Artificial markets and intelligent agents

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 173-178).In many studies of market microstructure, theoretical analysis quickly becomes in tractable for all but the simplest stylized models. This thesis considers two alternative approaches, namely, the use of experiments with human subjects and simulations with intelligent agents, to address some of the limitations of theoretical modeling. The thesis aims to study the design, development and characterization of artificial markets as well as the behaviors and strategies of intelligent trading and market making agents. Simulations and experiments are conducted to study information aggregation and dissemination in a market. A number of features of the market dynamics are examined: the price efficiency of the market, the speed at which prices converge to the rational expectations equilibrium price, and the learning dynamics of traders who possess diverse information or preferences.(cont.) By constructing simple intelligent agents, not only am I able to replicate several findings of human-based experiments, but I also find intriguing differences between agent-based and human based experiments. The importance of liquidity in securities markets motivates considerable inter ests in studying the behaviors of market-makers. A rule-based market-maker, built in with multiple objectives, including maintaining a fair and orderly market, maximizing profit and minimizing inventory risk, is constructed and tested on historical transaction data. Following the same design, an adaptive market-maker is modeled in the framework of reinforcement learning. The agent is shown to be able to adapt its strategies to different noisy market environments.by Tung Chan.Ph.D

Computational methodology for modelling the dynamics of statistical arbitrage

Recent years have seen the emergence of a multi-disciplinary research area known as "Computational Finance". In many cases the data generating processes of financial and other economic time-series are at best imperfectly understood. By allowing restrictive assumptions about price dynamics to be relaxed, recent advances in computational modelling techniques offer the possibility to discover new "patterns" in market activity. This thesis describes an integrated "statistical arbitrage" framework for identifying, modelling and exploiting small but consistent regularities in asset price dynamics. The methodology developed in the thesis combines the flexibility of emerging techniques such as neural networks and genetic algorithms with the rigour and diagnostic techniques which are provided by established modelling tools from the fields of statistics, econometrics and time-series forecasting. The modelling methodology which is described in the thesis consists of three main parts. The first part is concerned with constructing combinations of time-series which contain a significant predictable component, and is a generalisation of the econometric concept of cointegration. The second part of the methodology is concerned with building predictive models of the mispricing dynamics and consists of low-bias estimation procedures which combine elements of neural and statistical modelling. The third part of the methodology controls the risks posed by model selection and performance instability through actively encouraging diversification across a "portfolio of models". A novel population-based algorithm for joint optimisation of a set of trading strategies is presented, which is inspired both by genetic and evolutionary algorithms and by modern portfolio theory. Throughout the thesis the performance and properties of the algorithms are validated by means of experimental evaluation on synthetic data sets with known characteristics. The effectiveness of the methodology is demonstrated by extensive empirical analysis of real data sets, in particular daily closing prices of FTSE 100 stocks and international equity indices

College Catalog, 2012-2013, Graduate

https://digitalcommons.buffalostate.edu/buffstatecatalogs/1222/thumbnail.jp

College Catalog, 2015-2016, Graduate

https://digitalcommons.buffalostate.edu/buffstatecatalogs/1228/thumbnail.jp

Virginia Commonwealth University Courses

Listing of courses for the 2018-2019 year