C4.5: programs for machine learning
C4.5: programs for machine learning
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Neural Networks in the Capital Markets
Neural Networks in the Capital Markets
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Adaptive stock trading with dynamic asset allocation using reinforcement learning
Information Sciences: an International Journal
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In the context of a dynamic trading environment, the ultimate goal of the financial forecasting system is to optimize a specific trading objective. This paper presents a two-phase (extraction and filtering) stock trading system that aims at maximizing the rates of returns. Extraction of stocks is performed by searching specific time-series patterns described by a combination of values of technical indicators. In the filtering phase, several rules are applied to the extracted sets of stocks to select stocks to be actually traded. The filtering rules are induced from past data using distributional differences. From a large database of daily stock prices, the values of technical indicators are calculated. They are used to make the extraction patterns, and the distributions of the discretization intervals of the values are calculated for both positive and negative data sets. The values in the intervals of distinctive distribution may contribute to the prediction of future trend of stocks, so the rules for filtering stocks are induced using those intervals. We show the rates of returns by the proposed trading system, with the usefulness of the rule induction method using distributional differences.