Stock Price Prediction Based on Fuzzy Logic
Zhejiang Univ., Hangzhou
DOI: 10.1109/ICMLC.2007.4370347 Conference: Machine Learning and Cybernetics, 2007 International Conference on, Volume: 3
Stock markets are complex. Their dramatic movements, and unexpected booms and crashes, dull all traditional tools. The major concern of the study is to develop a system that can predict future prices in the stock markets by taking samples of past prices. The model elicits, from historical data price, some of the rules which govern the market, and shows that rules which are drawn from a particular stock are to some extent independent of that stock, and can be generalized and applied to other stocks regardless of specific time or industrial field. The experimental results of this study in the duration of 3 months reveal that the model can correctly predict the direction of the market with an average hit ratio of 87%. In addition to daily prediction, this model is also capable of predicting the open, high, low, and close prices of desired stock weekly and monthly.
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