The Market Mind Hypothesis, page 61
I will apply the dualist distinction not just at the macro level between the real and financial economy. I will also apply it between physical and mental aspects of investing. Trading (or, more specifically, entering/exiting trades) can be seen as investment’s sensorimotor actions which integrate its sensory and motor aspects (in addition to cognitive ones). Setting up a complex of trades is thus comparable to Clark and Chalmers’ Tetris game (1998). In other words, structuring your financial portfolio within the market requires coordination via your mind~body portfolio. The key mental aspects of investing (e.g. research) culminate in the decision to allocate. Qualitative or discretionary investing employs humans to allocate, making it an insourcing process. Quantitative or mechanical investing,70 on the other hand, employs computers to allocate, making it an outsourcing process. That is, it is focussed on determining how to outsource the allocation decision, which mostly involves coding/selecting algorithms. Such algorithmic allocation basically means that human decision-making no longer concerns asset classes but rather ‘programming classes’. The hope is that it also diminishes the influence of emotions in the process.
The key physical aspects of investing culminate in the execution of the allocation decision via a trade of which there are four basic types: long, short, sell, or cover. Again, if the trade is made by a human (e.g. by phone) it is called discretionary, if made by a computer (e.g. electronically) it is called mechanical (or systematic). Specifically, nowadays execution is increasingly outsourced to algorithms. The term algorithmic (e.g. high-frequency) ‘trading’ can thus be somewhat of a misnomer because it usually only refers to execution (e.g. matching buy with sell orders) not decision-making. Whether algorithmic trading is beneficial remains a big question.
Mechanical investing is investment’s practical implementation of mechanical economics. The most prominent manifestation of the discretionary-mechanical distinction is between, respectively, active and passive investing. Although hotly debated active-passive overshadows the key issue, namely the consequences of the relative growth in mechanical investing in terms of informational efficiency and general awareness of the market mind. Portfolioism suggests that the insourcing market is different from the outsourcing one for a mind~body. One area where this is prevalent is in intentionality, i.e. that what occupies the mind. Whereas the insourcing market involves exchanges based on direct attention on assets, the outsourcing market only involves exchanges based on indirect attention on assets, namely via direct attention on algorithms. This and related issues have wider implications for the market mind which I will discuss.
B2. Economics
Economic science, or economics for short, studies the supply and demand of goods and services, including their production, trade, and consumption by economic agents. Their metaphysical status determines how goods and services are classified. Most goods (like a car) are considered “search goods” because they can be evaluated before purchase via description, observation and touch. On the other hand, most services (like a haircut) are considered “experience goods” because they can only be evaluated directly and in real-time by experience, following the purchase.
Consumers, producers, but also investors and savers, are economic agents. Their individual embodied mentality, e.g. expressed as behaviour, forms the so-called microfoundations of economics. The macrofoundations of economics, on the other hand, consist mostly of complementary market forces (viewed for our purposes in a 4E cognitive setting) that emerge synergistically from their exchanges (which is aimed at discovery, especially of value; see also main text). As a reminder, they include competition~cooperation, consumption~production, risk~reward, saving~spending, supply~demand, and input~output. Among others, such emergence implies that the collective behaviour of agents is coordinated by these forces, e.g. via Smith’s invisible hand.
Agents, their coalitions, and their activities form economies, including markets. Economies exist at the macro level (e.g. regions and countries) and the micro level (e.g. corporations and individuals). An implied distinction is between market economies (in capitalist societies) and centrally planned economies (in socialist societies). A key criterion to determine the type of economy is thus the extent to which the government or state is a consumer and producer; if most goods/services are produced by state-owned companies the economy is planned. In this book I will primarily focus on market economies and criticise (the growing influence of) central planning.
Although market dynamics is a complex reflexive process, it basically involves the discovery of prices by freely exchanging agents when their demand and supply meet via competition and cooperation. It leads to the allocation of resources which is assumed to be more efficient than if it had occurred in a planned economy. Scarcity is a key determining factor. Scarcity is the condition whereby demand for a resource exceeds its availability. Scarcity of money is a special case because everybody wants more of it.
One distinction within economic science in terms of theory is between standard economics and financial economics, also known as (modern) finance. Again, these are nested disciplines, with shared assumptions, particularly the Rational Expectations Hypothesis (REH).71 Due to space constraints I will only summarise the REH here and refer to the literature for more details (but see also chapters 2 and 4). First, the REH states that agents have rational expectations, e.g. about prices, meaning that their forecasts are unbiased and based on all available information.72 The REH assumes that agents learn from their mistakes and have perfect (or complete) knowledge including about economic theory and the most recent (e.g. government) policies. Interpreting this in terms of the EMH, for example, investors know the true probability distribution with which to judge news and events that impact their portfolios. The result is that actual and expected prices converge to an optimal equilibrium.
