Module Trading Signal for Robot Hedging Trading: The Case of Taiex Index Futures
Date Issued
2014
Date
2014
Author(s)
Chang, Ho-Lin
Abstract
According to the statistic of Taiwan Futures Exchange, the participation rate of local professional finance institution investors is less than 1%. The local investment trust companies who are one of the largest fund holder in Taiwan, their local equity fund size is approximately NT$ 261.2 billion, but their futures contract market value is less than 1% of the fund size. The insurance companies who are another of the largest fund holder invest local equity market size is approximately NT$ 1 trillion. Also counting along with “other institutional investor sectors” including the insurance, banking, bill finance, Chunghwa post and investment companies sectors, their participation rate is only 0.58%. How to raise these professional finance institutions participate rate in Taiwan futures market, from the view of hedging purpose in this study, we create a module trading signal platform which not only can hedge the investment portfolio but also facilitate the development of domestic futures market. Instead of using tradition hedging methods such as OLS, ARCH or GRACH econometric models to predict the pricing and calculate the hedging ratio, we used several program trading signals and genetic algorithms to determine the timing of long/short and ratio of hedging. We take two sample periods, one is inside and one is outside the sample period, and then compared the profit in each period with the effective hedging. Taking 1,000,000 shares of 0050 ETF as an equity portfolio, the return of inside sample period before and after short hedging is -393,480 and 52,976,120 respectively. The outside sample period before and after short hedging is 8,800,000 and 10,043,600 respectively. From the perspective of increasing investment return, using the bullish signal to enlarge the investment on 0050 ETF and combine with short hedging profit is 90,986,964. On the other hand, the profit on the outside sample period is 12,744,097. From the result of short hedging analysis, both inside and outside period show the return is better on before hedging, especially increasing the investment on bullish signal. The result of this study expected provides an idea, make the institutional investors to design their own program trading signal and allocate by genetic algorithms to fulfill their needs. This would increase the participation rate of futures market and also discover the real pricing in futures market.
Subjects
Hedging
Taiex Futures
Module
Genetic Algorithm
Robot Hedge Trading
Type
thesis