未來研究建議

根據本研究的實驗結果與研究歷程之經歷提供下以之未來研究方向建議：

1. 本研究處理之資料僅限於 2007 年 7 月 18 日至 2010 年 3 月 17 日，樣本數目 不是很多，可能沒有辦法涵蓋所有不同情況的時空背景。尤其此段樣本期間剛好 有一半以上的時間是處在市場崩跌的金融海嘯，以此樣本學習到的知識規則所建

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立的模型，可能會特別適合某些時空環境。未來若研究者，若能補齊更長時間的 樣本資料，應能更增進模型的一般性。

2. 本研究僅提供台股期貨及選擇權賣方兩種交易策略，事實上，選擇權的各種 組合策略非常多樣，舉凡多、空頭價差或是跨式、勒式、蝶式等等，甚至選擇權 與期貨的交叉組合運用，都可依投資者的需求加以使用，另外包含停損、停利或 資金控管的策略，也可更優化交易的績效並且降低風險。未來的研究可著眼在不 同交易策略的實證，以提供投資人更多不同的選擇。

3. 研究模型方面，未來可以考慮加入分群的方法學，例如自組織映射神經(SOM)，

以幫助找出模型最適合之時空環境，以更提升預測的準確程度。

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參考文獻

英文參考文獻

[1] Albert S. Kyle, “Continuous Auctions and Insider Trading”, Econometrica, Vol.

53, No. 6, p. 1315, 1985.

[2] Ali F. Darrat, Shafiqur Rahman, “Has Futures Trading Activity Caused Stock Price Volitility?”, The Journal of Futures Markets, Vol. 15, No. 5, pp. 537-557, 1995.

[3] An-Pin Chen, Yu-Chia Hsu, “Dynamic Physical Behavior Analysis for Financial Trading Decision Support”, IEEE Computational Intelligence Magazine, Vol.5, Issue. 4, 2010.

[4] An-Sing Chen, et al., “Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index”, Computers &

Operations Research, Vol. 30, pp. 901-923, 2003.

[5] Bing Han, et al., “Investor Trading Behavior and Performances: Evidence from Taiwan Stock Index Options”, McCombs Research Paper, 2009.

[6] Brad M. Barber, et al., “Just How Much Do Individual Investors Lose by Trading?”, The Review of Financial Studies, Vol. 22, No. 2, 2009.

[7] Brad M. Barber, et al., “Who Gains from Trade? Evidence from Taiwan”, Working Paper, Univ. of California-Berkeley, 2004.

[8] C Tseng, et al., “Artificial Neural Network Model of the Hybrid EGARCH Volatility of the Taiwan Stock”, Physica A: Statistical Mechanics and its Application, Vol. 387, Issue. 13, pp. 3192-3200, 2008.

[9] C. Quek, et al., “A Novel Recurrent Neural Network-based Prediction System for Option Trading and Hedging”, Applied Intelligence, Vol. 29, No. 2, pp.

138-151, 2007.

[10] Chien-Cheng Lee, et al., “Federal Funds Rate Prediction: A Comparison Between the Robust RBF Neural Network and Economic Models”, Journal of Information Science and Engineering, Vol. 25, pp. 763-778, 2009.

[11] Fama, E. F. "Efficient Capital Markets: a Review of Theory and Empirical Work." Journal of Finance, vol.25, pp.383-417, 1970.

59

[12] Freeman,et al., Neural Network Algorithms, Applications, and Programming Techniques, New York: Addison-Wesley Publishing Company, 1992.

[13] Hendrik Bessembinder, et al., “An empirical Examination of Information, Differences of Opinion, and Trading Activity”, Journal of Financial Economics, Vol. 40, pp. 105-134, 1996.

[14] Hendrik Bessembinder, Paul J. Seguin, “Futures-Trading Activity and Stock Price Volatility”, The Journal of Finance, Vol. XLVII, No. 5, 1992.

[15] Howard Memuth, et al., "Neural Network Toolbox 6 User's Guide", The MathWorks, 2010.

