Application of machine learning in stock trading: a review
-
2018-06-08 https://doi.org/10.14419/ijet.v7i2.33.15479 -
Fundamental Analysis, Machine Learning, Stock Prediction, Technical Analysis -
Abstract
The wide adoption of machine learning techniques in predicting stock prices has led to the emergence of many articles on the topic. Howev-er, a systematic review on the topic remains lacking. This paper provides a systematic review of the recent applications of machine learning techniques in the construction of stock prediction models. A framework is designed to classify and evaluate the relevant work in recent arti-cles based on the type of model, type of financial market, type of prediction technique, type of optimization approach, type of indicators, type of performance metrics, type of benchmark models and prediction results. It is observed that financial indicators are the frequently used input variables and different forms of machine learning techniques are integrated to predict the stock prices. There are 4 variables that im-pose significant influence on the prediction model, namely the type of input variables, type of prediction technique, type of optimization approach and number of analysis layer. Thus, the limitations and potential enhancement on the 4 variables are discussed so that optimal combinations will be established in future research efforts.
Â
Â
 -
References
[1] C. Aggarwal, Data Mining: The Textbook. 1st ed., New York: Springer, 2015.
[2] M. C., Angadi and A. P., Kulkarni, “Time series data analysis for stock market prediction using data mining techniques with R,†International Journal of Advanced Research in Computer Science, vol. 6, no. 6, pp. 105-109, 2015.
[3] M. Awad and R. Khanna, Efficient Learning Machines: Theories, Concepts and Applications for Engineers and System Designers. 1st ed. New York: ApressOpen, 2015.
[4] M. Ballings and D. Van den Poel, “CRM in Social Media: Predicting increases in Facebook usage frequency,†European Journal of Operational Research, vol. 244, no. 1, pp. 248-260, 2015.
[5] M. Ballings and D. Van den Poel, “Customer event history for churn prediction: How long is long enough?,†Expert Systems with Applications, vol. 39, no. 18, pp. 13517-13522, 2012.
[6] M. Ballings, D. Van den Poel, N. Hespeels and R. Gryp, “Evaluating multiple classifiers for stock price direction prediction,†Expert Systems with Applications, vol. 43, no. 20, pp. 7046-7056, 2015.
[7] S. Banik, A. F. M. K. Khan and M. Anwer, “Hybrid machine learning technique for forecasting Dhaka stock market timing decisions,†Computational Intelligence and Neuroscience: CIN, vol. 2014. pp. 1-6, 2014.
[8] P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting. 3rd ed., New York: Springer, 2016.
[9] J. Chai, J. Du, K. K. Lai and Y. P. Lee, “A hybrid least square support vector machine model with parameters optimization for stock forecasting,†Mathematical Problems in Engineering, vol. 2015, pp. 1-7, 2015.
[10] T. D. Chaudhuri, I. Ghosh, and S. Singh, “Application of machine learning tools in predictive modelling of pairs trade in Indian stock market,†IUP Journal of Applied Finance, vol. 23, no. 1, pp. 5-25, 2017.
[11] Y. Chen and Y. Hao, “A feature weighted support vector machine and k-nearest neighbour algorithm for stock market indices prediction,†Expert Systems with Applications, vol. 80. P. 340-355, 2017.
[12] R. Dash and P. K. Dash, “A hybrid stock trading framework integrating technical analysis with machine learning techniques,†The Journal of Finance and Data Science, vol. 2, no. 1, pp. 42-57, 2016.
[13] C. L. Dunis, S. D. Likothanassis, A. S. Karathanasopoulos and G. S. Sermpinis, “A hybrid genetic algorithm–support vector machine approach in the task of forecasting and trading,†Journal of Asset Management, vol. 14, no. 1, pp. 52-71, 2013.
[14] B. Graham, The Intelligent Investor. 1st ed., New York: Harper & Brothers, 1949.
