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An Automated Stock Investment System Using Machine Learning Techniques: An Application in Australia

Authors: Carol Anne Hargreaves


A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: Machine learning, stock market trading, logistic principal component analysis, automated stock investment system.

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[1] U. Arun, B. Gautam and D. Avijan, “Prediction of Stock Performance in the Indian Stock Market using Logistic Regression”, International Journal of Business and Information, Vol. 7, 2012, pp.105-136.
[2] C.A. Hargreaves, P. Dixit, A. Solanki, “Stock Portfolio Selection using Data Mining Approach”, IOSR Journal of Engineering, Vol 3, Issue 11, 2013, pp. 42-48.
[3] C.A. Hargreaves, C. Kardivel Mani, “The Selection of Winning Stocks using Principal Component Analysis”, American Journal of Marketing Research, Vol 1, No.3, 2015, pp. 183-188.
[4] C.A. Hargreaves, Y. Hao,” Does the use of Technical & Fundamental Analysis improve Stock Choice? A Data Mining Approach applied to the Australian Stock Market”, International Conference on Statistics in Science, Business and Engineering (ICSSBE), IEEE Explore, 2012, pp. 1-6.
[5] C.A. Hargreaves, Y. Hao, “Prediction of Stock Performance Using Analytical Techniques”, Journal of Emerging Technologies in Web Intelligence, Vol 5, No. 2, 2013, pp. 136-142.
[6] R. Peachavanish, “Stock selection and Trading Based Cluster Analysis of Trend and Momentum Indicators,” Proceedings of the International MultiConference of Engineers and Computer Scientists, 2016, Vol 1,
[7] C. Kardivel Mani, C. A. Hargreaves, “Stock Trading using Analytics”, American Journal of Marketing Research, Vol.2, No. 2, 2016, pp. 27-37.
[8] S. Thawornwong, S and D. Enke, "The adaptive selection of financial and economic variables for use with artificial neural networks," Neurocomputing, vol. 56, 2004, pp. 205-232.
[9] K. Wu, K; Y. Wu, H. Lee, “Stock trend prediction by using K-means and Apriori algorithm for sequential chart pattern mining”, Journal of Information Science and Engineering, 30, 2014, pp. 653-667.
[10] Wang, Z., Sun, y., Stockli, P. (2014). “Functional Principal Components Analysis of Shanghai Stock Exchange 50 Index”. Discrete Dynamics in Nature and Society Volume 2014 Article ID 365204, 7 pages
[11] Renugadevi, T., Ezhilarasie,R., Sujatha, M and Umamakeswari, A. (2016). Stock Market Prediction using Hierarchical Agglomerative and K-Means Clustering Algorithm. Indian Journal of Science and Technology,Vol.9,(48),DOI:10.17485/ijst/2016/v9i48/108029
[12] Wang, Y., In-Chan Choi. (2013).” Market Index and stock price direction prediction using Machine Learning Techniques: An empirical study on the KOSPI and HSI”. Science Direct. Pages 1-13.
[13] Hengshan W, and Phichhang O, Prediction of Stock Market Index Movement by Ten Data Mining Techniques, Modern Applied Science, Vol. 3(12), 2009