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題名：機器學習於投資組合之表現—隨機森林法的應用
作者：簡清源
作者(外文)：CHIEN, CHING-YUAN
校院名稱：元智大學
系所名稱：管理學院博士班
指導教授：羅懷均
學位類別：博士
出版日期：2022
主題關鍵詞：交易策略；機器學習；隨機森林；蒙地卡羅模擬；凱利公式；Trading Strategy；Machine Learning；Random forest；Monte Carlo；Kelly formula
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近年來投資人逐漸應用人工智慧來輔助進行投資決策。因此，本文主要研究以人工智慧的機器學習法，應用於台灣股票市場的交易。本文運用個股過去的股票報酬率、交易週轉率與月營收年增率，應用隨機森林演算法預測未來可能上漲的股票作為投資標的，再搭配蒙地卡羅模擬法與凱利公式來建構投資組合。
實證研究發現，使用隨機森林演算法，可建構的年平均報酬率略高於市場投資組合的報酬率，且有較低的風險，因此有較佳的Sharpe index。特別是加入月營收年增率後的投資策略，更能大幅降低投資組合的標準差，因此能獲得顯著正的超額報酬。本研究實證顯示，機器學習對於投資策略與投資組合建構具有正面的幫助，特別在提高投資組合的報酬表現及降低投資組合的風險。這項研究能幫助投資人瞭解如何利用機器學習建構可獲利的投資策略與投資組合。
In recent years, investors have gradually applied artificial intelligence to assist investment decisions. Therefore, this research mainly application the machine learning method of artificial intelligence to the trading of the Taiwan stock market. This paper uses the past stock return rate, transaction turnover rate and revenue growth rate of individual stocks, and uses random forest algorithm to predict future stocks that may rise as investment targets, and then uses Monte Carlo simulation method and Kelly formula to construct investment portfolios.
Empirical studies have found that, using the random forest algorithm, the annual average return rate that can be constructed is slightly higher than the return rate of the market portfolio, and it has lower risk and thus a better Sharpe index. In particular, the investment strategy after adding the annual growth rate of monthly revenue can greatly reduce the standard deviation of the investment portfolio, and thus can obtain a significant positive excess return.This study confirmed shows that machine learning has positive benefits for investment strategy and portfolio construction, especially in reducing portfolio risk. This study can help investors understand how machine learning can be used to construct profitable investment strategies and portfolios.
Anghel, D. G. (2021). A reality check on trading rule performance in the cryptocurrency market: Machine learning vs. technical analysis. Finance Research Letters, 39, 101655.
Ayala, J., García-Torres, M., Noguera, J. L. V., Gómez-Vela, F., & Divina, F. (2021). Technical analysis strategy optimization using a machine learning approach in stock market indices. Knowledge-Based Systems, 225, 107119.
Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications, 19(8), 1165-1195.
Bell, R. M., & Cover, T. M. (1980). Competitive optimality of logarithmic investment. Mathematics of Operations Research, 5(2), 161-166.
Bell, R., & Cover, T. M. (1988). Game-theoretic optimal portfolios. Management Science, 34(6), 724-733.
Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25(2), 197-227.
Bicksler, J. L., & Thorp, E. O. (1973). The capital growth model: an empirical investigation. Journal of Financial and Quantitative Analysis, 8(2), 273-287.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
Brown, J. H., Crocker, D. K., & Foerster, S. R. (2009). Trading volume and stock investments. Financial Analysts Journal, 65(2), 67-84.
Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52, 57-82.
Cheng, T., & Zhao, A. B. (2022). Stock return prediction: Stacking a variety of models. Journal of Empirical Finance, 67, 288-317.
Dewi, P. S., Widyanto, J. K., & Tick, A. (2021, September). Monte Carlo simulation of stock movements-an Indonesian case. In 2021 IEEE 19th International Symposium on Intelligent Systems and Informatics, (SISY) (pp. 101-106). IEEE.
Estember, R. D., & Maraña, M. J. R. (2016, March). Forecasting of stock prices using Brownian motion-Monte Carlo simulation. In International Conference on Industrial Engineering and Operations Management, 8-10.
Fama, E. F., French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3-56.
Ghosh, P., Neufeld, A., & Sahoo, J. K. (2022). Forecasting directional movements of stock prices for intraday trading using LSTM and random forests. Finance Research Letters, 46, 102280.
Grinblatt, M., & Moskowitz, T. J. (2004). Predicting stock price movements from past returns: The role of consistency and tax-loss selling. Journal of Financial Economics, 71(3), 541-579.
