- author = "Sonia Schulenburg",
- title = "Can learning classifier systems represent competent traders? The stock markets trading case",
- school = "Artificial Intelligence Applications Institute, Division of Informatics, The University of Edinburgh",
- year = "2003",
- address = "UK",
- month = jul,
- keywords = "genetic algorithms, genetic programming, Learning Classifier Systems, LCS",
-
URL = "
http://hdl.handle.net/1842/27352",
-
URL = "
https://era.ed.ac.uk/bitstream/handle/1842/27352/SchulenburgS_2003redux.pdf",
- size = "281 pages",
-
abstract = "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, the concept of continual learning is addressed, and to show how the model constantly adapts to new market behaviour, additional experiments which include a number of real stocks are presented, followed by a discussion section.",
- notes = "Mentions GP particularly in future work, mostly LCS",
