Iryna Veryzhenko

On the Design of Agent-Based Artificial Stock Markets

The purpose of this paper is to define software engineering abstractions that provide a generic framework for stock market simulations. We demonstrate a series of key points and principles that has governed the development of an Agent-Based financial market application programming interface (API). The simulator architecture is presented. During artificial market construction we have faced the whole variety of agent-based modelling issues : local interaction, distributed knowledge and resources, heterogeneous environments, agents autonomy, artificial intelligence, speech acts, discrete or continuous scheduling and simulation. Our study demonstrates that the choices made for agent-based modelling in this context deeply impact the resulting market dynamics and proposes a series of advances regarding the main limits the existing platforms actually meet.

Optimal Portfolio Diversification? A multi-agents ecological competition analysis

In this research we study the relative performance of investment strategies scrutinizing their behaviour in an ecological competition where populations of artificial investors co-evolve.We test different variations around the canonical modern portfolio theory of Markowitz, strategies based on the naive diversification principles and the combination of several strategies.We show, among others, that the best possible strategy over the long run always relies on a mix ofMean-Variance sophisticated optimization and a naive diversification. We show that this result is robust when short selling is allowed in the market and whatever the performance indicator chosen to gauge the relative interest of the studied investment strategies.

Risk Aversion Impact on Investment Strategy Performance: A Multi Agent-Based Analysis

In order to supply an additional evidence on the effect of individual investors preferences on their portfolio dynamics from the wealth and risk adjusted return point of view, we construct an agent-based multi-asset model. We populate the artificial market with heterogeneous mean-variance traders with quadratic utility function. We compare the relative performance of investment strategies differ on their risk preferences using ecological competitions, where populations of artificial investors co-evolve. Our findings show that the higher relative risk aversion helps the agents survive in a long-range time frame in the competitions for higher wealth or Sharpe ratio of constrained portfolios. However, when short-selling is allowed, the highest (as well as lowest) risk aversion does not guarantee the highest earnings. Risk lovers as well as absolute risk averse run quickly out of competitions. Only the traders with moderate level of risk aversion survive in the long run.

Computation of the Ex-Post Optimal Strategy for the Trading of a Single Financial Asset

In this paper we explain how to compute the maximum amount of money one investor can earn in trading a single financial asset under a set of trading constraints. The obtained algorithm allows to identify the ex-post optimal strategy S* over a set of (known) prices, which is unconventional in Finance. We deliberately adopt such a simplification to show that even if one posits a complete knowledge of the “future”, the determination of S* is far from triviality, especially in a framework with transaction costs. We review some solutions that are exponential and propose a new polynomial algorithm. Among others, our results shed light on a not so documented aspect of financial markets complexity, propose an absolute boundary for the profits one can realize in a specific time window and against which any investment strategy can be gauged.