یک مدل ترکیبی بر اساس مبتنی بر سیستم تطبیقی ـ​​ شبکه ای استنتاج فازی برای پیش بینی بازار سهام تایوان

In recent years, many academy researchers have proposed several forecasting models based on technical analysis to predict models such as Engle, 1982 and Cheng et al., 2010. After reviewing the literature, two major drawbacks are found in past models: (1) the forecasting models based on artificial intelligence algorithms (AI), such as neural networks (NN) and genetic algorithms (GAs), produce complex and unintelligible rules; and (2) statistic forecasting models, such as time series, require some basic assumptions for variables and build forecasting models based on mathematic equations, which are not easily understandable by stock investors. In order to refine these drawbacks of past models, this paper has proposed a model, based on adaptive-network-based fuzzy inference system which uses multi-technical indicators, to predict stock price trends. Three refined processes have proposed in the hybrid model for forecasting: (1) select essential technical indicators from popular indicators by a correlation matrix; (2) use the subtractive clustering method to partition technical indicator value into linguistic values based on an data discretization method; (3) employ a fuzzy inference system (FIS) to extract rules of linguistic terms from the dataset of the technical indicators, and optimize the FIS parameters based on an adaptive network to produce forecasts. A six-year period of the TAIEX is employed as experimental database to evaluate the proposed model with a performance indicator, root mean squared error (RMSE). The experimental results have shown that the proposed model is superior to two listing models (Chen’s and Yu’s models).

For participants in stock market, technical analysis method is one of major analysis techniques in stock market forecasting that has the ability to forecast the future price direction by studying past market data, primarily stock price and volume. The technical analysis method has assumed that stock price and volume are the two most relevant factors in determining the future direction and behavior of a particular stock or market, and the technical indicators, come from the mathematic formula based on stock price and volume, can be applied to predict the future price fluctuation and also provided for investors to determine the timing of buying or selling the stocks (Chi, Peng, Wu, & Yu, 2003). For stock analysts and fund managers, using technical indicators to analyze stock market is a practical way, but it is hard to apply this technique for common investors because there are too many technical indicators to be considered as forecasting factors and most of popular indicators are usually not understandable. Therefore, for those stock market investors, who utilize technical indicators to predict market fluctuations, how to select useful technical indicators to forecast stock price trends accurately is the key issue to make profit. In academy research, many time-series models was advanced by financial researchers to model stock market based on historical stock data, such as autoregressive conditional heteroscedasticity (ARCH) model by Engle (1982), ARCH (GARCH) model by Bollerslev (1986), autoregressive moving average (ARMA) model, and the autoregressive integrated moving average model (ARIMA) by Box and Jenkins (1976). As the arising of intelligent algorithms in recent years, many researchers have applied soft computing (Zadeh, 1994) algorithms in time-series model for financial forecasting. Kimoto, Asakawa, Yoda, and Takeoka (1990) developed a prediction system for stock market by using neural network. Nikolopoulos and Fellrath (1994) have combined genetic algorithms (GAs) and neural network (NN) to develop a hybrid expert system for investment decisions. Kim and Han (2000) proposed an approach based on genetic algorithms to feature discretization and the determination of connection weights for artificial neural networks (ANNs) to predict the stock price index. Huarng and Yu (2006) applied a backpropagation neural network to establish fuzzy relationships in fuzzy time series for forecasting stock price. And, Roh (2007) has integrated neural network and time series model for forecasting the volatility of stock price index. After reviewing the past models, three major drawbacks are found: (1) stock market analyst and fund managers apply various technical indicators to forecast stock market based on personal experience, which might give wrong judgments on market signals; (2) for some statistical models, specific assumptions are required for observations, and those models cannot be applied to the datasets that do not follow the statistical assumptions; and (3) some soft computing algorithms, such as neural networks (NN) and genetic algorithms (GAs), contain complex computation procedures like black-box, and the rules mined from these algorithms are not easily understandable for common invertors. To improve the past forecasting models, this paper proposes a hybrid forecasting model to refine past models in stock price forecasting. The proposed model utilizes technical indicators as forecasting factors and an intelligent inference system as forecasting algorithms that can offer understandable rules for common investors. Three main processes are provided in the model as follows: (1) selects essential technical indicators from popular indicators with a “correlation matrix”; (2) use the subtractive clustering method (Chiu, 1994) to granule the dataset of essential technical indicators into linguistic stock dataset and apply a fuzzy inference system (FIS) to extract non-linear relationships (rules) among the linguistic stock dataset; and (3) employ an adaptive network to optimize FIS parameters to improve forecasting accuracy and produce understandable forecasting rules. To verify the performance of the proposed model, this paper employs a six-year period of the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) as experimental dataset, and two fuzzy time-series models (Chen, 1996 and Yu, 2005) as comparison models.

This paper has proposed a new hybrid model, which employs technical indicators as forecasting factors and three novel methods (correlation matrix, subtractive clustering, and ANFIS) in forecasting processes, to promote prediction performance in stock market. From the experimentation and forecasting results, three findings are given in this paper in the followings. (1) Experimental results (Table 3) have shown that the proposed model outperforms two listing fuzzy time-series models (Chen’s and Yu’s models) numerically in RMSE. It can be explained by that the employing useful technical indicators as forecasting factors, which are highly related to the future stock index, can promote forecasting accuracy. Besides, we argue that the ANFIS model can efficiently reduce forecasting error because the forecasting rules are optimal in training dataset. (2) From Table 3, it is clear that the proposed model bears smaller performance variation (standard deviation = 31) performs more stably than the two listing models (Chen’s = 44; Yu’s = 39). The main reason we argue is that the backpropagation architecture of ANFIS model can produce optimal forecasting rules in training dataset to make the forecasts for the future stock index more reliable. (3) The proposed model can produce fewer forecasting rules to forecast stock market. In the experimentation using the TAIEX as datasets, only two rules are generated by the proposed model. In the forecasting processes, the subtractive clustering method is utilized to granule conditional attributes (technical indicators) and it reduces probably the amount of FIS rules. Additionally, from the forecasting rules generated by the proposed model, two advantages are discovered: (1) Reasonable and understandable rules, “if-then” rules, produced by ANFIS can model the qualitative aspects of human knowledge. (2) The forecasting rules based on objective stock data rather than subjective human judgments can provide stock investors objective suggestions (forecasts) to make decisions. In the future works, the model can be verified by other stock market such as China, Japan and Hong Kong. Besides, there are two suggested approaches to refine the proposed model to improve forecasting performance: (1) validate the generated rules by financial experts or stock analysts to improve accuracy; and (2) apply other data discretization methods in preprocessing phase to evaluate the performance variation.