Second, the REH states that all agents are assumed to believe, know and use the same REH-model and thus to consider other agents to be rational. From a game theory perspective, Hofstadter calls the strong version of this claim superrationality: “Superrational thinkers, by recursive definition, include in their calculations the fact that they are in a group of superrational thinkers” (Hofstadter, 1983, p. 748). It means that all agents are roughly identical and can be aggregated via the so-called ‘representative agent’. The economist (or econometrician) can use this REH-model to simulate an economy with these assumed characteristics. It leads to model singularity. In the words of Thomas Sargent, one of the REH’s architects, “the fact is you simply cannot talk about [model] differences within the typical rational expectations model. There is a communism of models. All agents within the model, the econometricians [who use it], and God share the same model” (Evans and Honkapohja, 255, p. 566). In turn, this leads to one of the criticisms aimed at the REH, namely circular reasoning: by assuming that actual and expected prices adjust rapidly to their equilibrium values one allows (within the model) an economy to operate at or near optimality.
Finally, the REH implies that the mental of rational expectations can influence the physical of the real economy, as a self-fulfilling prophecy. That is, everybody is expected to eventually behave according to the model thereby realising the latter in the real world. However, it does not make this metaphysically explicit. In chapters 2 and 4 I address this in more detail.
There are numerous studies which question the assumptions underlying the REH, including from behavioural economics which I’ll discuss shortly. For example, they relate to its curious interpretation of rationality as ‘consistency’ in making choices. To make this rationality clear, Savage’s axioms are often invoked, like the transitivity “if A is preferred to B and B to C, then A will be preferred to C”. Following these axioms is considered rational behaviour if it is done consistently.73 Over the years Ellsberg and others have criticised this. For example, regret theory (which complements prospect theory) suggests that regret impacts preferences to the point of violating such transitivity, e.g. via preference reversals. For an early critique on the limitations of rationality, see Haltiwanger and Waldman (1985).
There are two main criticisms of the REH that are relevant here. First, due to its mechanical worldview it neither considers the individual nor the economy to be a complex adaptive mind~body. Specifically, it ignores their endogenous change, in particular change brought about by their ability to generate novelty. At the individual level this includes the cognitive ability to fundamentally change one’s mind in terms of the underlying methods, processes and strategies that lead to beliefs and forecasts. Within the economic system it includes fundamental change in terms of radical innovation, e.g. creative destruction, in the methods, processes and strategies that generate the earnings of companies. The second, and related criticism is the REH’s belief that agents’ subjective probability distribution associated with their beliefs (e.g. in the form of forecasts) is the objective or true distribution.
Cognitive science, via 4E cognition, puts the REH under more pressure. It points out that preferences are conditional on 4E-contexts because these influence cognition. For example, if your cognition becomes embedded in a particular culture or social institution, habits and rules influence it. In relation to knowledge acquisition, Gray and Fu (2004) performed a series of neat experiments to test how subjects selected strategies to retrieve information. These strategies involved using one’s head (i.e. accessing internal data via memory) or using the world (i.e. accessing external data via a screen) as resource. They found that subjects selected information retrieval strategies that required the least time effort. As the title of their paper indicates, this makes the “case of ignoring perfect knowledge in-the-world for imperfect knowledge in-the-head”. I will extend this by making the distinction between collective internal knowledge (based on market data) and collective external knowledge (based on macroeconomic data).
The result of all this is that the assumptions by the REH, in particular regarding rationality, invite and facilitate its wider mechanical worldview. It takes an extreme intentional stance while adopting a strong version of so-called practical rationality: it is focussed on the results from acting on underlying beliefs, rather than on the truth of those beliefs (see Chapter 4). It means that although the REH is largely an ‘as-if’ principle, it has real impact. In particular, the REH promotes fully predetermined (e.g. DSGE) models whereby rational expectations are made consistent with these models by assuming that the model’s agents know the model, accept its predictions, and behave accordingly. This highlights, again, why the seemingly opposites of the new classical and Keynesian disciplines can be combined into mainstream economic engineering because of their shared mechanical conviction. Namely, the theories and resulting policies and other practices of the former are rules-based and thus can be easily implemented via computers and other machines. This is attractive to the latter because its theory and practices are interference based and can be executed via central control. This issue will regularly return throughout this book.
B3. Finance
Finance, or formally modern finance, studies the financial economy made up of financial markets. It thus deals with a subset of issues that belong more generally to economics.
Specifically, finance is the investment theory within mechanical economics. It includes Modern Portfolio Theory (MPT) developed by Harry Markowitz, and the Capital Asset Pricing Model (CAPM) co-developed by William Sharpe. A related model is based on the Arbitrage Pricing Theory developed by Stephen Ross. Finance is built around the academic ‘ivory tower’ of the Efficient Market Hypothesis (EMH). The latter was developed in the 1960s, primarily by Paul Samuelson and Eugene Fama (e.g. 1970) based on research by early explorers like Louis Bachelier, Alfred Cowles, Holbrook Working, and Maurice Kendall.74 The EMH argues that markets are efficient. It further assumes investors behave rationally and advocates market equilibrium, an idealised state where demand equals supply.
