Social finance as cultural evolution, transmission bias, and market dynamics

Social transmission helps explain how private signals are distributed across investors; in classical finance this distribution is often taken as given. Furthermore, social transmission of financial traits, including financial folk models, shapes the heuristics and biases taken as given in behavioral finance (as reviewed in ref. 8) and shapes how these traits vary across agents and over time.

For example, in behavioral finance investor belief in continuation or reversal of price trends is taken as given, as in the literature on positive feedback trading (9). The evolutionary perspective we propose seeks to explain how investors come to possess such beliefs or decision rules. In this approach, investors acquire trend-chasing or contrarian traits from others in a process of cultural transmission. The folk model that return trends tend to continue competes for investor attention and belief with the “buy on the dips” folk model that returns tend to reverse. Similarly, empirical behavioral finance research often takes investor sentiment to be an exogenous driver of behavior (10, 11). Social finance regards shifts in sentiment as an endogenous outcome of microevolutionary cultural processes.

Behavioral finance considers the special case of social finance in which psychological biases are effectively innate (for analytical purposes). Similarly, classical finance considers the special case in which investor traits such as discount rates and risk tolerance are fixed. In social finance, financial traits spread from person to person, so that the distribution of these traits in the population evolves.

Econophysics and evolutionary finance models that assume mechanistic trading rules do not endogenize the belief formation or preferences of classical finance. Other models allow only for given or mechanistically generated beliefs. Social finance allows for both social transmission of beliefs and the possibility of active thought processes on the part of decision makers.*

Previous literature on evolutionary finance often focuses on the competition between trend chasing and fundamental trading behaviors or optimism versus pessimistic beliefs (see reviews in refs. 12 and 13). Building upon these advances, social finance widens application of the evolutionary perspective to the rich diversity of actual financial traits and folk economic models. Examples include belief in the value versus growth investment philosophies or belief in the use of the payback criterion for managerial project decisions.

Most research mentioned in point 2 studies shifts in the distribution of wealth over time across investors with given investment strategies. Social finance emphasizes that investors adopt and modify their financial traits, such as saving propensities, trading strategies, or belief in different investor philosophies. So the distribution of traits in the investor population, not just wealths, evolves.

Econophysics and other pioneering evolutionary finance models have often employed mechanistic assumptions about investor behavior or price determination. This can be a fruitful modeling strategy. Social finance recognizes that investors analyze alternatives, often in reasonable ways, as active processors of information.

A subset of evolutionary finance models allows for social interaction. An important ingredient of existing evolutionary finance, as in many models of cultural evolution, is payoff-biased transmission—the copying of traits that have performed well (14) (for financial markets, see refs. 15 and 16). Social finance seeks to systematically delineate and explore a wide range of relevant social transmission biases (as analyzed, e.g., in ref. 14). These include greater transmission of actions that are more observable and salient to others, of ideas that are easier to understand, of folk models that are more heavily cued in the environment, and of traits that bearers have an incentive to disseminate to others.

Social finance recognizes that new financial traits appear routinely and are often systematically modified as they are transmitted from one agent to another. In genetic evolution, such newly generated or modified inheritance is called mutation pressure. In cultural evolutionary applications, mutation pressure is commonplace. For example, when investors who have favorable information about a stock buy it, they often communicate with positive “spin” to others in the hope of driving up its price.

Econophysics and evolutionary models have often been applied to high-frequency trading and market microstructure, financial power laws, time decay in volatility and covariances, and chaotic dynamics (see review in ref. 17). Social finance widens the scope of financial phenomena and empirical puzzles to be addressed. These include medium- and low-frequency patterns of trading and asset pricing, long-term capital market equilibrium, the pricing of factor risk, bubbles and crashes, asset pricing anomalies, and managerial and regulatory policy. Since such applications have been of great interest to financial economists and practitioners, greater focus on these topics promises to build bridges with an intellectual lineage that has been largely disjoint from econophysics and evolutionary finance.

A financial market consists of a population of agents who transact based upon their preferences, beliefs, and folk models of how markets work. These cultural traits, and resulting financial strategies, are transmitted, potentially with modification, from person to person. Examples include trading strategies such as dollar cost averaging, technical strategies, and diversification versus stock picking. Investors copy, debate, modify, and persuade each other of these strategies and folk models.

