Predicting the stock price based on the hybrid DOE-based optimization of neural networks

• Main Authors: Hui-Mei Wei, 危惠美
• Other Authors: Min-Hsuan Fan
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/mfm3ea

碩士 === 國立臺中科技大學 === 資訊管理系碩士班 === 100 === In recent year, people are is suffering from the raising of commodities prices, which is caused by oil price and electrical price rising at the same time. Under the pressure of inflation and low saving interest rate, how to make money and accumulate fortune by investment has become the main concern of people’s everyday life. Among the financial commodities, the stock is one investment tool which investors can get the public information more easily. However, the goal for investment is to make great profit by the lowest cost. Predicting price activities in stock market on the basis of either professional knowledge or stock analytical tools have been a great concern of individual and institutional investors around the world, because price variations result in gains and losses for investors. Stock return predictions are at the core of many research issues too. The stock forecasting model which based on NN models has been increasing. The goods of NN’s applications on empirical research studies are high accuracy rate of forecasting. Besides, the application of NN will not be constrained by the assumption of normality and it can deal with the non-linear distributions. The effect of a network’s functional approach depends on the network architecture, parameters, and problem complexity. If inappropriate network architecture and parameters are selected, the results may not be desirable. On the controversy, the results will be more significant if good network architecture and parameters are setting. Researchers set the parameters of ANNs intuitively or by trial-and-error processes to obtain the results. It is time and money consumption, besides the parameters setting won’t get the best result. In this study, adopting the financial data and the applying of ANNs was proposed. Besides the application of experimental design and through the process of main effect analysis and interaction analysis, the best parameters for the ANN model can be found. The research result shows that by the method applied in this research the correlation coefficient can improve to be 0.93 and 0.87 which is much better than the result by try-and-error. The research method applied in the research was approved to improve the accuracy rate of stock price forecasting.