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    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/118755
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/118755

    Title: 利用隨機森林模型建構台灣指數期貨交易策略
    Constructing a TAIEX Futures Trading Strategy Using Random Forest
    Authors: 鄭仁杰
    Cheng, Jen-Chieh
    Contributors: 江彌修
    Chiang, Mi-Hsiu
    Cheng, Jen-Chieh
    Keywords: 機器學習
    Machine learning
    Ensemble learning
    Random forest
    Trading strategy
    TAIEX futures
    OOB error rate
    Calmar ratio
    Date: 2018
    Issue Date: 2018-07-19 17:26:12 (UTC+8)
    Abstract:   過去幾十年以來,預測金融商品價格走勢一直都是被熱烈討論的研究領域,但由於各種不同面向的因素交互影響,導致市場特性總是複雜且波動,價格走勢預測更加困難。普遍而言,錯誤率可被視為交易策略風險的指標,因此必須極小化錯誤率才能讓每單位風險享有更高獲利。為了使問題更單純,本研究將價格走勢預測視為分類問題,而本篇文章會使用機器學習(Machine Learning)來預測類別。在眾多演算法中,本研究選用多數決學習(Ensemble Learning)中具有許多良好特性的隨機森林(Random Forest)為本次交易策略建構的基礎架構。
      本研究選用技術面與籌碼面指標作為訓練模型的特徵,建構兩個交易策略,而分析預測結果的方法除了隨機森林的袋外錯誤率(OOB Error Rate)以外,本研究會更著重在績效表現,以更接近交易策略的本質。由於台股期貨報酬不符合常態,本研究引入一種更為直覺的指標-卡馬比率(Calmar Ratio)作為評估績效的主要標準,另外再加入多種績效指標來提升績效評估的穩健性。
    Over the past decades, predicting trends in financial products prices has been an area of interest, but due to the interaction effects of different factors from all sides, the nature of market is always complex and dynamic. In general, error rate is seen as a proxy of risk of trading strategy, and it needs to be minimized to improve strategy effectiveness. To simplify the problem, the forecasting problem in our research is treated as a classification problem, and Machine Learning is used to solve it. Because of some attractive characteristics, our research used one of Ensemble Learning, which is Random Forest, to construct trading strategies.
    Our research selected technical and chip indicators as the features to train model, and the ways to analyze predictions contained OOB error rate, which derived from Random Forest, and the performance indicators. Because TAIEX Futures historical returns are non-normal distribution, our research introduced an intuitive performance indicator- Calmar Ratio as the evaluation criteria, and the other performance indicators have been added to improve the robustness.
    Our research have tested the performance of strategies and the robustness from different angle, and the result shows that our strategies truly beat the benchmark in whole period, not just training period. Besides, there is a lot of evidence that testing period in our research was in recovery to the peak, and this will lower the discrimination between strategies and benchmark performance. However, from the point of view of OOB error rate, our strategies are truly sufficiently robust.
    Reference: 1. Ballings, M., Van den Poel, D., Hespeels, N. and Gryp, R. (2015), “Evaluating Multiple Classifiers for Stock Price Direction Prediction,” Expert Systems with Applications, Vol. 42, No. 20, pp.7046-7056.
    2. Blume, L., Easley, D. and O′Hara, M. (1994), ”Market Statistics and Technical Analysis: The Role of Volume,” Journal of Finance, Vol. 49, No. 1, pp.153-181.
    3. Brieman, L. (2001), “Random Forests”, Machine Learning, Vol. 45, No. 1, pp.5-32.
    4. Brieman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984), “Classification and Regression Tree”, Wadsworth.
    5. Brooks, C., A. Rew, and S. Ritson (2001), “A Trading Strategy Based on the Lead-Lag Relationship between the Spot Index and Futures Contract for the FTSE 100,” International Journal of Forecasting, Vol. 17, pp.31-44.
    6. Chu, H. H., Chen, T. L., Cheng, C. H., Huang, C. C. (2009), “Fuzzy Dual-Factor Time-Series for Stock Index Forecasting,” Expert Systems with Applications, Vol. 36, No. 1, pp.165-171.
    7. Dutta, J., Bandopadhyay, G. and Sengupta, S. (2012), “Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression,” International Journal of Business and information, Vol. 7, No. 1, pp.105-136.
    8. Grossman, S. J. and Stiglitz, J. E. (1980), “On the Impossibility of Informationally Efficient Markets,” The American Economic Review, Vol. 70, No. 3, pp.393-408.
    9. Ho, T. K. (1995), “Random Decision Forests”, Proceedings of 3rd International Conference on Document Analysis and Recognition, pp.278–282.
