以遺傳神經網路建構台灣股市買賣決策系統之研究

Building Trading System for Taiwan Stock Market Using Genetic Neural Networks

連立川(Li-Chuan Lien) ； 葉怡成(I-Cheng Yeh)

《資訊管理學報》 15卷1期 (2008/01) Pp. 29-51

https://doi.org/10.6382/JIM.200801.0029

摘要

本研究採用台灣大盤股價指數及成交值所轉換的18個價量技術指標做為輸入參數，以期末資金最大化做為適應度函數，應用遺傳演算法(Genetic Algotithms)建構買賣決策類神經網路(Neural Networks)。研究結果顯示，本研究所比較之四種交易策略：遺傳神經網路策略(Genetic Neural Networks, GNN)、遺傳邏輯規則策略(Genetic Logic Rule, GLR)、單一遺傳邏輯規則策略(Single Genetic Logic Rule)及買入持有策略，在測試範例期間的平均年獲利率分別是10.27%、2.02%、-0.05%與-7.2%。另外由GNN、GLR及SGLR的風險評估得知，其高於買入持有策略的機率分別是91.77%、80.51%及79.39%。因此，GNN是一個穩定且有效的台灣股市交易策略。

關鍵字

股票市場 ； 技術指標 ； 遺傳演算法 ； 類神經網路

並列摘要

This paper used 18 kinds of price and volume technical indices transferred from the Taiwan stock price index as the input parameters, the maximization of the final capital as the fitness function, the genetic algorithms as the optimization tool to construct the trading system based on neural networks. The results showed that the four kinds of trading strategies, the Genetic Neural Networks strategy(GNN), the Genetic Logic Rule strategy(GLR), the Single Genetic Logic Rule(SGLR), and the buy and hold strategy, in test period produced the average year profit rate respectively are 10.27%, 2.02%, -0.05%, and -7.2. Moreover, compared the risk assessment of the GNN, GLR and SGLR strategies, the probability of average year profit rate higher than the buy and hold strategy rate respectively are 91.77%, 90.51%, and 79.39. Therefore, the GNN trading system is a not only effective but also stable Taiwan stock market of trading system.

並列關鍵字

Stock market ； technical index ； genetic algorithms ； neural networks

參考文獻

連立川、葉怡成、江宗原、楊豐銘(2006)。遺傳演算法建構台灣股市期貨買賣決策規則之研究。資管評論。14，47-61。

Allen, F.,Karlajainen, R.(1999).Using genetic algorithms to find technical trading rules.Journal of Financial Economics.51,245-271.

Armano, Giuliano(2002).Stock market prediction by a mixture of genetic-neural experts.International Journal of Pattern Recognition and Artificial Intelligence.16,501-526.

Cheh, J. J.,Weinberg, R. S.,Yook, K. C.(1999).An application of an artificial neural network investment system to predict takeover targets.Journal of Applied Business Research.15,33-45.

Glaria, B. A.(1996).Stock market indices in Santiago de Chile: forecasting using neural networks.IEEE International Conference on Neural Networks.4,2172-2175.