[16] Hsiang-Lin Chang, “Forcasting Taiwanese Stock Market Based on the Open Interest on the Futures Option”, National Cheng Kung University, International Master of Business Administration Master’s Thesis, 2007.

[17] Jay Desai, et al., “Forecasting of Indian Stock Market Index S&P CNX Nifty 50 Using Artificial Intelligence”, FEN Subject Matter e Journals: Behavioral &

Experimental Finance e Journal, Vol. 3, Issue. 79, 2011.

[18] Kedar nath Mukherjee, R. K. Mishra, “ Impact of Open Interest and Trading Volume in Option Market on Underlying Cash Market: Empirical Evidence from Indian Equity option Market”, The ICFAI University, India, 2004.

[19] Kyoung-Jae Kim, Ingoo Han, “Genetic algorithms approach to feature

discretization in artificial neural networks for the prediction of stock price index”, Expert Systems with Applications, Vol. 19, pp. 125-132, 2000.

[20] Lonnie Hamm, B. Wade Brorsen, “Trading Futures Markets Based on Signals from a Neural Network”, Applied Economics Letters, Vol. 7, No. 2, pp. 137-140, 2000.

[21] Mark Broadie, et al., “Understanding Index Option Returns”, Review of Financial Studies, Vol. 22, Issue. 11, pp. 4493-4529, 2009.

[22] Nai-Fu Chen, et al., “Stock Volatility and the Levels of the Basis and Open Interest in Futures Contracts”, The Journal of Finance, Vol.50, No. 1, pp.

281-300, 1995.

[23] Oleg Bondarenko, “Why are Put Options So Expensive”, AFA San Diego Meetings, University of Illinois at Chicago Working Paper, 2003.

60

[24] Philip M. Tsang, et al., “An Empirical Examination of the Use of NN5 for Hong Kong Stock Price Forcasting”, International Journal of Electronic Finance, Vol.

1, No. 3, 2007.

[25] Rafiqul Bhuyan, Mo Chaudhury, “Trading on the Information Content of Open Interest: Evidence from the US Equity Options Market”, Derivatives, Vol. 11, No. 1, pp. 16-36, 2005.

[26] Robert R. Trippi, Duane. Desieno, “Trading Equity Index Futures with a Neural Network- a Machine-Learning Enhanced Trading Strategy”, Journal of Portfolio Management, vol. 19, No. 1, pp. 27-33, 1992.

[27] Rumelhart, D. E., et al., “Learning internal representations by error propagation.

In Parallel Distributed Processing: Explorations in the Microstructure of Cognition”, Vol. 1, Cambridge, pp. 318-362, 1986.

[28] Sandeep Srivastava, “Informational Content of Trading Volume and Open Interest – an Empirical Study of Stock Option Market in India”, NSE Research Initiative Working Paper, No. 29, 2003.

[29] Sanford J. Grossman, Joseph E. Stiglitz, “On the Impossibility of Informationally Efficient Market”, The American Economic Review, Vol. 70, No.3, 1980.

[30] William F. Sharpe, “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk”, The Journal of Finance, Vol. 19, No. 3, 1964.

[31] Stephen Figlewski, “Futures Trading and Volatility in the GNMA Market”, The Journal of Finance, Vol. 36, No. 2, 1981.

[32] Steven Walczak, “An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks”, Journal of Management Information Systems, Vol. 17, Issue. 4, 2001.

[33] Toyohide Watanabe, Kenji Iwata, “Estimation for Up/Down Fluctuation of Stock Prices by Using Neural Network”, Communications in Computer and

Information Science, Vol. 49, Part 2, pp. 171-178, 2009.

[34] Vatcharaporn Esichaikul, Pongsak Srithongnopawong, “Using Relative Movement to Support ANN-based Stock Forecasting in Tai Stock Market”, International Journal of Electronic Finance, Vol. 4, No. 1, pp. 84-98, 2010.

