An Optimal Base Study of Futures Butterfly Trading Strategies- Genetic Algorithm with Artificial Neural Networks Application

• Main Authors: Wu, Po-Tsang, 吳柏蒼
• Other Authors: Dr. GOO, YEONG-JIA
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/sasa2s

碩士 === 國立臺北大學 === 企業管理學系 === 101 === Butterfly trading strategy is one of the most important methods to reduce the risk of the considerably high-risk futures exchange. Regression analysis or time series model utilized to achieve this purpose by most researchers in the conventional way. In this thesis, we attempt to integrate genetic algorithm with artificial neural network to forecast the price of Taiwan index futures（TX）of various expiry months. Moreover, we apply the principle of butterfly trading strategy to simulate the investment gains and losses. The period of study spans from 2003/03/16 to 2012/9/17 and the study includes 2360 pieces of daily data. The dependent variables are rate of return of five different futures from nearby month to far month. There are 54 variables are composes of six lag lengths of nine indices, which include rate of return, trading volume, the volume of open interest, 5-day K index、10-day K index、5-day D index、10-day D index、5-day bias & 10-day bias. The preliminary variable selection is done by stepwise regression. The training of genetic algorithm and artificial neural network developed in this study is completed via SAS-IML programming language and the trading information collected from the nine indices listed above. With moving window method, we are able to forecast the rate of return of Taiwan index futures of five expiry months on the thirty-first day based on the training result from the first day to the thirtieth day. We then exploit butterfly trading strategy to conduct an arbitrage by buying in the Taiwan index futures with the highest rising percentage of its expiry month and selling out the one with the lowest rising percentage of its expiry month. Via the same method, we forecast the rate of return of Taiwan index futures of five expiry months on the thirty-second day based on the training result from the second day to the thirty-first day. We then exploit butterfly trading strategy to conduct an arbitrage by buying in the Taiwan index futures with the highest rising percentage of its expiry month and selling out the one with the lowest rising percentage of its expiry month. The combinations of butterfly trading strategy are different on every business day predicted to invest because the expiry months of the highest and lowest rising percentages are not identical on every business day. In this study, we trade every day and day trade on that day. The prediction and trade on and after thirty-third day can be deduced by the same analogy. The accuracy rates are between 48%~60% when the base period is 180 days and the annualized return considered with transaction cost reaches 140.83% in trading strategy 1，and 75.92% in trading strategy 2. According to the data presented in this study, the effect of the genetic algorithm and artificial neural network is significant in the prediction for the rate of return of Taiwan index futures.