The transmission of traits from one investor to another is a kind of inheritance—similarity in traits between an agent (or set of agents) that, for some set of social transactions, is designated as culturally ancestral and an agent (or set of agents) that is designated as descendant. In cultural evolution, selection and mutation pressure are often directional drivers of change. An example of selection is the high recent transmissibility of the idea that Bitcoin is a fabulous investment opportunity. Pro-Bitcoin ideas have recently had high fitness—many cultural descendants. An example of mutation pressure mentioned earlier is spin about purchased stocks.

Analytically, these cultural evolutionary forces can be precisely quantified. The evolution of such traits in a single generation can be decomposed into a selection component and a nonselection component (reflecting mutation pressure) using the Price equation (18–20).

The study of cultural evolution is highly interdisciplinary. We highlight some theoretical and empirical insights from this literature that are especially relevant for financial markets.

Darwinian selection in biology promotes adaptation, wherein organisms function well in some environment. Selection and adaptation are among the key premises of what Lo (7) calls the adaptive markets hypothesis. In cultural evolution, too, adaptation is about differential reproductive success—of cultural traits rather than genes. Adaptation of financial traits does not always promote the success of the vehicles that bear them (such as investors or managers), in terms of either welfare or reproduction. Even fallacious folk financial models often persist in the population of amateurs and professionals.

Nevertheless, there are myriad examples of cultural traits that spread owing to benefits conferred upon their bearers. Examples include belief in stock market participation and various quantitative methodologies used by investment professionals. Crucially, the conferred benefits need not be correctly understood by the bearer (25).

For example, popular books and websites almost uniformly recommend that borrowers refinance their fixed rate mortgages prematurely—as soon as the present value of doing so turns positive. Obeying would ensure that the borrower’s gain from refinancing is virtually zero! However, the simplicity of this rule of thumb makes it highly transmissible. Furthermore, it may actually help borrowers, since many are prone to excessive delay and inertia. “Dollar cost averaging” is another strategy that might be beneficial for reasons that investors do not correctly understand (26).

Financial beliefs that are detrimental to their bearers can also be highly transmissible. For example, Rantala (27) provides evidence that participation in a major Finnish Ponzi scheme was mediated by social interaction between participants. The payback criterion for discounting projects causes mistaken investment decisions by ignoring the time value of resources, yet survives.

The transmissibility of a trait often depends on how common it is, one reason being that payoffs to the bearer are frequency dependent. For example, congestion costs in investing strategies are ubiquitous—adopting a strategy reduces the benefit to competitors of doing so. So security trading is an ecological interaction between different investor types whose activities influence each other’s profitability via market price (28). Owing to frequency dependence, a diversity of traits (or investment strategies) can be maintained in a population.

Owing to frequency dependence and payoff interactions, a diversity of folk models can coexist in the population adapted to distinct ecological niches. In financial markets these niches are filled by retail investors and a variety of financial institutions and players. Indeed, Lo (7) refers to the panoply of specialized adaptations of hedge funds as the “Galapagos islands of finance.”

Strategic complementarities can be positive as well as negative and can induce multiple equilibria, an example being either liquid or illiquid financial markets. Societies can pass through critical transitions between very different financial equilibria. Historically, some societies have evolved to attain thriving financial markets, legal systems that underpin such markets, and extensive financing of business innovation. This involves a coordinated shift in the beliefs and expectations of financial agents and regulators. It will be valuable to model the cultural evolutionary determinants of the critical transition from financial autarky to financial development.

We now discuss models of the evolution of financial markets. We start with biased transmission of cultural traits and then turn to compartmental models and to avenues for empirical testing.

Regulation evolves as policymakers learn about the effectiveness of different regulatory systems. Greater heterogeneity in regulatory systems across principalities can promote effective regulation via mutation, in the form of experimentation, and selection of superior outcomes (80). Sunsetting of existing regulations also promotes experimentation and can prevent regulatory drift in the form of large mutations at the time of rare crises (81). Both heterogeneity and sunsetting can potentially help regulators adapt to rapidly changing environments, avoid the extinction of beneficial policies via random drift, and compete more effectively in the evolutionary arms race with investors (whose strategies evolve quickly).

The cultural evolutionary approach also suggests additional directions for promoting financial literacy and prudent investor behavior. Evolutionary design can be used to increase the transmissibility of accurate or functional investor beliefs and behaviors.

