Abstract: This paper extends subjective expectations theory to form a new approach called the discovering markets hypothesis (DMH). Market participants form expectations on the basis of subjective knowledge and communicate with each other through narratives to improve their understanding of factual information before acting in markets. Thus, market prices are shaped by the subjective interpretation of emerging facts and shared narratives. To understand how new narratives replace existing ones, we refer to the theory of scientific revolutions. Winning narratives shape market prices until their victory is confirmed by the facts or they are discredited by facts and replaced by new narratives.
JEL Classification: B53, D84, E71
Marius Kleinheyer ([email protected]) is a research analyst at the Flossbach von Storch Research Institute in Köln, Germany and PhD candidate at the University Rey Juan Carlos, Madrid. Thomas Mayer ([email protected]) is the founding director of the Flossbach von Storch Research Institute and honorary professor at the Universität Witten-Herdecke.
Prices fluctuate, and especially in financial markets, where they are heavily influenced by expectations of the future. Some economists have explained price fluctuations with the myopia of market participants. For instance, bid and ask prices are based on prices observed in the past, and when supply and demand do not match, prices are adjusted. Other economists have replaced myopia with perfect foresight in their models. According to them, all market participants always have all the necessary information to agree on a price equating supply to demand so that prices change only when they receive new information. However, actual price behavior is neither consistent with complete myopia nor perfect foresight among market participants. Sometimes, prices move as if market participants were myopic, sometimes as if they were forward looking. This has prompted another theory, according to which price fluctuations reflect market participants’ collective oscillation between rational and irrational behavior.
This paper argues that there is a better way to explain price fluctuations in financial markets. Market participants form their price expectations on the basis of information that they collect and interpret with their individual skills and knowledge of economic relations. They act in the market or communicate with others through narratives to improve their understanding of their factual information before acting. Thus, market prices are shaped by the subjective interpretation of emerging facts and shared narratives. The resulting price movements in return influence narratives and the subjective interpretation of facts.
First, the theories of adaptive and rational expectations and the concept of adaptive markets will be discussed. These theories will then be connected to the theory of subjective expectations and an extension to the latter suggested, the discovering markets hypothesis (DMH). Empirical evidence is presented to support this approach, and finally, its utility in making predictions.
OBJECTIVE THEORIES OF EXPECTATIONS
Economist John Hicks took issue with the idea put forward by Léon Walras that transactions take place at prices where demand is equal to supply. Since traders generally could not know what would be supplied and demanded at certain prices, they could only guess. Hence, Hicks (1939) argued, transactions would generally occur at prices which did not equate supply and demand. Following Hicks, we could describe the market as a mechanism that matches expectations and prices, but not necessarily potential supply and demand.
John Maynard Keynes raised the question of how expectations about the future are formed. Where they could, people would rationally calculate subjective probabilities for different outcomes and choose the most likely. But they would also often fall back on whim, sentiment, or chance. The latter was especially the case in capital markets, where participants were driven by “animal spirits.” There, it was often necessary to forecast “what average opinion expects average opinion to be” (Keynes 1936). Keynes left the formalization of his macroeconomic expectations theory to his disciples, which often led to a mechanistic reduction of his arguments. An example of this is the theory of adaptive expectations.
In the adaptive expectations model an expected market price depends on the expected price of the previous period and an “error correction” term that is given as a fraction of the difference between the expected and the actual price in the previous period. This model is not only intuitively appealing but benefits also from the advantage that expected prices can be expressed as a weighted average of past prices. Given its user friendliness the adaptive expectations theory has been built into many macroeconomic models and has been used by many econometricians. However, even its most enthusiastic users have had to admit that it describes the formation of expectations in a very mechanical way that falls far short of Keynes’s more sophisticated view (see also Gertchev 2007).
In the early 1960s, the US economist John Muth contradicted the theory of adaptive expectations. He argued that the expectations of economic agents were nothing more than predictions, which could be made with the appropriate economic theory (Muth 1961). In the formation of rational expectations only the future counted, which would be fathomed with the help of economics. If people used all available information efficiently and knew how the economy really worked, then realized prices would differ from expected prices only as a result of random influences. And if the expected value of random influences were zero, market prices would over the longer run equilibrate supply and demand.
Muth’s theory, originally intended to explain price formation in specific markets, was incorporated into an economy-wide, dynamic general equilibrium model by Robert Lucas. According to Lucas, economic agents form their expectations of the future with full knowledge of all economic relations and using all available information. Based on these expectations they maximize their utility over their lifetime. With his work Lucas not only solved Hicks’s problem of imperfect information but also challenged established Keynesian macroeconomics. He argued that robust economic predictions could be made only with models founded in microeconomic theory because macroeconomic relations observed in the past were unstable over time.1 Economic agents would change their behavior in response to economic policy. For instance, the famous relationship between unemployment and inflation proposed by the Phillips Curve would go up in smoke once people realized that the gains in purchasing power afforded by higher nominal wages were subsequently eroded by higher inflation.
