Integrating Implied Volatility and Technical Analysis for Taiwan Stock Index behavior analysis by using Artificial Neural Network

• Main Authors: Chien-Chih Liu, 劉建志
• Other Authors: An-Pin Chen
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/13595390731555448732

碩士 === 國立交通大學 === 資訊管理研究所 === 96 === In recent years, many literatures have discussed that whether or not stock price is predictable, and researchers use all kinds of investing strategies to optimize the investment in stock market. Among the methods of calculating stock price, Technical Analysis is a popular method in forecasting stock price. Moreover, some research also claimed that implied volatility reflects the investors’ expectation for the trend of the stock price. To verify the aforementioned claim, this study would probe the fluctuation of Taiwan Stock Market Index and implied volatility and determine whether the relations really exist. And then try to predict the trend of stock price with the relations. Technical indicators of moving average convergence-divergence are taken as input factors to predict next day's stock price trend. The input factor’s value of variance would be classified into four periods. The four periods were defined as falling substantially, falling slightly, backing and filling, and rising. In the four periods, input factors include the differences between implied volatilities calculated from the stock option put and call main series to predict the daily closing price and the closing price of one, two, five, and ten day(s) later. In the present, Neural Network is a novel artificial intelligence methodology which is widely applied to solve complex financial problems. Through the training of neural network, the result had shown that the accuracy of prediction and rate of annual profit were positive in terms of 10-day forecast. At the same time, prediction of falling trend was substantially better than the prediction of rising trend. The result of this experiment also shows that Neural Network is significantly more effective than random walk model and unclassified model. In this study, the use of the differences between implied volatilities in predicting the trend of stock price is meaningful. The methods used in this study provide investors a tool for analyzing the stock market and the option market.