    10. Ho, T. K., Hull, J. J. and Srihari, S. N. (1994), “Decision Combination in Multiple Classifier Systems,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16, No. 1, pp.66-75.
    11. Huang, C. J., Yang, D. X. and Chuang, Y. T. (2008), “Application of Wrapper Approach and Composite Classifier to the Stock Trend Prediction,” Expert Systems with Applications, Vol. 34, No. 4, pp.2870-2878.
    12. Hurst, H. E. (1951), “Long-Term Storage Capacity of Reservoirs”, Trans. Amer. Soc. Civil Engineers, Vol. 116, pp.770-799.
    13. Keating, C. and Shadwick, W. F. (2002), “A Universal Performance Measure”, Journal of Performance Measurement, Vol. 6, No. 3, pp.59-84.
    14. Khaidem, L., Saha, S. and Dey, S. R. (2016), “Predicting the Direction of Stock Market Prices Using Random Forest,” arXiv preprint arXiv:160500003.
    15. Kim, S. H. and Chun, S. H. (1998), “Graded Forecasting Using an Array of Bipolar Predictions: Application of Probabilistic Neural Networks to a Stock Market Index,” International Journal of Forecasting, Vol. 14, No. 3, pp.323-337.
    16. Kohavi, R. and John, G. (1997), “Wrappers for Feature Subset Selection,” Artificial Intelligence, Vol. 97, No. 12, pp.273-324.
    17. Kumar, M. and Thenmozhi, M. (2006), “Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest,” In Proceedings of Ninth Indian Institute of Capital Markets Conference, Mumbai, India.
    18. Lai, R. K., Fan, C. Y., Huang, W. H. and Chang, P. C. (2009), “Evolving and Clustering Fuzzy Decision Tree for Financial Time Series Data Forecasting,” Expert Systems with Applications, Vol. 36, No. 2, pp.3761-3773.
    19. Lo, A. W., Mamaysky, H. and Wang, J. (2000), “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation,” Journal of Finance, Vol. 55, No. 4, pp.1705-1765.
    20. Malkiel, B. G. (2003), “The Efficient Market Hypothesis and Its Critics,” The Journal of Economic Perspectives, Vol. 17, No. 1, pp.59-82.
    21. Malkiel, B. G. and Fama, E. F. (1970), “Efficient Capital Markets: A Review of Theory and Empirical Work,” Journal of Finance, Vol. 25, No. 2, pp.383-417.
    22. Mandelbrot, B. B. and Wallis, J. (1968), “Noah, Joseph and Operational Hydrology”, Water Resources Research, Vol. 4, pp.909-918.
    23. Ren, N., Zargham, M. and Rahimi, S. (2006), “A Decision Tree-Based Classification Approach to Rule Extraction for Security Analysis,” International Journal of Information Technology and Decision Making, Vol. 5, No. 1, pp.227-240.
    24. Shannon, C. E. (1948), “A Mathematical Theory of Communication”, Bell System Technical Journal, Vol. 27, No. 3, pp.379-423 and 623-656.
    25. Sheu, H. J. and Wei, Y. C. (2011), “Options Trading Based on the Forecasting of Volatility Direction with the Incorporation of Investor Sentiment”, Emerging Markets Finance and Trade, Vol. 47, No. 2, pp.31-47.
    26. Simon, D. P. and Wiggins, R. A. (2001), “S&P Futures Returns and Contrary Sentiment Indicators,” Journal of Futures Markets, Vol. 21, No. 5, pp.447-462.
    27. Sortino, F. A. and Van der Meer, R. (1991), “Downside Risk”, The Journal of Portfolio Management, Vol. 17, No. 4, pp.27-31.
    28. Sortino, F. A., Van der Meer, R. and Plantinga, A. (1999), “The Dutch Triangle”, The Journal of Portfolio Management, Vol. 26, No. 1, pp.50-57.
    29. Wang, L. R. and Shen, C. H. (1999), “Do Foreign Investments Affect Foreign Exchange and Stock Markets – The Case of Taiwan,” Applied Economics, Vol. 31, No. 11, pp.1303-1314.
    30. Wilder, J. W. Jr. (1978), “New Concepts in Technical Trading Systems,” Trend Research.
    31. 陳彥碩 (2011),外資現貨買賣超、期貨與選擇權多空交易與大盤指數之關係:台灣證券市場實證研究,碩士論文,政治大學金融研究所。
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105352022
    Data Type: thesis
    DOI: 10.6814/THE.NCCU.MB.017.2018.F06
    Appears in Collections:[金融學系] 學位論文

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