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.

/pdf/agent-based-modeling-methods-and-techniques-for-simulating-4rcr8tlyyz.pdf

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.

/pdf/early-warning-signals-for-critical-transitions-1qy2ayqlyk.pdf

Early-warning signals for critical transitions

This article exposes problems of the commonly used technique of splitting the available data into training, validation, and test sets that are held fixed, warns about drawing too strong conclusions from such static splits, and shows potential pitfalls of ignoring variability across splits. Using a bootstrap or resampling method, we compare the uncertainty in the solution stemming from the data splitting with neural network specific uncertainties (parameter initialization, choice of number of hidden units, etc.). We present two results on data from the New York Stock Exchange. First, the variation due to different resamplings is significantly larger than the variation due to different network conditions. This result implies that it is important to not over-interpret a model (or an ensemble of models) estimated on one specific split of the data. Second, on each split, the neural network solution with early stopping is very close to a linear model; no significant nonlinearities are extracted.

https://papers.ssrn.com/sol3/Delivery.cfm?delivery_id=1504

A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series

Over the past 50 years, first in the US and then throughout the rest of the world, involvement in financial markets has grown spectacularly. Whether directly, through the purchases of securities, or indirectly, through pension plans and mutual funds, the financial industry now touches hundreds of millions of people.

Computational Finance

Exposes problems of the commonly used technique of splitting the available data into training, validation, and test sets that are held fixed, warns about drawing too strong conclusions from such static splits, and shows potential pitfalls of ignoring variability across splits. Using a bootstrap or resampling method, we compare the uncertainty in the solution stemming from the data splitting with neural-network specific uncertainties (parameter initialization, choice of number of hidden units, etc.). We present two results on data from the New York Stock Exchange. First, the variation due to different resamplings is significantly larger than the variation due to different network conditions. This result implies that it is important to not over-interpret a model (or an ensemble of models) estimated on one specific split of the data. Second, on each split, the neural-network solution with early stopping is very close to a linear model; no significant nonlinearities are extracted.

/pdf/a-bootstrap-evaluation-of-the-effect-of-data-splitting-on-33zqmxty1w.pdf

A bootstrap evaluation of the effect of data splitting on financial time series

We propose a theory of asset pricing based on heterogeneous agents who continually adapt their expectations to the market that these expectations aggregatively create And we explore the implications of this theory computationally using our Santa Fe artificial stock market Asset markets, we argue, have a recursive nature in that agents' expectations are formed on the basis of their anticipations of other agents' expectations, which precludes expectations being formed by deductive means Instead traders continually hypothesize---continually explore---expectational models, buy or sell on the basis of those that perform best, and confirm or discard these according to their performance Thus individual beliefs or expectations become endogenous to the market, and constantly compete within an ecology of others' beliefs or expectations The ecology of beliefs coevolves over time Computer experiments with this endogenous-expectations market explain one of the more striking puzzles in finance: that market traders often believe in such concepts as technical trading, "market psychology," and bandwagon effects, while academic theorists believe in market efficiency and a lack of speculative opportunities Both views, we show, are correct, but within different regimes Within a regime where investors explore alternative expectational models at a low rate, the market settles into the rational-expectations equilibrium of the efficient-market literature Within a regime where the rate of exploration of alternative expectations is higher, the market self-organizes into a complex pattern It acquires a rich psychology, technical trading emerges, temporary bubbles and crashes occur, and asset prices and trading volume show statistical features---in particular, GARCH behavior---characteristic of actual market data

Asset Pricing Under Endogenous Expectation in an Artificial Stock Market

This paper introduces an order-driven market with heterogeneous investors, who submit limit or market orders according to their own trading rules. The trading rules are repeatedly updated via simple learning and adaptation of the investors. We analyze markets with and without learning and adaptation. The simulation results show that our model with learning and adaptation successfully replicates long-memories in trading volume, stock return volatility, and signs of market orders in an informationally efficient market. We also discuss why evolutionary dynamics are important in generating these features.

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

Blake LeBaron

Papers

A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series

Computational Finance

A bootstrap evaluation of the effect of data splitting on financial time series

Asset Pricing Under Endogenous Expectation in an Artificial Stock Market

Long-Memory in an Order-Driven Market