This paper investigates methods to evolve an automated agent that executes a niche trading stock strategy. Unlike trading strate- gies that seek to exploit broad market trends, we choose a very specific strategy on the assumption that it will be easier to learn, require less input data to do so, and more straightforward to evaluate the agents performance. In this case, we select a Low Price Recovery Strategy (LPRS), which involves picking stocks that have a high likelihood of quickly recovering after a steep, one day decline in share price. A series of intelligent agents are evolved through the use of a Genetic Program- ing approach. The inputs to our algorithms included traditional stock performance metrics, sentiment indicators available from online sources, and associated classification rules. The essential aspects of the research discussed include: identification of opportunities, feature selection and extraction, design of various genetic programs for evolving the agent, and testing approaches for the agents. We demonstrate that the evolved agent yields results outperform a randomized version of the LPRS and the benchmark Standard & Poor's 500 (S&P500) stock market index.
['Robert E. Marmelstein', 'Alexander L. Hunt', 'Christoper Eroh']
Evolving a Low Price Recovery Strategy for Distressed Securities