Fast and frugal heuristics - simple rules for making decisions with realistic mental resources - are presented here. These heuristics can enable both living organisms and artificial systems to make smart choices, classifications, and predictions by employing bounded rationality. But when and how can such fast and frugal heuristics work? What heuristics are in the mind's adaptive toolbox, and what building blocks compose them? Can judgments based simply on a single reason be as accurate as those based on many reasons? Could less knowledge even lead to systematically better predictions than more knowledge? This book explores these questions by developing computational models of heuristics and testing them through experiments and analysis. It shows how fast and frugal heuristics can yield adaptive decisions in situations as varied as choosing a mate, dividing resources among offspring, predicting high school drop-out rates, and playing the stock market.

Simple Heuristics That Make Us Smart

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

ARCH modeling in finance: A review of the theory and empirical evidence

Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed.

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Agent-based modeling: Methods and techniques for simulating human systems

Complex dynamical systems, ranging from ecosystems to financial markets and the climate, can have tipping points at which a sudden shift to a contrasting dynamical regime may occur. Although predicting such critical points before they are reached is extremely difficult, work in different scientific fields is now suggesting the existence of generic early-warning signals that may indicate for a wide class of systems if a critical threshold is approaching.

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Early-warning signals for critical transitions

This short note draws some connections between Mandelbrot‗s empirical legacy, and the interdisciplinary work that followed in finance. Much of this work is now labeled econophysics, but some has always been more in the realm of economics than physics. In a few areas the overlap is even becoming quite complete as in market microstructure. I will also give some ideas about the various successes and failures in this area, and some directions for the future of agent- based modeling in particular.

Financial price dynamics and agent-based models as inspired by Benoit Mandelbrot

This paper tests two of the simplest and most popular trading rules--moving average and trading range break--by utilizing the Dow Jones Index from 1897 to 1986. Standard statistical analysis is extended through the use of bootstrap techniques. Overall, their results provide strong support for the technical strategies. The returns obtained from these strategies are not consistent with four popular null models: the random walk, the AR(1), the GARCH-M, and the Exponential GARCH. Buy signals consistently generate higher returns than sell signals, and further, the returns following buy signals are less volatile than returns following sell signals. Moreover, returns following sell signals are negative, which is not easily explained by any of the currently existing equilibrium models. Copyright 1992 by American Finance Association.(This abstract was borrowed from another version of this item.)

Simple Technical Trading Rules And The Stochastic Properties Of Stock Returns

Empirical Evidence for Nonlinearities and Chaos in Economic Time Series: A Summary of Recent Results

Recent research has shown the importance of time horizons in models of learning in finance. The dynamics of how agents adjust to believe that the world around them is stationary may be just as crucial in the convergence to a rational-expectations equilibrium as getting parameters and model specifications correct in the learning process. This paper explores the process of this evolution in learning and time horizons in a simple agent-based financial market. Trading is done in a market with a single stock in finite supply, paying a stochastic dividend. A risk free asset is available in infinite supply. Agents maximize an infinite-horizon time-separable utility function in each period's consumption. They are required to select from a set of given forecasting/trading rules optimized to past data. Heterogeneity is introduced through the time horizon that they believe is relevant to use in deciding over trading rules. Long horizon agents build relative performance measures looking back into the distant past, while those with short horizons believe that only recent measures of performance are useful for decision making. The price of the risky asset is set to balance current agent demand with its fixed supply at each period. Once the price is endogenously determined, returns are calculated and dividends paid. Agents make consumption decisions and wealth is calculated. Relative wealth affects the market in two ways. First, wealthier individuals are able to move prices by larger amounts. Second, evolution takes place in which less wealthy agents are dropped out of the market and replaced with new ones drawn according to current wealth levels. The horizon lengths of wealthier agents are given more weight in the generation of new agents. The primary objectives of this paper are to understand better the convergence properties of learning with heterogeneous horizons. Several benchmark cases are explored in which a stationary rational-expectations equilibrium exists, and agents should converge to the longest horizon possible. The model is explored to see in which cases this convergence does not occur, and if it does not, what sorts of short-horizon features self-reinforce in agents' short-horizon forecasting models. Also, experiments are performed on the "invadeability" of a group of short-horizon investors to see if they can be invaded by those with long horizons. The paper also briefly addresses two eventual goals. First, the replication of certain features in financial data, such as excess volatility and trading volume phenomena. Second, while his model is strictly computational, some assessments about moving it to an analytic setting are made.

Evolution and Time Horizons in an Agent-Based Stock Market

Heterogeneous agent models for financial markets have provided explanations for many empirical regularities of relatively high frequency (hourly/daily) financial time series. They have been much quieter when it comes to longer range features. This paper examines a simplified computational heterogeneous agent model in the context of various longer range time series properties for equity returns. The model is compared to a specially created long range data set, and is found to perform well in terms of replicating features, and even revealing some aspects of the data that have not been well quantified to date. By matching empirical properties at both short and long horizons this sets a higher standard in terms of validation which this model is able to match.

https://people.brandeis.edu/~blebaron/wps/longrange.pdf

Blake LeBaron

Papers

Financial price dynamics and agent-based models as inspired by Benoit Mandelbrot

Simple Technical Trading Rules And The Stochastic Properties Of Stock Returns

Empirical Evidence for Nonlinearities and Chaos in Economic Time Series: A Summary of Recent Results

Evolution and Time Horizons in an Agent-Based Stock Market

Heterogeneous Agents and Long Horizon Features of Asset Prices