運用數學模式來對股價指數作預測

Abstract

本論文運用決策向量機器(SVM)來進行股價指數走向的預測。本文的主要目的在於驗證SVM預測股價指數走向的準確度，並設計一套有效的交易策略，並評估其獲利率。我們以台灣加權指數期貨作為實驗標的,資料由台灣期貨交易所取得。所取得的資料將之轉換成數日之相對變動百分比來表示指數走向的樣式。結果顯示用SVM的交易策略優於買進持有策略。

This thesis deals with the application of a novel neural network technique, Support Vector Machine (SVM), in stock indices movement prediction. The purpose of this thesis is to demonstrate and verify the predictability of stock index direction using SVM, to develop effective trading strategies and to test the relative performance. A real future contract (Taiwan Stock Exchange Capitalization Weighted Stock Index) collected from Taiwan Futures Exchange is used as the data set. The series of relative difference in percentage of price (RDP) is adopted as the input variables to describe the patters of market movement. Results indicate that the technique is capable of returning results that are superior to those attained by buy-and-hold strategy.