應用類神經網路探試未平倉量於台指期貨之多空行為分析

Abstract

本研究以民國96/07/02~98/03/03台灣期貨交易所公佈之 “期貨市場三大法人交易情形統計表” 日資料，及“期貨大額交易人未沖銷部位結構表” 日資料，為研究樣本，利用自組織映射圖神經網路及倒傳遞神經網路，研究台指期、現貨及選擇權未平倉等與台股期貨指數漲跌之關係。

經文獻探討發現，台灣股票投資市場為一弱勢效率市場，此市場確實有隻看不見的手，在主導整個市場走向，許多研究顯示機構投資人常利用其投資部位大的優勢，左右市場多空趨勢，並以避險及套利的操作策略從中牟取超額利潤。本研究希望透過台灣期貨交易所目前提供三大法人及大額交易人的交易資訊與未平倉資訊等籌碼面因素，來發現機構投資人及大額交易人之多、空買賣力分析，希冀由此能預測台股期貨指數漲跌趨勢。

實證結果顯示，三大法人及大額交易人未平倉量之籌碼面因素，可預測台灣期貨指數趨勢。本研究亦證實自組織映射圖神經網路與倒傳遞類神經網路的結合，對於以期貨選擇權未平倉量，預測期貨指數趨勢之準確度優於倒傳遞類神經網路，另外亦發現適當的門檻值設置確實可穩定投資績效表現。

This study investigates the relationship between TAIEX futures and open interest of futures and options, using an integration of self-organizing map and back-propagation neural network-based trading model. The data of the trading volume and open interest of three legal persons , and the open interest of large futures traders are based on Taiwan Futures Exchange, which cover from July 2, 2007 to March 3, 2009.

The review of literature summarizes that Taiwan stock market have the weak form efficiency. Moreover, many researches imply that institutional investors earn significant abnormal profit by exploiting their large investments, which could cause a change in the market trend. The purpose of this research is to analyze the trading behavior of institutional investors and large traders, and make further predictions of Taiwan stock index futures trend.

The empirical results indicate that the open interest of three legal persons and large traders can be a factor of forecasting Taiwan stock index futures, besides we find that the integrate model of self-organizing map and back-propagation neural network is superior to the back-propagation neural network model, and determining the right threshold can lead to profitable growth in investments.