of modern finance. The “finance” way to value a particular asset A is to see if one can find a bundle of other assets that have exactly the same risk characteristics, lets call the other asset B. Suppose the market price of A is PA and the market price of the other asset is PB . Notice at this point we need no information about these assets and what underlies their value, only their relative risk and returns. Suppose that the market prices satisfy say PA > PB, and at some future point we expect the price to be equal.


Suppose we sell short 1 unit of asset A at price PA, and use to proceeds to buy QB units of asset B, namely QB=PA/PB >1 since the current value of A is higher. At a future point the prices become equal, say P. At that point we have to buy back asset A (I put aside the details on short selling). The profit one earns per share of stock A sold short is:

Profit = Sales of B - Purchase of A = P x PA/PB - P = P(PA/PB -1) >0.

The point here is that ones does not need to actually know the true value of the assets. If we know they are miss-priced, and will be correctly priced in the future then one can make "easy money". The two challenges to this exercise are the ability to be able to sell as many shares as possible, and the expectation that the market will eventually price the assets correctly.

This was a version of this strategy that made Michael Burry a fortune from the miss-pricing of mortgage backed securities, as described in the book and movie “The Big Short”. It is worthwhile keeping in mind that while the individual investor engaged in arbitrage is earning income, there is little evidence that the investor is “making money” in the sense of increasing social welfare. It is really an open question on the social value of this sort of arbitrage pricing, and hence this is why we might call it “easy money”. One can earn large amount amounts of money even though no new products or services are being supplied to the economy.


Easy money was the starting point for Alameda Research. The principals observed that there were small differences in the price of bitcoin in Japan and America. Buying low in one market and selling the same coin in another market resulted in risk free returns. One of the arguments for free markets is that opportunities for arbitrage pricing do not last long. Hence, when other firms entered into this market, this reduced returns, and motivated Alameda Research to find other arbitrage and investment opportunities. The catch is that once on leaves the world of pure arbitrage the money is less free and can be much riskier!


The crypto market is an example of such a risky market. Since it is new and not well understood prices are extremely volatile. A consequence of market volatility is that if one can anticipate trends correctly, the one can exploit the large price fluctuations to make “easy money”. Though, in order to earn a return, one has to tie in capital and ride the trend (up or down). Notice that if one can correctly predict an asset’s future price increase, then the returns are higher the more one can invest, an incentive that has been the demise of many poorly diversified investors. If one places all one’s bets into a single trend, and borrows to make that investment, then one is in a vary precarious position. A huge payday if the bet works, huge losses if not. If information is moving quickly and efficiently, then markets should adjust quickly to the true value of the asset, reducing the length of time in a losing (or profitable) position. In such cases, assets are quickly marked to market, reducing the size of potential gains and losses.


But, as we saw with the 2008 financial crises, traders such as Burry anticipated the miss-pricing of mortgage backed securities months in advance of the eventual repricing of the assets. How is this possible that this information moved so slowly resulting in a sluggish response to information? Now we get back to Savage’s small world model and the FTX case. The Financial Times produced a very nice video of the FTX case, Garrahan 2023, where they interview a number of crypto investors. One potential FTX investor asked FTX for more financial information regarding the platform. When FTX refused to supply the information, the investor choose not to invest in FTX. He is what we would call a “prudent investor”. The puzzle then was why FTX was so successful at fund raising given that it was unwilling to provide detailed financial information?


A clue comes from another investor mentioned in the video, who for lack of a better term, is an “animal spirits investor”. Here we are referring to Keynes’ observation that emotion and other “non-rational” factors affect decision making in markets. That investor, like most successful investors, had a large diversified portfolio. Their investment strategy was not to waste time on expensive market research, but to look for exciting projects that were getting “buzz”, and hence the prices were being driven by the psychological factors that the “animal spirits investor” could exploit. Some of his investments would necessarily fail, but with a large portfolio the total risk is contained. The fact that the “animal spirits investor” spends less time in research means that then can execute more trades. As long as “buzz” is positively correlated with returns, they can make money with such a strategy, even though their per project returns might be lower than the returns for the “prudent investor”.


