Stock trading using XCS

Abstract

Stock trading is a complex process which is subject to distinct events both inside and outside an organization. An increase in revenue is a direct influence which causes the stock price to move upwards; likewise, the price of a stock fluctuates due to indirect influences. For example, a stock’s price may move upwards due to a firm arriving at beneficial deals or adding eminent professionals to its board of directors; a rise in correlated stock prices. The stock prices are affected to a great extent by statements of the finance minister and other related officials. In addition, subjective judgments and emotions of traders can also influence the variation of indices and stock prices in the market. The efficient market hypothesis proposes that stock price is unpredictable, assuming all past information has been influenced on current price and therefore it is not useful for the prediction of future price. Nevertheless, there are opposing theories which state that stock prices are predictable through the identification of trends and price patterns based upon past data such as price and volume quotes, balance sheets, and income statements. In the stock market, naive traders (or investors) assume risks due to the above uncertainties, but still have opportunities to make profits through proper, in-depth analysis on sufficient quantities of past data. There are many indicators that are accessible and can help predict the direction of future prices or index values using fundamental and technical data. Fundamental data, derived from the balance sheets and income statements, is preferred for mid-term and long-term investors but not suitable for short-term investors; meanwhile, technical data can be used for short-term investors as well. Technical data is preferred for short-term investments but it can also be used for long-term investments by choosing a specific window of time to look back in determining the indicators for long periods. The current trading model for stocks and indices was developed using an accuracy-based learning classifier system (XCS), which combines reinforcement learning, genetic algorithms, and other heuristics to form an adaptive system whose purpose is to execute stock trades for profit. A test bed developed for experimenting with this system consists of technical data, with candidate features chosen as the most popular indicators.