Stock Data Analysis Based on BP Neural Network

• Published in: 2010 Second International Conference on Communication Software and Networks pp. 396 - 399
• Main Authors: Zhou Yixin, Jie Zhang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.02.2010
• Subjects: BP neural network; Buildings; Data analysis; Data mining; Data Mining Alogorithm; Fluctuations; Neural networks; Prediction algorithms; Predictive models; Smoothing methods; Statistical analysis; Stock Market Forecasting; Stock markets; Technical Indicators
• ISBN: 1424457262; 9781424457267
• DOI: 10.1109/ICCSN.2010.12

In this paper, we apply data mining technology to Chinese stock market in order to research the trend of price, it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier structure of the neural network, namely input layer, hidden layer and output layer. After building the data pre-processing set before data mining, lots of widely used stock market technical indicators such as the KD indicators, similarities and differences between exponential smoothing moving average MACD, Relative Strength Index RSI, will be introduced into the model. Finally, we get a better predictive model to improve forecast accuracy.