應用類神經網路於選擇權價格預測

Applying Neural Network to Forecast the Option Prices

林隆啟(Lung-Chi Lin)

指導教授 ： 廖光彬

崑山科技大學/商業管理學院/企業管理研究所/碩士(2005年)

https://doi.org/10.6828/KSU.2005.00019

摘要

本研究使用倒傳遞(Back-propagation)類神經網路，來對選擇權之價格進行預測準確度研究。經由我們的實驗結果發現，輸入節點數、隱藏元節點數、學習速率這三個参數選定的優良與否，會影響倒傳遞類神經網路(BPN)的預測能力。我們使用MAPE和MSE比較了本研究所得出之最佳類神經網路模式，並將之排名，所得出的排名結果相同。
在我們的實驗中，以2個輸入節點、1個隱藏元節點、學習速率為1時，針對本研究所選取之資料，其預測的表現是最好的，但這並不代表此類神經網路模式適用於所有情形；本研究所要突顯的是，類神經網路参數的選定是影響其預測表現的重要因素，故選擇適當的類神經
網路参數組合，對於類神經網路的預測能力是相當重要的因素。

關鍵字

類神經網路 ； 倒傳遞類神經網路 ； 股票選擇權

並列摘要

This study attempts to evaluate the option price forecasting accuracy of back-propagation neural networks. Our experimental results show that the choices of the number of input nodes, the number of hidden nodes, and the learning rate can affect the forecasting capability of a back-propagation neural network. No matter whether MAPE or MSE is used to rank the performances of the neural networks built, the ranking is the same.
Based on the data used in this experiment, the forecasting performance of a neural network is best when two input nodes, one hidden nodes, and a learning rate of 1 are used. But this does not mean this combination of parameters can be applied in other circumstances. What the experiment demonstrates is that the choice of neural network parameters is an important factor in influencing forecasting performance. Consequently, choosing suitable neural network parameters can be a crucial factor in improving the forecasting capability of a neural network.

並列關鍵字

Neural Networks ； Back-propagation Neural Networks ； Stock Options

參考文獻

[2]Black, F. and Scholes, M., “The pricing of options and corporate liabilities”, Journal of Political Economy, Vol.81,1973, pp.637-659.

[4]McCulloch, W.S. and Pitts,W., “ A Logical Calculus of Ideas Immanent in Nervous Activity” , Bull. Mathematical Biophysics, Vol.5, 1943 , pp.115-133.

[5]Rosenblatt, F., “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain”, Psychological Review, Vol.65, 1958, pp.386-408.

[6]Rosenblatt, F., “Principles of Neurodynamics”, New York: Spartan books, 1962.

[10]Gray , G. and L. Osburn , “Forecadting S&P and Gold Furtures Price :An Application of Neural Networks.” The Journal of Futures Markets, Vol.13, 1993, pp.634~643.