Market efficiency primarily means the extent to which information75 is captured—as in fully reflected—in prices. The EMH states that markets are efficient in varying degrees, depending on the type of information that is assumed to be reflected. Following Fama (1970) we can make the distinctions regarding efficiency shown in Table B.1.
Table B.1:Efficiency Variations.
Strength of efficiency Type of information (reflected in prices)
Weak
Semi strong
Strong Historic prices
Public information, historic prices
Private information, public information, historic prices
In its strongest form, the EMH states that the prices of stocks, bonds, and other securities fully reflect all available information at any point in time, including economic, political, or other relevant events. In investment parlance, randomly arriving news is almost instantly ‘discounted’ in prices, turning their fluctuations into martingales. This is the result of rational, profit-maximising investors searching for data that informs their knowledge, expectations, and decisions. Each investor thereby acts as the representative agent and responds to the locally felt shocks of news. It culminates in trading the aforementioned securities which moves prices until the so-called risk-adjusted expected returns are equal for all securities, leading to the market’s equilibrium. Any additional price changes are thus due to new information such as unexpected events.
According to the EMH, price discovery under these circumstances is limited to exploiting the discrepancy between the market value, or price, and the fundamental value of a security by alert and rational investors. This fundamental value is also called intrinsic or true value. It is a misnomer in that it is based on (e.g. cash flow) expectations rather than on any intrinsic property (at least for most securities). Any discrepancy between price and fundamental value is only a short-term inefficiency because its discovery will trigger trades leading to an almost immediate re-pricing which will return the price to its fundamental value. In short, the price is almost instantly reflecting the fundamental value. This basically means that investors should not expect to earn abnormal (i.e. excess) returns (other than by chance). Translated: consistently ‘beating the market’ is nearly impossible, there are no ‘free lunches’, and any edge will quickly be found out and arbitraged away. Finally, apart from its information efficiency in broader implied terms the market is also assumed to be efficient in its role of allocating capital to the real economy. I call this funding efficiency.
Although the EMH has lost much of its stature, particularly since 2008, it remains a cornerstone of mechanical economics. It is formally difficult to (dis)prove market efficiency, for example due to the requirement to test market behaviour via asset price models (the so-called joint hypothesis test). Still, an overall critical view is justified. What follows is a trailer to more extensive criticism in Subchapter 2.3.
As per our metaphysical stance we identify two aspects to efficiency: informational (≈ mental) efficiency and funding (≈ physical) efficiency. In predictive processing terms, the former concerns perception while the latter concerns action. Both depend on correct allocation of, respectively, attention and capital. Informational efficiency refers to the degree (i.e. strong, semi-strong, or weak) to which prices reflect all available, relevant information. For our purposes, I make a distinction between internal information (from market data) and external information (from macroeconomic data). This is motivated by the unrealistic assumption, generally within the REH settings, that instability is only due to exogenous factors. Informational efficiency can actually suffer when attention (and its related awareness) is allocated to overweigh internal information—for example via momentum—which then dominates market behaviour. I will explain this shortly. Funding efficiency refers to the degree financial instruments, particularly credit securities, meet the funding needs of physical resources in the real economy. Funding efficiency relates to the completeness of markets (i.e. the Arrow-Debreu framework)76 in two ways. First, in case of incomplete markets, there are no financial instruments available to fund certain physical resources and allow investors exposure to these. Second, in case of real resource constraints,77 financial instruments may overfund physical resources. In the latter case, funding efficiency can suffer when, for example, credit allocation overweighs constrained supply of urban land. Ideally, mental and physical allocations mutually correct and complement to achieve overall market efficiency. In other words, a large part of the market’s efficiency depends on its mental and physical handling of the ‘reality’ of the real economy. That involves correct perception and action by Mr Market. Erroneous perceptions, for example in the form of extreme market moods like exuberance, do not properly match that reality.
More generally, there are a number of reasons or causes for market inefficiency. I will first mention three traditional examples. Depending on the exact form, they can affect funding and/or informational efficiency:
Externalities. A positive (negative) externality is the benefit (cost) of an economic activity to third parties who do not participate in that activity. An externality is not accounted for in prices. Climate change is a good example of a negative externality.78 I also discuss externalities in the Economic Note in the Introduction.
Mono-/Oligopolies. These exist when companies have such a large market share that they dominate that market. It turns market capitalism into corporate capitalism.79 This type of concentration has grown over the years and occurs in many industries. I call it “The Big 3 in Everything” who are TBTC (Too Big To Care). I will discuss this in Subchapter 2.3.