Grinblatt, M., Titman, S., & Wermers, R. (1995). Momentum investment strategies, portfolio performance, and herding: A study of mutual fund behavior. American Economic Review, 85(5), 1088-1105.
Gupta, R., Nel, J., & Pierdzioch, C. (2021). Investor confidence and forecastability of us stock market realized volatility: Evidence from machine learning. Journal of Behavioral Finance, 1-12.
Kelly, J., A new interpretation of information rate. Bell System Technical Journal, 35, 917-926.
Laborda, R., & Laborda, J. (2017). Can tree-structured classifiers add value to the investor? Finance Research Letters, 22, 211-226.
Leippold, M., Wang, Q., & Zhou, W. (2021). Machine learning in the Chinese stock market. Journal of Financial Economics, 145(2), 64-82.
Lin, A. Y., & Swanson, P. E. (2003). The behavior and performance of foreign investors in emerging equity markets: Evidence from Taiwan. International Review of Finance, 4(3‐4), 189-210.
Liu, Q., Wang, C., Zhang, P., & Zheng, K. (2021). Detecting stock market manipulation via machine learning: Evidence from China Securities Regulatory Commission punishment cases. International Review of Financial Analysis, 78, 101887.
Lo, H. C., Koedijk, K. G., Gao, X., & Hsu, Y. T. (2020). How do job vacancy rates predict firm performance? A web crawling massive data perspective. Pacific-Basin Finance Journal, 62, 101371.
Lohrmann, C., & Luukka, P. (2019). Classification of intraday S&P500 returns with a Random Forest. International Journal of Forecasting, 35(1), 390-407.
MacLean, L.C., Sanegre, R., Zhao, Y. & Ziemba, W.T. (2004). Capital growth with security. Journal of Economic Dynamics and Control, 28, 937-954.
MacLean, L. C., Thorp, E. O., Zhao, Y., & Ziemba, W. T. (2011). How does the fortune’s formula kelly capital growth model perform? Journal of Portfolio Management, 37(4), 96-111.
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2010). Good and bad properties of the Kelly criterion. Risk, 20(2), 1.
MacLean, L., & Zhao, Y. (2022). Kelly investing with downside risk control in a regime-switching market. Quantitative Finance, 22(1), 75-94.
MacLean, L. C., Ziemba, W. T., & Blazenko, G. (1992). Growth versus security in dynamic investment analysis. Management Science, 38(11), 1562-1585.
Naughton, T., Truong, C., & Veeraraghavan, M. (2008). Momentum strategies and stock returns: Chinese evidence. Pacific-Basin Finance Journal, 16(4), 476-492.
Oshiro, T. M., Perez, P. S., & Baranauskas, J. A. (2012, July). How many trees in a random forest? In International Workshop on Machine Learning and Data Mining in Pattern Recognition, (pp. 154-168). Springer, Berlin, Heidelberg.
Parungrojrat, N., & Kidsom, A. (2019). Stock price forecasting: Geometric Brownian motion and Monte Carlo simulation techniques. MUT Journal of Business Administration, 16(1), 90-103.
Rasekhschaffe, K. C., & Jones, R. C. (2019). Machine learning for stock selection. Financial Analysts Journal, 75(3), 70-88.
Rotando, L. M., & Thorp, E. O. (1992). The Kelly criterion and the stock market. The American Mathematical Monthly, 99(10), 922-931.
Shen, S., Jiang, H., & Zhang, T. (2012). Stock market forecasting using machine learning algorithms. Department of Electrical Engineering, Stanford University, Stanford, CA, 1-5.
Statman, M. (1987). How many stocks make a diversified portfolio? Journal of Financial and Quantitative Analysis, 22(3), 353-363.
Thorp, E. O. (2011). The Kelly criterion in blackjack sports betting, and the stock market. The Kelly Capital Growth Investment Criterion: Theory and Practice, 789-832.
Wang, C., & Chin, S. (2004). Profitability of return and volume-based investment strategies in China's stock market. Pacific-Basin Finance Journal, 12(5), 541-564.
Xiang, J. N. P., Velu, S. R., & Zygiaris, S. (2021, December). Monte Carlo simulation prediction of stock prices. In 2021 14th International Conference on Developments in esystems Engineering, (DESE) (pp. 212-216). IEEE.
Zhong, X., & Enke, D. (2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1), 1-20.
Ziemba, W. T., & Hausch, D. B. (1986). Betting at the Racetrack, Vancouver and Los Angeles: Dr. Z Investments, Inc. Contract Splits.

 
 
 
 
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