There are fundamental difficulties when only using a supervised learning philosophy to predict financial stock short-term movements. We present a reinforcement-oriented forecasting framework in which the solution is converted from a typical error-based learning approach to a goal-directed match-based learning method. The real market timing ability in forecasting is addressed as well as traditional goodness-of-fit-based criteria. We develop two applicable hybrid prediction systems by adopting actor-only and actor-critic reinforcement learning, respectively, and compare them to both a supervised-only model and a classical random walk benchmark in forecasting three daily-based stock indices series within a 21-year learning and testing period. The performance of actor-critic-based systems was demonstrated to be superior to that of other alternatives, while the proposed actor-only systems also showed efficacy
H. Li et al., "Short-Term Stock Market Timing Prediction under Reinforcement Learning Schemes," IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning 2007, Institute of Electrical and Electronics Engineers (IEEE), Jan 2007.
The definitive version is available at http://dx.doi.org/10.1109/ADPRL.2007.368193
Engineering Management and Systems Engineering
Keywords and Phrases
Stock Market; Forecasting Framework; Random Walk Benchmark; Timing Prediction
Article - Conference proceedings
© 2007 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.