[35] Warren S. Sarle, et al., “Neural Networks and Statistical Models”, SAS Users Group International Conference, 1994.

61

[36] Zhang, G., Patuwo, B. E., & Hu, M. Y., "Forecasting with Artificial Neural Networks :The State of the Art", International Journal of Forecasting, Vo.14, pp.35-62, 1998.

[37] Zhe Liao, Jun Wang, “Forecasting Model of Global Stock Index by Stochastic Time Effective Neural Network”, Expert System with Application, Vol. 37, Issue.

1, pp. 834-841, 2010.

中文參考文獻

[38] 中國科學技術大學生物醫學工程跨系委員會，神經網路及其應用，儒林圖書 公司，1993 年。

[39] 呂惠甄，「外資買賣超對現貨與期貨市場之波動探討」，國立台北大學，合 作經濟系碩士論文，民國九十四年。

[40] 李新穎，「開放外資進入台灣資本市場管理政策之研究」，國立台灣大學，

財務金融研究所碩士論文，民國九十一年。

[41] 林可依，「台灣上市股市三大法人買賣超是否可提供投資人短期利機會?」，

國立東華大學，國際經濟研究所碩士論文，民國九十二年。

[42] 林昇甫，洪成安，神經網路入門與圖樣辨識，全華科技圖書公司，2002 年。

[43] 林佳蓉，「成交量與未帄倉量對期貨價格波動性之關聯性-台灣期貨市場之 實證」，國立成功大學，企業管理研究所碩士論文，民國九十二年。

[44] 林貞汝，「應用基因演算法及自組織映射圖神經網路對外資在台股指數期貨 持有成本之分析與大盤走勢行為知識發現」，國立交通大學，資訊管理研究 所碩士論文，民國九十八年。

[45] 林鈺綾，「三大法人選擇權與期貨未帄倉量之研究」，國立交通大學，資訊 管理研究所碩士論文，民國九十九年。

[46] 邵韻如，「台灣股市三大法人淨買賣超是否可提供投資人短期獲利機會？」，

國立東華大學，國際經濟研究所碩士論文，民國九十三年。

[47] 施柏屹，「倒傳遞類神經網路學習收斂之初步探討」，國立中央大學，機械 工程研究所碩士論文，民國八十九年。

[48] 馬黛等著，「期貨到期效應、資訊揭示與從眾行為：各國市場之實證研究」，

國立中山大學財務管理系，民國九十三年。

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[49] 陳朝治，「應用影像處理與類神經網路於 ITO 導電玻璃之瑕疵分類」，國 立台灣科技大學，自動化及控制研究所碩士論文，民國九十六年。

[50] 曾士育，「以自組織映射圖神經網路探勘金融投資決策之研究」，國立高雄 第一科技大學，資訊管理系碩士論文，民國九十二年。

[51] 葉怡成，類神經網路模式應用與實作，儒林圖書有限公司，民國九十二年。

[52] 葉鳳琴，「三大法人投資行為與加權股價指數互動關系之探討」，淡江大學，

財務金融學系金融碩士在職專班碩士論文，民國九十二年。

[53] 劉信良，「期貨市場-期貨及選擇權未帄倉量解讀」，建華投資月刊，一百 五十七期，七十二至七十四頁，民國九十三年。

[54] 廖彥豪，「三大機構投資人買賣超與台灣加權股價指數互動關係之研究」，

南華大學，財務管理研究所碩士論文，民國九十五年。

[55] 廖朝正，「期貨三大法人未帄倉部位與加權指數互動關係之研究」，銘傳大 學，財務金融學系碩士在職專班碩士論文，民國九十八年。

[56] 賴步昇，「台灣股票市場資訊揭示與投資人情緒反應的互動關係」，淡江大 學，管理科學研究所碩士論文，民國九十七年。

[57] 駱國華，「應用類神經網路探詴未帄倉量於台指期貨之多空行為分析」，國 立交通大學，資訊管理研究所碩士論文，民國九十八年。