This paper surveys research on computational agent-based models used in finance. It will concentrate on models where the use of computational tools is critical in the process of crafting models which give insights into the importance and dynamics of investor heterogeneity in many financial settings.

This paper develops a model of a learning market-maker by extending the Glosten-Milgrom model of dealer markets. The market-maker tracks the changing true value of a stock in settings with informed traders (with noisy signals) and liquidity traders, and sets bid and ask prices based on its estimate of the true value. We empirically evaluate the performance of the market-maker in markets with different parameter values to demonstrate the effectiveness of the algorithm, and then use the algorithm to derive properties of price processes in simulated markets. When the true value is governed by a jump process, there is a two regime behavior marked by significant heterogeneity of information and large spreads immediately following a price jump, which is quickly resolved by the market-maker, leading to a rapid return to homogeneity of information and small spreads. We also discuss the similarities and differences between our model and real stock market data in terms of distributional and time series properties of returns. Submitted to: Quantitative Finance 1.

This paper studies market-makers, agents responsible for maintaining liquidity and orderly price transitions in markets. Market-makers include major firms making markets on global stock exchanges, as well as software agents that run behind the scenes on novel electronic markets like prediction markets. We use a sophisticated model of marketmaking to build richer agent-based models of markets and show how these models can be useful both in understanding properties of existing markets and in predicting the impacts of structural changes. For example, we show how competition among market-makers can lead to significantly faster price discovery following a jump in the true value of an asset. We also show that myopic profit-maximization, apart from leading to poor market quality, is sub-optimal even for a monopolistic market-maker. This observation leads to an interesting characterization of the market-maker’s explorationexploitation dilemma as a tradeoff between price discovery and profit-taking.

We study the profit-maximization problem of a monopolistic market-maker who sets two-sided prices in an asset market. The sequential decision problem is hard to solve because the state space is a function. We demonstrate that the belief state is well approximated by a Gaussian distribution. We prove a key monotonicity property of the Gaussian state update which makes the problem tractable, yielding the first optimal sequential market-making algorithm in an established model. The algorithm leads to a surprising insight: an optimal monopolist can provide more liquidity than perfectly competitive market-makers in periods of extreme uncertainty, because a monopolist is willing to absorb initial losses in order to learn a new valuation rapidly so she can extract higher profits later. 1

This thesis describes and evaluates a market-making algorithm for setting prices in financial markets with asymmetric information, and analyzes the properties of artificial markets in which the algorithm is used. The core of our algorithm is a technique for maintaining an online probability density estimate of the underlying value of a stock. Previous theoretical work on market-making has led to price-setting equations for which solutions cannot be achieved in practice, whereas empirical work on algorithms for market-making has focused on sets of heuristics and rules that lack theoretical justification. The algorithm presented in this thesis is theoretically justified by results in finance, and at the same time flexible enough to be easily extended by incorporating modules for dealing with considerations like portfolio risk and competition from other market-makers. We analyze the performance of our algorithm experimentally in artificial markets with different parameter settings and find that many reasonable real-world properties emerge. For example, the spread increases in response to uncertainty about the true value of a stock, average spreads tend to be higher in more volatile markets, and market-makers with lower average spreads perform better in environments with multiple competitive market-makers. In addition, the time series data generated by simple markets populated with marketmakers using our algorithm replicate properties of real-world financial time series, such as volatility clustering and the fat-tailed nature of return distributions, without the need to specify explicit models for opinion propagation and herd behavior in the trading crowd.

Evolutionary simulation of hedging pressure in futures markets Abstract — We present a real world application that models a financial futures market. The agent-based simulation includes speculator agents each of which uses a Genetic Algorithm to improve its profitability in the market. This is a realistic simulation whose rates-of-return distribution is similar to those of real futures markets such as corn and FTSE100 futures. The futures markets have never before been simulated to this level of detail, and the simulation is used to test the long-held belief that speculators are more profitable if they incorporate “hedging pressure ” into their price calculations — essentially, the use of market knowledge about supply and demand. Surprisingly, we show that hedging pressure cannot be used to improve profits for speculators. I.

The Problem: Market-makers serve important functions in financial markets, providing liquidity to the markets and immediacy to the execution of trades. We are designing algorithms for automated market-making in different market conditions such as competitive vs. monopolistic dealer markets and studying outcomes in terms of indicators of market quality such as the bid–ask spread. Motivation: Progress in machine learning techniques has led to the development of various techniques well suited to online estimation and rapid aggregation of information. Theoretical models of marketmaking have been used to derive price-setting equations for which solutions cannot be achieved in practice, whereas empirical work on algorithms for market-making has so far focused on sets of heuristics and rules that lack theoretical justification. We are developing algorithms that are theoretically justified by results in finance, and at the same time flexible enough to be easily extended by incorporating modules for dealing with considerations like portfolio risk and competition from other market-makers. Previous Work: Previous research at CBCL on market-making has focused on heuristics that use the limit-order book to set prices [3] and on reinforcement learning for market-making in very simple environments [8, 2]. Our work draws heavily on the market-microstructure literature, especially the price setting equations derived by Glosten and Milgrom under conditions of information asymmetry and