The Research Of Stock Trading Points Prediction Based On Bicluster Mining

The stock market is not only the barometer of national economy,but also an important means of individual investment and corporate finance.Effective forecast of stock price trend can guide investors to make reasonable investment decisions,moreover,it provides a reference for the country to develop relevant economic policies.Therefore,stock forecasting is a key research direction in the field of financial forecasting,and has great practical significance and application value.However,the stock market is a complicated dynamic system.It is affected by many uncertain factors,making the fluctuation of stock price presents a strong nonlinear characteristics which greatly increases the difficulty of stock prediction.In order to realize the prediction of stock price trend,we need to investigate the complex rules of stock market based on the processing of a large amount of data information.As it is known,data mining enables the reveal of hidden information behind the massive data,which provides a novel method for stock prediction.The key to the stock prediction is to construct appropriate prediction model.The fuzzy inference system has strong nonlinear mapping ability,which can approximate the complex nonlinear relationship with arbitrary precision,leading to the unique rationality and applicability in constructing a prediction model.Thus,for the purpose of stock price trend prediction,we propose two different forecasting methods based on data mining technique and fuzzy inference system.Due to the volatility of stock price,coupled with the difficulties of fuzzy inference system on stock predicting,such as the difficulty in obtaining rules,the inability to generate rules automatically,and the lack of objectivity and justice of the rules,this paper presents a forecasting method of trading points based on biclustering and fuzzy inference.Firstly,using the biclustering technology to extract the laws and valuable information from historical data of the stock,the extracted information will be treated as an expert knowledge to build fuzzy rules automatically.After that,fuzzy inference is employed to output reasoning results which will be finally transformed to the prediction of stock price trend according to a dynamic threshold module constructed by particle swarm optimization algorithm.On the other hand,to forecast the stock price trend is to classify the trend into rising or falling type.Owing to the complex change rules of stock price,a single classifier performs poorly since it is not able to describe the characteristics of the entire data set.Consequently,this paper proposes a hybrid forecasting model based on naive Bayes and Adaboost.At first,using the biclustering algorithm to extract the pattern of stock price trend,then combining the rising patterns and falling patterns to construct weak classifiers by naive Bayes,finally the Adaboost algorithm is used to combine multiple weak classifiers into a strong classifier,which could improve the accuracy of classification.Moreover,considering that different weak classifier is composed of different trend patterns,the diversity of the weak classifiers can be ensured,resulting in the enhancement to the generalization ability of the strong classifier.In order to verify the rationality and effectiveness of the proposed methods,several comparative experiments have been designed.The experimental results show that the proposed methods obtain higher average return rates both in stock and stock index when compared with other methods...