Abstract
The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a “data mining-based evolutionary fuzzy expert system” (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into sub-populations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for stock price forecasting problems.
Highlights
Forecasting is the process of making projections about future performance based on existing historic data
It is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting whole knowledge base of fuzzy expert system for stock price forecasting problems
data mining-based evolutionary fuzzy expert system’’ (DEFES) is equipped with data mining concept
Summary
Forecasting is the process of making projections about future performance based on existing historic data. We use a novel genetic fuzzy system (GFS) type based on a method proposed by Cordon and Herrera (2001) to extracting the whole KB of Mamdani type fuzzy rule-based system for each sub-population and construct expert system with the ability of simulating behavior of stock price. Crossover when the parents encode different rules: In this second case, it makes no sense to apply the previous operator because it will provoke the obtaining of disrupted descendents This fact is because the combination of two membership functions associated with different primary fuzzy sets makes the obtaining of two new fuzzy sets not belonging to the intervals of performance determined by the initial fuzzy partition. In the subsequent stages with Extraction of Mamdani type fuzzy rule-based system, Rule filtering and Genetic tuning process, it builds a DEFES for each cluster using related training data.
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