What is interesting is that the model of the world that the “animal spirits investor” has is quite different from the “prudent investor”. For each project the “animal spirits investor” has a general idea of the expected returns and risk gleaned from the market and where they think the “smart money” is investing. In other words, they search out people whose views they believe are correlated with market trends. As Keynes pointed out many years ago, the views of individuals can lead to short run trends or waves in the market. Like a surfer, if the “animal spirits investor” can consistently catch the trends, then they can make “easy money” without investing time and energy to carefully evaluating the true value of an asset.


This might seems irrational, however it leaves out the fact that assessing the market value of an entity is very difficult and expensive. This in turn has important implications for price setting, a fact that was well know more that 30 years ago. Green 1977, Grossman and Stiglitz 1980, and Hellwig 1980 have shown that when information is costly, then there is no well behaved competitive asset market equilibrium. It is quite easy to see why. If investors are prudent, the market prices are equal to the true expected value of an asset. But these individuals are spending large sums on research. In such an environment a hedge fund could enter, do no research and hence earn an above average rate of return by not doing the research.


Conversely, if there are no informed investors, then this leaves an opening for informed traders to enter the market. The consequence is that only possible outcome is to have both "prudent" and "animal spirits" investors in the market. This in turn can lead to market instability, exactly what we observe in real world financial markets.


Regulators understand this problem and thus there are many rules that try to ensure that information is revealed to market quickly. The purpose of regulation is to reduce the return to “easy money” and stabilize markets by forcing market participants to reveal information in a timely fashion. Since providing high quality and timely information is costly, then a prediction of effective regulation is that nobody wants to comply!


The fact that individuals are self-interested and they build incomplete models of the world implies that they will call for deregulation of well functioning regulated markets. When these lobbying efforts are successful, then the quality of information in markets falls, resulting in market failure. Hence, the combination of self-interest and the fact that many market participants have incomplete models of the world leads to cycles of regulation and de-regulation.


Economics research has also shown that many of the alternatives to market regulation do not to work. For example, one could argue that since markets are incomplete, then the solution is to have more markets, such as markets for complex derivative securities. However, the Nobel Laureate, Oliver Hart 1975 showed in his PhD thesis that adding markets can in some cases makes things work.


Many individual investors are self aware and know that they have incomplete knowledge, or they may choose to have incomplete knowledge, as in the case of the "animal spirits" investor. The problem is if they rely upon the advice of person who in turn relied on the advice of others, one can have a chain of recommendations that eventually has no useful information. There is a large literature on learning from others, beginning with the work Banerjee 1992 and Bikhchandani, Hirshleifer, and Welch 1992, who show the fragility of reputation effects based upon learning from other. This is still a very active area of research (see for example the recent work by Liang and Mu 2020 who document other cases where learning truth can be slow).


These models highlight the fact that for most of us when we know that are understanding is incomplete we ask for advice. The question is how to we determine who is knowledgeable? For example, a climate scientist may be very knowledgeable about physical systems, but she is not an expert in finance. What are they to do? One could try to learn more about how markets works. The book by John Cassidy 2009 has a great discussion of the economics of the financial crises that is accessible to a wider public (see in particular Chapter 21: A Matter of Incentives). Though it has won several accolades, it is not at the top of people’s reading lists – it ranks 622,635 on Amazon books.


Another book on market failure, “The Myth of the Rational Market” by Justin Fox 2011, was written shortly after the Cassidy book, and it was quite a bit more successful – today it ranks 158,760 on the the Amazon best seller list, and it is in the top 100 in several categories. Paul Krugman 2009 lauded the book in a review titled “Schools for Scoundrels” to highlight how the book focused on the individuals whose theories are linked to the financial crises of 2008. The relative success of these two books very consistent with the advice I have got from book editors as I think about writing a new book. Human interest, scoundrels and disaster sells, while discussion of complex ideas does not!


Before discussing this point it is worth presenting Fox’s conclusion to his book:

The academic approach to finance that rose to prominence - and in some cases dominance – in the last few decades of the 20th century was all about formulating rules and laws everybody could follow. As supplements to individual judgment, and as checks on it, many of these new rules and tools of finance have turned out to be quite helpful. But as substitutes they bring disaster. They replace diversity and thought with mindless conformism. And while mindless conformism was characteristics of financial bubbles and panics long before there were finance professors, fostering even more of it has been the gravest sin of modern finance.

