Showing papers by "Blake LeBaron published in 1999"

Evolution and Time Horizons in an Agent Based Stock Market

Abstract: 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. The results indicate that while the simple model structure used here replicates usual rational expectations results with long horizon agents, the route to evolving a population of both long and short horizon agents to long horizons alone may be difficult. Furthermore, populations with both short and long horizon agents increase return variability, and leave patterns in volatility and trading volume similar to actual financial markets.

Computational Finance

Abstract: 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.

The Stability of Moving Average Technical Trading Rules on the Dow Jones Index

Abstract: This paper analyzes the behavior of moving average technical trading rules applied to over 100 years of the Dow Jones Industrial Index. It is found that the dierences between conditional means during buy and sell periods has changed dramatically over the previous 10 years relative to the previous 90 years of data, but dierences in conditional variances have not changed much over the entire sample. Further robustness checks indicate that similar results could be obtained with simple momentum based strategies. The analysis is performed on the actual Dow series, but these techniques could be useful in derivative markets where better estimates of conditional means and variances would be useful information.

An artificial stock market

Abstract: The Santa Fe Artificial Stock Market consists of a central computational market and a number of artificially intelligent agents. The agents choose between investing in a stock and leaving their money in the bank, which pays a fixed interest rate. The stock pays a stochastic dividend and has a price which fluctuates according to agent demand. The agents make their investment decisions by attempting to forecast the future return on the stock, using genetic algorithms to generate, test, and evolve predictive rules. The artificial market shows two distinct regimes of behavior, depending on parameter settings and initial conditions. One regime corresponds to the theoretically predicted rational expectations behavior, with low overall trading volume, uncorrelated price series, and no possibility of technical trading. The other regime is more complex, and corresponds to realistic market behavior, with high trading volume, high intermittent volatility (including GARCH behavior), bubbles and crashes, and the presence of technical trading. One parameter that can be used to control the regime is the exploration rate, which governs how rapidly the agents explore new hypotheses with their genetic algorithms. At a low exploration rate the market settles into the rational expectations equilibrium. At a high exploration rate it falls into the more realistic complex regime. The transition is fairly sharp, but close to the boundary the outcome depends on the agents’ initial “beliefs”—if they believe in rational expectations they occur and are a local attractor; otherwise the market evolves into the complex regime.

Building Financial Markets With Articial Agents: Desired goals, and present techniques

Abstract: Financial markets operate as a large interacting group of agents each in a constant struggle to better understand and interpret current prices and information. The complex interconnections between prices and information is probably more dramatic in nancial markets than any other economic situation. Economic theory has been capable of describing many dierent nancial equilibria, but it remains quiet on the types of dynamics that can occur while learning is still active and equilibrium is never quite obtained. Models of learning agents allow a direct attack on this problem. However, even though this approach may seem appealing at rst it does come with many costs. The rst of these is the modeling of the agents themselves. Boundedly rational agents can come in many forms, and an important question for theorizing is where to\set the dial" of rationality. This paper will describe some of the methods that have worked, and which directions look promising. Some methods for endogenously setting the level of rationality will be discussed. Finally, comparisons to the single agent, homogeneous belief world will be made, stressing why this is still a useful benchmark. A second issue involves the actual trading mechanisms, and this will be brie∞y discussed in relation to how outcomes can be aected. In closing, some of the policy questions centered on market stability and structure will be compared with certain computational issues.

Information Dissemination and Aggregation in Asset Markets with Simple Intelligent Traders

Abstract: Various studies of asset markets have shown that traders are capable of learning and transmitting information through prices in many situations. In this paper we replace human traders with intelligent software agents in a series of simulated markets. Using these simple learning agents, we are able to replicate several features of the experiments with human subjects, specifically regarding (1) dissemination of information from informed to uninformed traders and (2) aggregation of information spread over different traders.

Volatility Persistence and Apparent Scaling Laws in Finance

Abstract: Recent evidence has shown possible scaling and self-similarity in high frequency financial time series. This paper demonstrates that many of these graphical scaling results could have been generated by a simple stochastic volatility model. This casts doubt on the power of these tests to discern between true scaling and simple highly dependent stochastic processes.

Foreign Exchange Market Trading Volume and Federal Reserve Intervention

Abstract: We find a large positive correlation between daily trading volume in currency futures markets and foreign exchange intervention by the Federal Reserve over the period 1979-1996. Neither contemporaneous nor predicted volatility can fully account for the increases in trading activity. Whether or not the intervention operation is publicly reported appears to be an important determinant of trading volume.

Time Series Properties of an Artificial Stock Market

Abstract: A market of artificially intelligent traders is constructed to buy and sell a risky asset along with a risk free bond. Prices of the risky asset are determined endogenously from the interactions of the strategies which make trades and gather data. Each trader tries to learn about the world around it while enhancing its trading strategies. The primary purpose of this paper is to demonstrate that such a market replicates some of the basic empirical features of many asset markets including the persistence of volatility and trading volume, weak trends in prices, and leptokurtosis in returns. Also, for certain parameter values agents converge to a well defined rational expectations equilibrium.

Evolution and Time Horizons in an Agent-Based Stock Market

Abstract: 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.