Abstract
To forecast a complex and non-linear system, such as a stock market, advanced artificial intelligence algorithms, like neural networks (NNs) and genetic algorithms (GAs) have been proposed as new approaches. However, for the average stock investor, two major disadvantages are argued against these advanced algorithms: (1) the rules generated by NNs and GAs are difficult to apply in investment decisions; and (2) the time complexity of the algorithms to produce forecasting outcomes is very high. Therefore, to provide understandable rules for investors and to reduce the time complexity of forecasting algorithms, this paper proposes a novel model for the forecasting process, which combines two granulating methods (the minimize entropy principle approach and the cumulative probability distribution approach) and a rough set algorithm. The model verification demonstrates that the proposed model surpasses the three listed conventional fuzzy time-series models and a multiple regression model (MLR) in forecast accuracy.
Highlights
Individual stock investors never stop dreaming of becoming wealthy by trading stocks
In order to avoid any possible intrusions of the model designer’s subjective predictions, based on technical analytical methods, one objective, automatic, artificial intelligence model is proposed, which combines three data mining techniques into forecasting processes: (1) minimize entropy principle approach (MEPA), which subdivides data into membership functions [14,15,16,17,18]; (2) cumulative probability distribution approach (CPDA), which fuzzifies the observations into linguistic values based on the cumulative probability of the observations [17,19,20]; and (3) rough set theory [17,19,21,22,23,24], which mines rules from the linguistic dataset
For the systems based on neural networks, three drawbacks are addressed: (1) there is little perceived reliability for neural-fuzzy systems because it is hard to determine whether the number of observations in a training dataset is adequate for forecasting; (2) the forecasting algorithms employing neural networks or genetic algorithms are not understood by the average stock investor; and (3) the neural-fuzzy technique is strictly quantitative and generalized to the point where human qualitative judgments are completely removed from the system [36]
Summary
Individual stock investors never stop dreaming of becoming wealthy by trading stocks. In order to avoid any possible intrusions of the model designer’s subjective predictions, based on technical analytical methods, one objective, automatic, artificial intelligence model is proposed, which combines three data mining techniques into forecasting processes: (1) MEPA (minimize entropy principle approach), which subdivides data into membership functions [14,15,16,17,18]; (2) CPDA (cumulative probability distribution approach), which fuzzifies the observations into linguistic values based on the cumulative probability of the observations [17,19,20]; and (3) rough set theory [17,19,21,22,23,24], which mines rules from the linguistic dataset. Objective and effective rules can be produced as the basis for forecasting
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