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Title: Can learning classifier systems represent competent traders? : the stock markets trading case
Author: Schulenburg, S.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2003
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I seek an understanding of the dynamics: learning and evolution, of certain groups of artificially created agents – known here as trader-types – making decisions in a real stock market scenario. With this as the primary motivation, a specific problem has been devised and three different groups of agents have been modelled to learn, forecast and trade in a real stock market scenario given exogenously in the form of easily-obtained stock statistics such as various price moving averages, first difference in prices, volume ratios, etc. These artificial trader-types trade and learn simultaneously during – in most cases – a ten year period. They start with no prior knowledge about the market, i.e. they have no notion of what is a good or a bad approach to start with: all their market models are created randomly at the beginning of the period, with the idea that new models developed through experience will be formed and polished as time progresses. The life of such trader-types commences when they are given an initial wealth to trade over two assets (a risk less bond represented by the fixed interest rate given by the bank and a real risky stock) and ends in most cases after one decade. First, in this problem I try to explore whether it is feasible to represent with Learning Classifier Systems (LCS) some of the key elements that play a role in the decision-making process of real stock market traders when viewing it from an evolutionary framework. Specifically, two fundamental questions are addressed under this first and broad topic: Are the trader-types able to (i) evolve and (ii) behave in similar ways to human traders under the real market conditions described above? Here the work is concentrated in LCS as the learning approach and in viewing the agent as part of a process where adaptation to a partially understood market environment is a necessary element for survival to occur. Second, this thesis reports on a number of experiments where the forecasting performance of the adaptive agents is compared against the performance of the buy-and-hold strategy, a ­trend-following strategy, a random strategy and finally against the bank investment over the same period of time at a fixed compound interest rate. To make the experiments as real as possible, agents also pay commissions on every trade. The results so far suggest that this is an excellent approach to make trading decisions in the stock market and that continual learning and adaptation not only play an important role but are also necessary elements in the decision-making process. Third, a possible explanation about the relationship between the artificial agents’ evolved sets of market strategies and the price dynamics created by real agents in the market is given for a number of stocks.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available