In this chapter we describe a stock market simulation in which stock market participants use genetic algorithms to gradually improve their trading strategies over time. A variety of experiments show that, under certain conditions, some market participants can make consistent profits over an extended period of time, a finding that might explain the success of some real-world money managers. These experiments suggest a four parameter model of market participants. Each participant can be described along four dimensions: information set, constraint set, algorithm set, and model set. The information set captures what data the participant has access to (e.g., the participant has access to all historical price data). The constraint set describes u...
The central question that this thesis addresses is how economic agents learn to form price expectati...
This paper investigates the effect of using varying amounts of training data on the specificity and ...
Using virtual stock markets with artificial interacting software investors, aka agent-based models, ...
In this paper we propose an artificial stock market model based on interaction of heterogeneous agen...
Artificial stock market is a growing field in the past few years. The essence of this issue is the i...
AbstractDevelopment of stock market is affected by many factors. It is difficult to predict changes ...
Abstract This paper deals with multi-agent based modeling of artificial stock market by using the co...
In this chapter, we will present agent-based simulations as well as human experiments in double auct...
<p><em>The paper focuses on artificial stock market simulations using a multi-agent model incorporat...
Abstract—This paper extends a previous market microstruc-ture model, where we used Genetic Programmi...
The R code explores the calibration and simulation of the Farmer and Joshi (2002) agent-based model ...
This paper applies evolutionary modeling to expectation formation of an asset's price. As a first st...
This dissertation proposes a two-risky-asset Artificial Stock Market Model and investigates its appl...
In order to verify the effects of machine learning in a market structure, an evolutionary model cont...
In this paper, we propose a new architec-ture to study artificial stock markets. This architecture r...
The central question that this thesis addresses is how economic agents learn to form price expectati...
This paper investigates the effect of using varying amounts of training data on the specificity and ...
Using virtual stock markets with artificial interacting software investors, aka agent-based models, ...
In this paper we propose an artificial stock market model based on interaction of heterogeneous agen...
Artificial stock market is a growing field in the past few years. The essence of this issue is the i...
AbstractDevelopment of stock market is affected by many factors. It is difficult to predict changes ...
Abstract This paper deals with multi-agent based modeling of artificial stock market by using the co...
In this chapter, we will present agent-based simulations as well as human experiments in double auct...
<p><em>The paper focuses on artificial stock market simulations using a multi-agent model incorporat...
Abstract—This paper extends a previous market microstruc-ture model, where we used Genetic Programmi...
The R code explores the calibration and simulation of the Farmer and Joshi (2002) agent-based model ...
This paper applies evolutionary modeling to expectation formation of an asset's price. As a first st...
This dissertation proposes a two-risky-asset Artificial Stock Market Model and investigates its appl...
In order to verify the effects of machine learning in a market structure, an evolutionary model cont...
In this paper, we propose a new architec-ture to study artificial stock markets. This architecture r...
The central question that this thesis addresses is how economic agents learn to form price expectati...
This paper investigates the effect of using varying amounts of training data on the specificity and ...
Using virtual stock markets with artificial interacting software investors, aka agent-based models, ...