[15] R. Hafezi, J. Shahrabi and E. Hadavandi, E., “A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price,†Applied Soft Computing, vol. 29, pp. 196-210, 2015.
[16] S. Hegazy, O. S. Soliman and M. A. Salam, “Comparative study between FPA, BA, MCS, ABC, and PSO algorithms in training and optimizing of LS-SVM for stock market prediction,†International Journal of Advanced Computer Research, vol. 5, no. 18, pp. 35-45, 2015.
[17] K. Hong and E. Wu, “The Roles of Past Returns and Firm Fundamentals in Driving Us Stock Price Movements,†International Review of Financial Analysis, vol. 43, pp. 62-75, 2016.
[18] Y. Hu, K. Liu, X. Zhang, L. Su, E. W. T. Ngai and M. Liu, “Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review,†Applied Soft Computing, vol. 36, pp. 534-551, 2015.
[19] P. A. Idowu, C. Osakwe, A. K. Anderonke and E. R. Adagunodo, “Prediction of stock market in Nigeria using artificial neural network," International Journal of Intelligent Systems and Applications, vol. 4, no. 11, pp. 68-74, 2012.
[20] M. Inthachot, V. Boonjing and S. Intakosum, “Artificial neural network and genetic algorithm hybrid intelligence for predicting Thai stock price index trend,†Computational Intelligence and Neuroscience, vol. 2016, pp. 1-8, 2016.
[21] S. Kamley, S. Jaloree and R. S. Thakur, “Performance forecasting of share market using machine learning techniques: A review,†International Journal of Electrical and Computer Engineering (IJECE), vol. 6, no. 6, pp. 3196-3204, 2016.
[22] Keele University, “Guidelines for performing systematic literature reviews in software engineering,†Keele University, 2006. [Online]. Available: https://userpages.uni-koblenz.de/~laemmel/esecourse/slides/slr.pdf [Accessed: Oct. 27, 2017].
[23] H. Kim and S. T. Han, “The enhanced classification for the stock index prediction,†Procedia Computer Science, vol. 91, pp. 284-286, 2016.
[24] B. G. Malkiel and E. F. Fama, “Efficient capital markets: A review of theory and empirical work,†The Journal of Finance, vol. 25, no. 2, pp. 383-417, 1970.
[25] B. G. Malkiel, “The efficient market hypothesis and its critics,†Journal of Economic Perspectives, vol. 17, no. 1, pp. 59-82, 2003.
[26] R. K. Narang, Inside the Black Box: The Simple Truth About Quantitative Trading. 1st ed., New Jersey: John Wiley & Sons, 2009.
[27] A. Nayak, M. M. M. Pai and R. M. Pai, “Prediction models for Indian stock market,†Procedia Computer Science, vol. 89, pp. 441-449, 2016.
[28] R. K. Nayak, D. Mishra and A. K. Rath, “A naïve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices,†Applied Soft Computing, vol. 35, pp. 670-680, 2015.
[29] R. T. F. Nazario, J. L. Silva, V. A. Sobreiro and H. Kimura, “A literature review of technical analysis on stock markets,†The Quarterly Review of Economics and Finance, vol. 66, pp. 115-126, 2017.
[30] G. Paleologo, A. Elisseeff and G. Antonini, “Subagging for credit scoring models,†European Journal of Operational Research, vol. 201 no. 2, pp. 490-499, 2010.
[31] J. Patel, S. Shah, P. Thakkar and K. Kotecha, “Predicting stock market index using fusion of machine learning techniques,†Expert Systems with Applications, vol. 42, no. 4, pp. 2162-2172, 2014.
[32] J. Patel, S. Shah, P. Thakkar and K. Kotecha, “Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques,†Expert Systems with Applications, vol. 42, no. 1, pp. 259-268, 2014.
[33] M. Qiu, C. Li and Y. Song “Application of the artificial neural network in predicting the direction of stock market index,†In Proc. 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), 2016, pp. 219-223.
