Slow-Fast Analysis of a Multi-Group Asset Flow Model with Implications for the Dynamics of Wealth

The multi-group asset flow model is a nonlinear dynamical system originally developed as a tool for understanding the behavioral foundations of market phenomena such as flash crashes and price bubbles. In this paper we use a modification of this model to analyze the dynamics of a single-asset market in situations when the trading rates of investors (i.e., their desire to exchange stock for cash) are prescribed ahead of time and independent of the state of the market. Under the assumption of fast trading compared to the time-rate of change in the prescribed trading rates we decompose the dynamics of the system to fast and slow components. We use the model to derive a variety of observations regarding the dynamics of price and investors’ wealth, and the dependence of these quantities on the prescribed trading rates. In particular, we show that strategies with constant trading rates, which represent the well-known constant-rebalanced portfolio (CRP) strategies, are optimal in the sense that they minimize investment risks. In contrast, we show that investors pursuing non-CRP strategies are at risk of loss of wealth, as a result of the slow system not being integrable in the sense that cyclic trading rates do not always result in periodic price variations

Let’s Chat... When Communication Promotes Efficiency in Experimental Asset Markets

The growing prevalence of stock market chat rooms and social media suggests communication between traders may affect market outcomes. Using data from a series of laboratory experiments, we study the causal effect of trader communication on the price efficiency of markets. We show that communication allows markets to convey private information more effectively. This effect is most pronounced when the communication platform publicizes a reputation score that might identify a person as not being truthful. This illustrates the need for market designers to consider social interactions when designing market institutions to leverage the social motives that foster information aggregation

Nonlinear Dynamics and Stability In a Multigroup Asset Flow Model

The multigroup asset flow model for asset price dynamics incorporates distinct motivations, e. g., trend and fundamentals (value) and assessments of value by different groups of investors. The stability and bifurcation properties are established for the curve of equilibria. We prove that if all trader groups focus on fundamentals, then all equilibria are stable. For systems in which there is one fundamental and one momentum (trend) group, we establish conditions for stability. In particular, an equilibrium that is stable becomes unstable as the time scale on which momentum investors focus diminishes. The computations examine the excursions, which we define as the maximum deviation in price of the trajectory from its initial price located near the curve of equilibria

Information Aggregation and the Cognitive Make-up of Traders

We assess the effect of the cognitive make-up of traders on the informational efficiency of markets. We put forth that cognitive skills, such as cognitive reflection, are crucial for ensuring the informational efficiency of markets because they endow traders with the ability to infer others’ information from prices. Using laboratory experiments, we show that information aggregation is significantly enhanced when (i) all traders possess high levels of cognitive sophistication and (ii) this high level of cognitive sophistication is common information for all traders. Our findings shed light on the cognitive and informational constraints underlying the efficient market hypothesis

Revisiting Information Aggregation in Asset Markets: Reflective Learning & Market Efficiency

The ability of markets to aggregate disperse information leading to prices that reflect the fundamental value of an asset is key to assessing the often-debated efficiency of markets. We study information aggregation in the experimental environment originally created by Plott and Sunder (1988). Contrary to the current belief, we find that markets do not aggregate information. The model that best describes our data, as well as data on information aggregation subsequent to Plott and Sunder (1988), is prior information (Lintner, 1969). That is, traders use their private information but fail to use market prices to infer other traders’ information. We argue that reflecting on asset prices to infer others’ information requires specific skills related to the concept of cognitive reflection. We develop a learning model in which only a subset of the traders possess this reflective capacity. We show, using both simulations and laboratory experiments, that information aggregation can only be achieved when the market is populated by highly reflective traders and this high level of cognitive reflection is commonly known to all of the traders

How Does Passive Investing Effect the Informational Efficiency of Prices?

We investigate the causal effects of passive investing on informational efficiency and market quality metrics by developing a novel laboratory experiment that introduces Index trackers with exogenous passive investment flows. We find that, while improving liquidity, Index tracking hurts informational efficiency, confirming our main hypothesis. Furthermore, we observe violations of the law of one price, leading to widespread and persistent arbitrage opportunities. Additionally, our research uncovers that Active traders, particularly those with private information about asset values and high cognitive ability, reap benefits from the introduction of Index tracking

Emerald Ash Borer Modeling Methods for Future Forest Projections

Natural Resource Ecology & Mgm

Modeling the Effects of Emerald Ash Borer on Forest Composition in the Midwest and Northeast United States

Natural Resource Ecology & Mgm