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.

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

Least Median of Squares Estimation by Optimization Heuristics with an Application to the CAPM and Multi Factor Models

For estimating the parameters of models for financial market data, the use of robust techniques is of particular interest. Conditional forecasts, based on the capital asset pricing model, and a factor model are considered. It is proposed to consider least median of squares estimators as one possible alternative to ordinary least squares. Given the complexity of the objective function for the least median of squares estimator, the estimates are obtained by means of optimization heuristics. The performance of two heuristics is compared, namely differential evolution and threshold accepting. It is shown that these methods are well suited to obtain least median of squares estimators for real world problems. Furthermore, it is analyzed to what extent parameter estimates and conditional forecasts differ between the two estimators. The empirical analysis considers daily and monthly data on some stocks from the Dow Jones Industrial Average Index (DJIA).LMS, CAPM, Multi Factor Model, Differential Evolution, Threshold Accepting

Which heuristics can aid financial-decision-making?

© 2015 Elsevier Inc. We evaluate the contribution of Nobel Prize-winner Daniel Kahneman, often in association with his late co-author Amos Tversky, to the development of our understanding of financial decision-making and the evolution of behavioural finance as a school of thought within Finance. Whilst a general evaluation of the work of Kahneman would be a massive task, we constrain ourselves to a more narrow discussion of his vision of financial-decision making compared to a possible alternative advanced by Gerd Gigerenzer along with numerous co-authors. Both Kahneman and Gigerenzer agree on the centrality of heuristics in decision making. However, for Kahneman heuristics often appear as a fall back when the standard von-Neumann-Morgenstern axioms of rational decision-making do not describe investors' choices. In contrast, for Gigerenzer heuristics are simply a more effective way of evaluating choices in the rich and changing decision making environment investors must face. Gigerenzer challenges Kahneman to move beyond substantiating the presence of heuristics towards a more tangible, testable, description of their use and disposal within the ever changing decision-making environment financial agents inhabit. Here we see the emphasis placed by Gigerenzer on how context and cognition interact to form new schemata for fast and frugal reasoning as offering a productive vein of new research. We illustrate how the interaction between cognition and context already characterises much empirical research and it appears the fast and frugal reasoning perspective of Gigerenzer can provide a framework to enhance our understanding of how financial decisions are made

Construction of a taxonomy for requirements engineering commercial-off-the-shelf components

This article presents a procedure for constructing a taxonomy of COTS products in the field of Requirements Engineering (RE). The taxonomy and the obtained information reach transcendental benefits to the selection of systems and tools that aid to RE-related actors to simplify and facilitate their work. This taxonomy is performed by means of a goal-oriented methodology inspired in GBRAM (Goal-Based Requirements Analysis Method), called GBTCM (Goal-Based Taxonomy Construction Method), that provides a guide to analyze sources of information and modeling requirements and domains, as well as gathering and organizing the knowledge in any segment of the COTS market. GBTCM claims to promote the use of standards and the reuse of requirements in order to support different processes of selection and integration of components.Peer ReviewedPostprint (published version

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.

Behavioural Anomalies, Bounded Rationality and Simple Heuristics

The use of bounded rationality in explaining economic phenomena has attracted growing attention. In spite of this, there is still considerable disagreement regarding the meaning of bounded rationality. Basov (2005) argues that when modeling boundedly rational behaviour it is desirable to start with an explicit formulation of the learning process. A complete understanding of the boundedly rational decision-making process requires development of an evolutionary-dynamic model which can give rise to such learning processes. Evolutionary dynamics implies that individuals use heuristics to adjust their choices in light of past experiences, moving in the direction that appears most beneficial, where these adjustment rules are assumed ‘hardwired’ into human cognition through the process of biological evolution. In this paper we elaborate on the latter point by building a model of evolutionary selection relevant to heuristics. We show that in addition to explaining the origin of learning rules this approach also sheds light on some well documented preference anomalies.Bounded Rationality;Heuristics;Replicator Dynamics

Complexity, Evolution and Learning: a simple story of heterogeneous expectations and some empirical and experimental validation.

This note discusses complexity models in economics. A key feature of these models is that agents have heterogeneous expectations, disciplined by adaptive learning and evolutionary selection. Agents adapt their rules based upon past observations and switch between different forecasting heuristics based upon strategy performance. We discuss how these models match empirical facts as well as laboratory experiments with human subjects and how this approach may tame the ``wilderness of bounded rationality''.