The first problem with the conclusion is the way the term finance is being used. What he appears to means are the asset pricing models in finance, which is quite a distinct field from the economics of financial markets. As pointed out above, economic theory predicts that there is an incentive for traders in financial markets to resist regulation since it has the effect of increasing compliance costs, while reducing the returns to arbitrage. It is Fox's lack of diversity in discussing economics that leads to the conclusion, not a lack of diversity in the views of economists.


Second, the book highlights the simple fact that as humans living in a complex world we have to rely upon the opinions of others, and in general humans find human interest stories more engaging than complex math. Thus, Fox's book focuses more on individuals than the substance, which in turn helps explain its success. This point also explains another market failure that was evident in the FTX bankruptcy, namely the investment strategies of "consumer investors". These were the small, undiversified investors who were moved by advertising with celebrity endorsements to invest in crypto currency.


In some cases these celebrity endorsements may lead to some liability for those who sent messages. Again, such liability arises for the same reasons that are given above for regulation of financial markets. Information is expensive, and hence individuals necessarily rely on others they trust for advice. Our laws and regulations respond to these needs by trying to increases the quality of information provided in the markets by imposing liability on those sending low quality information.

In short, the goal of free markets is to provide a venue where creative individuals can provide new products that enhance social welfare. Financial markets help fund these innovations. However, markets are very complex information machines that only work with well with high quality regulation. It is a myth that there is such a thing as a “free market”. When there are too many uninformed individuals in a market, this can create volatility and a temptation to earn “easy money”. The tragedy of FTX is that the lure of “easy money” led to many very highly skilled individuals spending time on an activity that produced little social value. Today we face many challenges, including climate change, the destabilizing effects of inequality and finding betters way to reduce conflict between people and nations that deserve the attention of highly skill individuals.


There are many many dedicated individuals in both government and the private sector who are working hard to effectively regulate financial markets. Their goal, simply put, is to make financial markets boring - reduce the opportunities for easy money, so that creative individuals spend more time and energy addressing the many challenges we face as a society.


References

Banerjee, Abhijit V. 1992. “A Simple Model of Herd Behavior.” The Quarterly Journal of Economics 107 (3): 797–817. https://doi.org/10.2307/2118364.


Bikhchandani, Sushil, David Hirshleifer, and Ivo Welch. 1992. “A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades.” Journal of Political Economy 100 (5): 992–1026.


Cassidy, John. 2009. How Markets Fail. New York, NY: Farrar, Straus and Giroux.

Fox, Justin. 2011. The Myth of the Rational Market. HarperCollins Publishers Inc. https://www.harpercollins.com/products/the-myth-of-the-rational-market-justin-fox.

Garrahan, Daniel. 2023. “The Legend of Sam.” Financial Times. March 5, 2023. https://www.ft.com/video/f7a7fad1-f3ed-41ee-94a7-e1311989aa7e.

Green, Jerry. 1977. “The Non-Existence of Informational Equilibria.” The Review of Economic Studies 44 (3): 451–63. https://doi.org/10.2307/2296901.

Grossman, Sanford J., and Joseph E. Stiglitz. 1980. “On the Impossibility of Informationally Efficient Markets.” The American Economic Review 70 (3): 393–408.

Hart, Oliver. 1975. “On the Optimality of Equilibrium When the Market Structure Is Incomplete.” Journal of Economic Theory 11 (3): 418–43.

Hellwig, Martin F. 1980. “On the Aggregation of Information in Competitive Markets.” Journal of Economic Theory 22 (3): 477–98. https://doi.org/10.1016/0022-0531(80)90056-3.

Krugman, Paul. 2009. “School for Scoundrels.” The New York Times, August 6, 2009, sec. Books. https://www.nytimes.com/2009/08/09/books/review/Krugman-t.html.

Liang, Annie, and Xiaosheng Mu. 2020. “Complementary Information and Learning Traps*.” The Quarterly Journal of Economics 135 (1): 389–448. https://doi.org/10.1093/qje/qjz033.

Savage, Leonard J. 1972. The Foundations of Statistics. New York, N.Y.: Dover Publications.


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