For example, a way to help people save more can be to increase cues about saving (or low spending) by others (49). This is because ideas that are cued more heavily by the environment tend to spread more readily (82). Cues about saving can help offset the many daily cues (such as social media posts) about new purchases of others.

Learning from others is central in financial markets, as both classical finance and behavioral finance have long recognized. However, to fully understand the learning and transmission of financial traits, we need an evolutionary perspective that clarifies the nature of transmission biases and the coevolution of cultural traits and financial outcomes. The perspective that we advance, social finance, provides such a paradigm shift. It goes beyond behavioral finance (the application of psychology to markets) in focusing explicitly on how social interaction shapes thought and behavior and on the selection, mutation pressure, and drift processes by which these traits evolve. Social finance studies these issues with a broadened set of financial traits, transmission biases, and focal applications.

Social finance has promise to help explain important financial phenomena, such as bubbles and stock return predictability anomalies at different time horizons. For example, commentators have long argued that market bubbles, crashes, and swings in market sentiment are social phenomena and reflect contagion of emotions as well as ideas. This perspective contrasts with the classical finance view that (apart from highly restrictive theoretical scenarios) bubbles do not exist. It also contrasts with behavioral theories in which bubbles result from individual-level misperceptions, wherein social interaction occurs only via observation of market price. In contrast, in social finance, bubbles derive from the transmission of financial ideas, feelings, and behaviors through conversation, mass media and social media, and personal observation, and explicitly analyzes biases in this transmission process. This approach can therefore capture extreme amplification of misperceptions.

Furthermore, social finance offers a natural line of attack for addressing the increasingly dynamic nature of empirical anomalies in trading and returns in finance research. For example, there are behavioral models of the momentum anomaly (62), but in recent years the further puzzle has been why momentum is sometimes especially strong, and in other periods there are predictable “momentum crashes” wherein momentum reverses (83). There have also been notable swings in the returns on value versus growth stocks. The usual high returns on value stocks relative to growth stocks (84) were strongly reversed, for example, at the time of the millennial tech bubble and during the recent pandemic period. The dynamics of booms and busts in corporate financial behaviors, such as takeover and restructuring waves, are another natural domain for the social finance approach.

Another major underexplored topic is how and why investor interest has shifted toward and between different kinds of investment funds, such as mutual funds, exchange-traded funds, and hedge funds. Given the efficiencies of fund investing relative to individual stocks, the slowness of the historical rise over decades in fund investing is a major phenomenon to be explained. Finally, social finance is uniquely suited for the study of the evolution and spread of both narrow investment ideas, such as the recommendation to buy on the dips, and wider assemblies of investment ideas, such as the value and growth investment philosophies.

Finance scholars have devoted vastly greater attention to how investors learn from market price than to how they update beliefs by talking to each other (including textual communication). Research has also focused far more on rational than on biased social updating. Evolutionary modeling of biased transmission of private signals is a promising direction.

A major topic of evolutionary biology is the study of how adaptations as functional systems, such as the eye, evolve through sequential accumulation of traits. A little-explored direction in social finance is to model how coadapted assemblies of folk models, such as the value investing or growth investing philosophies, evolve. Studying this requires paying attention to complementarities between financial ideas, which can arise for various reasons. The ideas underlying portfolio theory are transmissible among sophisticated investors owing to their logic and usefulness. Other financial ideas combine effectively by appealing to the same moralistic emotions, as with the value philosophy. Moral intuitions may also explain why macrocultural traits, such as religion, affect financial behaviors (31).

Another rich direction suggested by social finance is the study of how emotions bias the transmission of different kinds of investment narratives and behaviors (3, 7) and how this influences bubbles and swings in market sentiment. For example, when a bubble starts to feel precarious, does fear cause investors to start transmitting danger-related instead of opportunity-related narratives?

Finally, financial applications have the advantage of extensive datasets on prices and actions, along with textual and social network data from the business press and social media. Financial markets also have a very well-defined and measurable payoff score, trading profits, that influences the spread of investor traits. So financial markets are an attractive domain for testing and refining general cultural evolutionary hypotheses.

We thank participants at the Conference on Evolutionary Models of Financial Markets, MIT Laboratory for Financial Engineering (virtual, June 2020); Siew Hong Teoh; Sheridan Titman; Jeffrey Zwiebel; and especially the discussant, Simon Levin and an anonymous referee for insightful comments.