Analyzing TSEC Weighted Index Based on Technical Analysis and Back- Propagation Neural Network

• Main Authors: Liu, Rui-Ting, 劉瑞婷
• Other Authors: Chen, An-Pin
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/29040604131440162167

碩士 === 國立交通大學 === 管理學院資訊管理學程 === 102 === In financial markets, the profit gained from a positive reward of trading. Base on this concept, if the mode focus only on the spreads, not only can reduce the risk of the transaction (from spreads itself), can also simplify the complexity of the model. Therefore, this study attempts to gather the information by using the technical analysis, combining form back propagation neural network learning, and training and predict to find the worded-trade points. This paper attempts to classify the price wave into two categories- the Not Well-formed and the Well-formed. The Not Well-formed wave during its life cycle only has the wave critical point and has no moving average crossing point, but the Well-formed wave has both. Because of the wave critical point happened earlier than the moving average crossover point, we may think that the time value of the wave critical point will bring more profit than the moving average crossover point. On the other hands, the moving average crossing point in the Well-formed will have more complete information in trend, momentum, and pattern, thus improving the forecasting accuracy. Therefore, designing the two trading strategy -the strategy X (including two time points), and the strategy Y (including Not Well-formed parts only), and compare both strategies performance. The results show that the accuracy rate between the two points has no significant which breaks the time value loss hypothesis. This is because the mode has learned from the patterns and improved the accuracy rate on the moving average crossover points. Basing on this conclusion, checking on the annualized rate of return, the average gained point per trade, and the number of transactions between two trading strategies, the result shows that the wave critical point has better performance than the moving average crossing point, which means that the BPNN can learn and shield the uncertainties at earlier trade point, thus can gain more profit in earlier trade point safely.