Evolutionary Selection of Individual Expectations and Aggregate Outcomes

In recent 'learning to forecast' experiments with human subjects (Hommes, et al. 2005), three different patterns in aggregate asset price behavior have been observed: slow monotonic convergence, permanent oscillations and dampened fluctuations. We construct a simple model of individual learning, based on performance based evolutionary selectionor reinforcement learning among heterogeneous expectations rules, explaining these different aggregate outcomes. Out-of-sample predictive power of our switching model is higher compared to the rational or other homogeneous expectations benchmarks. Our results show that heterogeneity in expectations is crucial to describe individual forecasting behavior as well as aggregate price behavior.

Evolution of Market Heuristics

The time evolution of aggregate economic variables, such as stock prices, is affected by market expectations of individual investors. Neo-classical economic theory assumes that individuals form expectations rationally, thus enforcing prices to track economic fundamentals and leading to an efficient allocation of resources. However, laboratory experiments with human subjects have shown that individuals do not behave fully rational but instead follow simple heuristics. In laboratory markets prices may show persistent deviations from fundamentals similar to the large swings observed in real stock prices. Here we show that evolutionary selection among simple forecasting heuristics can explain coordination of individual behavior leading to three different aggregate outcomes observed in recent laboratory market forecasting experiments: slow monotonic price convergence, oscillatory dampened price fluctuations and persistent price oscillations. In our model forecasting strategies are selected every period from a small population of plausible heuristics, such as adaptive expectations and trend following rules. Individuals adapt their strategies over time, based on the relative forecasting performance of the heuristics. As a result, the evolutionary switching mechanism exhibits path dependence and matches individual forecasting behavior as well as aggregate market outcomes in the experiments. Our results are in line with recent work on agent-based models of interaction and contribute to a behavioral explanation of universal features of financial markets.

Financial markets forecasts revisited: are they rational, herding or bold?

We test whether professional forecasters forecast rationally or behaviorally using a unique database, QSS Database, which is the monthly panel of forecasts on Japanese stock prices and bond yields. The estimation results show that (i) professional forecasts are behavioral, namely, significantly influenced by past forecasts, (ii) there exists a stock-bond dissonance: while forecasting behavior in the stock market seems to be herding, that in the bond market seems to be bold in the sense that their current forecasts tend to be negatively related to past forecasts, and (iii) the dissonance is due, at least partially, to the individual forecasters' behavior that is influenced by their own past forecasts rather than others. Even in the same country, forecasting behavior is quite different by market.

The effects of subject pool and design experience on rationality in experimental asset markets

Empirical evidence suggests that prices do not always reflect fundamental values and individual behavior is often inconsistent with rational expectations theory. We report the results of fourteen experimental markets designed to examine whether the interactive effect of subject pool and design experience tempers price bubbles and improves forecasting ability. Our main findings are: (i) price run-ups are modest and dissipate quickly when traders are knowledgeable about financial markets and have design experience; (ii) price bubbles moderate quickly when only a subset of traders are knowledgeable and experienced; and (iii) individual forecasts of price are not consistent with the predictions of the rational expectations model in any market.Financial markets

Evolution of market heuristics

Abstract The time evolution of aggregate economic variables, such as stock prices, is affected by market expectations of individual investors. Neoclassical economic theory assumes that individuals form expectations rationally, thus forcing prices to track economic fundamentals and leading to an efficient allocation of resources. However, laboratory experiments with human subjects have shown that individuals do not behave fully rationally but instead follow simple heuristics. In laboratory markets, prices may show persistent deviations from fundamentals similar to the large swings observed in real stock prices. Here we show that evolutionary selection among simple forecasting heuristics can explain coordination of individual behavior, leading to three different aggregate outcomes observed in recent laboratory market-forecasting experiments: slow monotonic price convergence, oscillatory dampened price fluctuations, and persistent price oscillations. In our model, forecasting strategies are selected every period from a small population of plausible heuristics, such as adaptive expectations and trend-following rules. Individuals adapt their strategies over time, based on the relative forecasting performance of the heuristics. As a result, the evolutionary switching mechanism exhibits path dependence and matches individual forecasting behavior as well as aggregate market outcomes in the experiments. Our results are in line with recent work on agent-based models of interaction and contribute to a behavioral explanation of universal features of financial markets. Copyright © Cambridge University Press 2012

Individual Expectations and Aggregate Macro Behavior

The way in which individual expectations shape aggregate macroeconomic variables is crucial for the transmission and effectiveness of monetary policy. We study the individual expectations formation process and the interaction with monetary policy, within a standard New Keynesian model, by means of laboratory experiments with human subjects. We find that a more aggressive monetary policy that sets the interest rate more than point for point in response to inflation stabilizes inflation in our experimental economies. We use a simple model of individual learning, with a performance-based evolutionary selection among heterogeneous forecasting heuristics, to explain coordination of individual expectations and aggregate macro behavior observed in the laboratory experiments. Three aggregate outcomes are observed: convergence to some equilibrium level, persistent oscillatory behavior and oscillatory convergence. A simple heterogeneous expectations switching model fits individual learning as well as aggregate outcomes and outperforms homogeneous expectations benchmarks.

Is more memory in evolutionary selection (de)stabilizing?

We investigate the effects of memory on the stability of evolutionary selection dynamics based on a multi-nomial logit model in an asset pricing model with heterogeneous beliefs. Whether memory is stabilizing or destabilizing depends in general on three key factors: (1) whether or not the weights on past observations are normalized; (2) the ecology of forecasting rules, in particular the average strength of trend extrapolation and the spread in biased forecasts, and (3) whether or not costs for information gathering of economic fundamentals have to be incurred.

Individual Expectations, Limited Rationality and Aggregate Outcomes

Recent studies suggest that the type of strategic environment or expectation feedback may have a large impact on whether the market learns the rational fundamental price. We present an experiment where the fundamental price experiences large unexpected shocks. Markets with negative expectation feedback (strategic substitutes) quickly converge to the new fundamental, while markets with positive expectation feedback (strategic complements) do not converge, but show under-reaction in the short run and over-reaction in the long run. A simple evolutionary selection model of individual learning explains these differences in aggregate outcomes.