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


Engineering Management and Systems Engineering

Keywords and Phrases

Stock Market; Forecasting Framework; Random Walk Benchmark; Timing Prediction

Document Type

Article - Conference proceedings

Document Version

Final Version

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© 2007 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.