Employing Machine Learning Algorithms to build Trading Strategies with higher than Risk-Free Returns

Abstract

This research aims at exploring whether simple trading strategies developed using state-of-the-art Machine Learning (ML) algorithms can guarantee more than the risk-free rate of return or not. For this purpose, the direction of S&P 500 Index returns on every 6th day (SPYRETDIR6) and magnitude of S&P 500 Index daily returns (SPYMAG) were predicted on a broad selection of independent variables using various ML techniques. Using five consecutive data spans of equal length, GBM was found to provide highest prediction accuracy on SPYRETDIR6, consistently. In terms of magnitude prediction of daily returns (SPYMAG), Random Forest results indicated that there is a very high correlation between actual/predicted values of SPY. Based on these results, Trading Strategy #1 (using SPYRETDIR6 predictions) and Trading Strategy #2 (using SPYMAG predictions) were developed and tested against a simple Buy & Hold benchmark of the same index. It was found that Trading Strategy #1 provides negative returns on all data spans, while Trading Strategy #2 has positive returns on average when data is separated into consecutive data spans. None of the trading strategies have a positive Sharpe ratio on average, but Trading Strategy #2 is almost as profitable as investing in T-bills using the risk-free rate.

Keywords

• Machine Learning
• S&P 500
• Forecasting
• Ensemble Methods
• XGBoost

References

1. Atsalakis, G.S. and K.P. Valavanis, (2009). Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with Applications, 36 (3), 5932-5941.
2. Ballings, M., D. Van den Poel, N. Hespeels and R. Gryp (2015). Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications, 42 (20), 7046-7056.
3. CBOE. (2020). VIX Index [Data File]. Retrieved 09 22, 2019, from http://www.cboe.com/cha rdata/GetDownloadSymbolData/?Symbol=VIX.
4. Diler, A.I. (2003). Forecasting the Direction of the ISE National-100 Index By Neural Networks Backpropagation Algorithm. Istanbul Stock Exchange Review, 7 (25-26), 65-82.
5. Ding, S., H. Zhao, Y. Zhang, X. Xu and R. Nie (2015). Extreme learning machine: algorithm, theory and applications. Artificial Intelligence Review, 44 (1), 103-115.
6. Egeli, B., M. Ozturan and B. Badur (2003). Stock Market Prediction Using Artificial Neural Networks. Decision Support Systems, 22, 171-185.
7. Fidelity. (n.d.). Relative Strength Index (RSI). Retrieved September 26, 2019, from https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/RSI.
8. FRED. (2019). Retrieved 09 27, 2019, from Federal Reserve Bank of St.Louis Economic Research: https://fred.stlouisfed.org/.

9. Gradient Boosting Machines. (2018). Retrieved 09 22, 2019, from https://uc-r.github.io/2018/ 06/14/gbm-regression/.
10. Gündüz, H., Z. Çataltepe and Y. Yaslan (2017). Stock daily return prediction using expanded features and feature selection. Turkish Journal of Electrical Engineering & Computer Sciences, 25 (6), 4829-4840.
11. Imandoust, S.B. and M. Bolandraftar (2014). Forecasting the direction of stock market index movement using three data mining techniques: the case of Tehran Stock Exchange. International Journal of Engineering Research and Applications, 4 (6), 106-117.
12. Investing. (2019). Retrieved 09 22, 2019, from https://www.investing.com/.
13. Kara, Y., M.A. Boyacioglu and Ö.K. Baykan (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert Systems with Applications, 38 (5), 5311-5319.
14. Kim, K.J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55 (1-2), 307-319.
15. Kumar, M. and M. Thenmozhi (2006). Forecasting Stock Index Movement: A Comparision of Support Vector Machines and Random Forest. Indian Institute of Capital Markets 9th Capital Markets Conference Paper.
16. Labiad, B., A. Berrado and L. Benabbou (2016). Machine learning techniques for short term stock movements classification for moroccan stock exchange. In 2016 11th International Conference on Intelligent Systems: Theories and Applications (SITA) (pp. 1-6). IEEE.
17. Michalski, R.S. and Y. Kodratoff (1990). Research in machine learning: Recent progress, classification of methods, and future directions. In Machine learning (pp. 3-30). Morgan Kaufmann.
18. Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel and J. Vanderplas (2011). Scikit-learn: Machine learning in Python. The journal of machine learning research, 12, 2825-2830.
19. Ridgeway, G. (2007). Generalized Boosted Models: A guide to the gbm package. Update, 1 (1), 2007.
20. Rodriguez, P.N. and A. Rodriguez (2004). Predicting stock market indices movements. WIT Transactions on Modelling and Simulation, 38.
21. Sunasra, M. (2017). Performance metrics for classification problems in machine learning. Medium. Retrieved 09 22, 2019, from https://medium.com/thalus-ai/performance-metrics -for-classification-problems-in-machine-learning-part-i-b085d432082b.
22. Wüthrich, B., D. Permunetilleke, S. Leung, W. Lam, V. Cho and J. Zhang (1998). Daily prediction of major stock indices from textual www data. Hkie transactions, 5 (3), 151-156.
23. XGBOOST. (2020). XGBoost Parameters. Retrieved 09 22, 2019, from https://xgboost.readthe docs.io/en/latest/parameter.html.
24. YAHOO. (2019a). Retrieved September 27, 2019, from https://finance.yahoo.com/.
25. YAHOO. (2019b). Retrieved September 20, 2019, from https://finance.yahoo.com/quote /SPY/history?p=SPY.
26. Yoon, Y. and G. Swales (1991). Predicting stock price performance: A neural network approach. In Proceedings of the twenty-fourth annual Hawaii international conference on system sciences (Vol. 4, pp. 156-162). IEEE.
27. Zhai, Y., A. Hsu, and S.K. Halgamuge (2007). Combining news and technical indicators in daily stock price trends prediction. In International symposium on neural networks (pp. 1087-1096). Springer, Berlin, Heidelberg.
28. Zhang, Y. and L. Wu, (2009). Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Systems with Applications, 36 (5), 8849-8854.