[34] M. Qiu, Y. Song and F. Akagi, “Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market,†Chaos, Solitons & Fractals, vol. 85, pp. 1-7, 2016.
[35] V. Ravi, D. Pradeepkumar and K. Deb “Financial time series prediction using hybrids of chaos theory, multi-layer perceptron and multi-objective evolutionary algorithms,†Swarm and Evolutionary Computation, vol. 36. pp. 136-149, 2017.
[36] R. Rosillo, J. Giner, D. D. Fuente and R. Pino, “Trading system based on support vector machines in the S&P 500 Index,†In Proc. International Conference on Artificial Intelligence (ICAI), 2012, pp. 1-5.
[37] O. B. Sezer, A. M. Ozbayoglu, E. Dogdu “An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework,†In Proc. ACMSE 2017 The Annual ACM Southeast Conference Featuring Multidisciplinary and Interdisciplinary Computing, 2017, pp. 223-226.
[38] T. Suciu, “Elements of Stock Market Analysis,†Bulletin of the Transilvania University of Brasov. Series V: Economic Sciences, vol. 6, no. 2, pp. 153-160, 2013.
[39] L. A. Teixeira and A. L. Oliveira, “A method for automatic stock trading combining technical analysis and nearest neighbour classification,†Expert Systems with Applications, vol. 37, no. 10, pp. 6885-6890, 2010.
[40] V. P. Upadhyay, S. Panwar, R. Merugu and R. Panchariya, “Forecasting stock market movements using various kernel functions in support vector machine,†In Proc. International Conference on Advances in Information Communication Technology & Computing, 2016, pp. 1-5.
[41] J. M. Verner, O. P. Bereton, B. A. Kitchenham, M. Turner and M. Niazi, “Systematic literature reviews in global software development: A tertiary study,†IET Conference Proceedings, vol. 2012, pp. 1-10, 2012.
[42] S. Wang and W. Shang, “Forecasting direction of China Security Index 300 movement with least squares support vector machine,†Procedia Computer Science, vol. 31, pp. 869-874, 2014.
[43] Y. Wang, “Stock price direction prediction by directly using prices data: An empirical study on the KOSPI and HIS. Int,†J. Business Intelligence and Data Mining, vol. 9, no. 2, pp. 145-160, 2014.
[44] B. Weng, M. A. Ahmed and F. M. Megahed, “Stock market one-day ahead movement prediction using disparate data sources,†Expert Systems with Applications, vol. 79, pp. 153-163, 2017.
[45] World Federation of Exchange, “WFE FY 2016 Market Highlights,†World Federation of Exchange, 2016. [Online]. Available: https://www.world-exchanges.org/home/index.php/statistics/market-highlights. [Accessed: Oct. 12, 2017].
[46] J. L. Wu and P. C. Chang, “A trend-based segmentation method and the support vector regression for financial time series forecasting,†Mathematical Problems in Engineering, vol. 2012. pp. 1-20, 2012.
[47] R. Yamamoto “Intraday technical analysis of individual stocks on the Tokyo Stock Exchange,†Journal of Banking & Finance. vol. 36, no. 11, pp. 3033-3047, 2012.
[48] H. Yu, R. Chen and G. Zhang “A SVM stock selection model within PCA,†Procedia Computer Science, vol. 31, pp. 407-412, 2014.
[49] K. Zbikowski, “Using volume weighted support vector machines with walk forward testing and feature selection for the purpose of creating stock trading strategy,†Expert Systems with Applications, vol. 42, no. 4, pp. 1797-1805, 2014.
-
Downloads
-
How to Cite
Sheng Tan, K., & Logeswaran, R. (2018). Application of machine learning in stock trading: a review. International Journal of Engineering & Technology, 7(2.33), 695-702. https://doi.org/10.14419/ijet.v7i2.33.15479Received date: 2018-07-13
Accepted date: 2018-07-13
Published date: 2018-06-08