Eugene Fama applied the concept of rational expectations to financial markets and hypothesized that financial prices contained all available information. At a minimum, it should not be possible to use past prices to predict future prices, and at best there would be no difference between market prices and fair prices of financial assets (Fama 1970). Thus, if markets are “weakly efficient,” future prices cannot be predicted on the basis of past prices. Already this rather restrained statement contradicts the theory of adaptive expectations, which assumes that past prices contain valuable information for future prices. Markets are “semi-strongly efficient” when prices reflect all publicly available information. In this case, forecasting on the basis of past price movements as well as by considering new publicly available information is impossible. Finally, Fama classifies markets as “strongly efficient” when prices not only reflect all relevant public information but also proprietary insider knowledge. In this case, market prices and fair values of assets would be identical.
Rational expectations theory and the efficient markets hypothesis (EMH) were not only very successful academically—Robert Lucas and Eugene Fama were both awarded Nobel Memorial Prizes for their work—but also highly influential in business and politics. EMH provided the theoretical foundation for “passive investing” through index funds. If no single fund manager could reliably beat the market, why pay fees for active portfolio management? Greater returns could surely be obtained by investing in the entire market at lower costs. And EMH also had a strong influence on government policies. If the market always knew best, why let government bureaucrats regulate it? “Light” regulation was in this case surely better than heavy-handed intervention.
However, Ricardo Campos Dias de Sousa and David Howden (2015), among others, have shown that EMH suffers from logical contradictions. If, as it stipulates, all market participants have all relevant information and interpret it in the same way, all would agree on a price and there would be no incentive to sell or buy. On the other hand, if only a sufficiently critical mass of market participants interpreted relevant information in the same way, transactions could take place, but the price allowing this transaction would be seen as efficient by one and inefficient by the other group. Thus, “efficient prices for one group requires inefficient prices in the eyes of the other” (Campos Dias de Sousa and Howden 2015, 396).
Rational expectations theory and EMH suffered their first practical setback in the early 2000s, when the “technology stock bubble” burst. Apparently market participants were not just cool-headed homines oeconomici but could get carried away by emotions. The experience gave a big boost to behavioral economics and finance. Until that point, behavioral economics had largely been an experimental science confined to the laboratories of a few universities—its key protagonists, Daniel Kahnemann and Amos Tversky, were Israeli psychologists. US economist Robert Shiller (2000) applied behavioral economics to finance, publishing a book in which he diagnosed the wild rally of technology stocks towards the end of the 1990s as a bubble just as it was peaking. Not least because of the excellent timing of the release of his book, a serious challenge to the EMH had emerged in science and financial business.
Rational expectations and EMH suffered another setback with the Great Financial Crisis of 2007–08. The systematic mispricing of risk, which became apparent when the credit bubble burst, was inconsistent with the idea that people would base their financial decisions on all available information and with a full knowledge of the true “economic model.” Obviously people in the credit markets had based their actions on inadequate information and a false economic model that indicated risk reduction through asset pooling when risks in fact accumulated as a growing number of people acted on this model.
Despite its obvious failure, EMH has remained the predominant theory of market behavior in academics and large parts of the business world simply because there has been no other theory in mainstream economics to displace it.2 In 2017, however, the US financial economist Andrew Lo came up with another challenger to EMH. Conscious of the difficulty of dethroning a theory taught widely at universities and perhaps with the ambition to follow in the footsteps of Nobel Prize winners Fama and Shiller, he named his theory the adaptive market hypothesis (AMH) (Lo 2017).
Lo’s intention was not to scrap EMH entirely, but to restrict its validity to times of continuous market development. During those times people act rationally, based on a wide knowledge of facts and a good understanding of the valid economic model. But when markets are disrupted for whatever reason, people turn from rational analysis to instinctive behavior. They join others in either rushing into markets for fear of missing out or fleeing them for fear of losing their fortunes. Lo (2017, 188) summarizes his theory in five key principles:
1. We are neither always rational nor irrational, but we are biological entities whose features and behaviors are shaped by the forces of evolution.
2. We display behavioral biases and make apparently suboptimal decisions, but we can learn from past experience and revise our heuristics in response to negative feedback.
3. We have the capacity for abstract thinking, specifically forward-looking what-if analysis; predictions about the future based on past experience; and preparations for changes in our environment. This is evolution at the speed of thought, which is different from but related to biological evolution.
4. Financial market dynamics are driven by our interactions as we behave, learn, and adapt to each other, and to social, cultural, political, economic, and natural environments in which we live.
5. Survival is the ultimate force driving competition, innovation, and adaptation.
Thus, during normal market conditions reward increases with risk. But at times of negative disruption people may shun risks irrespectively of the associated reward. The Capital Asset Pricing Model may work in normal times but fail in other market environments. Similarly, portfolio optimization according to Markowitz may work in good times but fail in bad times. When there is contagion among different markets, asset divers
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