結合基因演算類神經網路預測台股指數之模型

A TAIEX Forecasting Model with A Combination of Genetic Algorithms and Neural Networks

洪才元(Tsai-Yuan Hung)

指導教授 ： 林志浩

中原大學/商學院/資訊管理學系/碩士(2008年)

https://doi.org/10.6840/cycu200900497

摘要

台股指數預測研判會讓投資人對相對應商品損益產生影響，故其落點的研判是投資者關心中短線佈局與買賣台股指數期貨及指數選擇權的重點所在，通常大多投資人是憑藉的自我經驗決定，或是採由簡單的統計方法輔助計算來預測大盤的走勢，且不易評估指數落點，在預測的過程中存在許多不確定的因素問題及需要熟悉該領域的專家知識，使得僅用單一利用統計方法求得的結果並不是那麼的理想，然而預測指數有好幾種方式，而在人工智慧方式裡面最常見為應用類神經網路來作預測，有相當程度的成功，但其指數預測結果與昨日實際指數差距的幅度不大，與現實股市指數波動有差距，尤其在波段漲跌變化激烈的時候其誤差更大，所以本研究提出ㄧ個透過系統模擬方式預測台股指數之模型，應用倒傳遞類神經網路來建構人工智慧預測模組結合經驗法則來加大預測幅度，並用基因演算法找出經驗法則參數以求近似最佳解，以減少與實際指數的誤差，並對其他文獻實驗及一般類神經網路預測之所得指數誤差之比較，證實該模組預測結果為佳，能加大預測指數的落點幅度，並減少誤差，藉著這預測模型之建立，以期能幫助個別投資人減少專家知識與市場資訊的分析，以做有利的投資決策，而即使無高獲利，至少避免損失，因此正確預測指數，降低投資風險，增加獲利能力，是所有投資人所追求。

關鍵字

倒傳遞類神經網路 ； 股市 ； 基因演算法 ； 預測 ； 台股指數

並列摘要

The forecast for the trend of Taiwan stock index should have an impact on the investors toward the relative market price. Based on the forecast of the pivot of market price, the investors can decide their plan about short-term market, futures or index options. However, there are several ways to forecast stock index. Neural networks have been proposed for modeling nonlinear data. In the artificial intelligence field, researchers frequently use neural network to forecast stock index, but the results usually cause a slightly price gap when the market encounters a huge stock shake. Therefore, this study uses a system simulation model to do the forecast the Taiwan stock index. We combine the genetic algorithms and the back-propagation neural network to construct artificial intelligence forecasting model. It also combines the experience rules to enhance the precision of the proposed model. As a result, the forecast of this models can increase the breadth of market trend as compared with the traditional neural network. It increases the accuracy rate, and reduces gaps. By using this model, investors can decide when and where to invest their money. Therefore, the model will be quite appealing if we can predict the market behaviour accurately. Furthermore, this prediction model can help individual investors to determine the correctness of expert’s knowledge and market research report in order to make a beneficial investment decision. Therefore, this forecasting model is important for investors to avoid investment risks and enlarge high-profit abilities on stock investment.

並列關鍵字

forcasting ； stock ； TAIEX ； Genetic algorithms ； back-propagation network

參考文獻

6. 李沃牆、張克群（2006），比較不同波動率模型下台灣股票選擇權之評價績效，真理財經學報，14期，71-96。

19. 陳執中（2006），台灣加權指數隔月收盤之研究，成功大學統計系碩士論文。

27. Abhyankar, A. (1995), “Return and Volatility Dynamics in the FT-SE l00 Stock Index Stock Index Futures Markets,” Journal of Futures Markets, Vol.15, 457-486.

28. Brownstone, D. (1996), “Using percentage accuracy to measure neural network predictions in Stock Market movements,” Neurocomputing, Vol. 10, 237-250.

29. Cochrane, J.H. (1991), “Production-based Asset Pricing and the Link between Stock Returns and the Economic Fluctuations,” Journal of Finance, Vol. 46, Iss. 1, 209-237.