DOI QR코드

DOI QR Code

Stock Price Prediction and Portfolio Selection Using Artificial Intelligence

  • Received : 2019.04.01
  • Accepted : 2019.10.04
  • Published : 2020.03.30

Abstract

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.

Keywords

References

  1. Agrawal, S., Thakkar, D., Soni, D., Bhimani, K., and Patel, C. (2019). Stock market prediction using machine learning techniques. Data Collection Feature Extraction Data Normalization Training Output, 5(2), 1099-1103. https://doi.org/10.32628/CSEIT1952296
  2. Anbalagan, T., and Maheswari, S. U. (2014). Classification and prediction of stock market index based on Fuzzy Metagraph. Procedia Computer Science, 47(C), 214-221. https://doi.org/10.1016/j.procs.2015.03.200
  3. Arik, S., Eryilmaz, S. B., and Goldberg, A. (2014). Supervised classification-based stock prediction and portfolio optimization. Retrieved from http://arxiv.org/abs/1406.0824
  4. Banik, S., Khodadad Khan, A. F. M., and Anwer, M. (2014). Hybrid machine learning technique for forecasting dhaka stock market timing decisions. Computational Intelligence and Neuroscience, 2014. https://doi.org/10.1155/2014/318524
  5. Bouckaert, R. R., Frank, E., Hall, M., Kirkby, R., Reutemann, P., Seewald, A., and Scuse, D. (2017). WEKA Manual for Version 3-8-2.
  6. Cenesizoglu, T., Papageorgiou, N., Reeves, J. J., and Wu, H. (2019). An analysis on the predictability of CAPM beta for momentum returns. Journal of Forecasting, 38(2), 136-153. https://doi.org/10.1002/for.2552
  7. Cheng, C., and Chen, Y. (2007). Fundamental analysis of stock trading systems using classification techniques. Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007, 19-22.
  8. Chong, E., Han, C., and Park, F. (2017). Deep learning networks for stock market analysis and prediction. Expert Systems with Applications, 83(April), 187-205. https://doi.org/10.1016/j.eswa.2017.04.030
  9. Dase, R. K., and Pawar, D. D. (2010). Application of artificial neural network for stock market predictions: A review of literature. International Journal of Machine Intelligence, 2(2), 14-17. https://doi.org/10.9735/0975-2927.2.2.14-17
  10. Di Persio, L., and Honchar, O. (2016). Artificial neural networks architectures for stock price prediction: Comparisons and applications. International Journal of Circuits, Systems and Signal Processing, 10, 403-413. https://doi.org/10.1676/09-204.1
  11. Diakoulakis, I. E., Koulouriotis, D. E., and Emiris, D. M. (2018). A Review of Stock Market Prediction Using Computational Methods. In: Kontoghiorghes E.J., Rustem B., Siokos S. (eds) Computational Methods in Decision-Making, Economics and Finance. Applied Optimization, vol 74. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3613-7_20
  12. Dipietro, D. M. (2019). Alpha cloning: Using quantitative techniques and SEC 13f data for equity portfolio optimization and generation. The Journal of Financial Data Science Fall 2019, 1 (4), 159-171. https://doi.org/10.3905/jfds.2019.1.008
  13. Dunne, M. (2017). Stock market prediction declaration of originality. Dept of Computer Science, University College Cork, 1(1), 10.
  14. Flechas Chaparro, X. A., de Vasconcelos Gomes, L. A., and Tromboni de Souza Nascimento, P. (2019). The evolution of project portfolio selection methods: from incremental to radical innovation. Revista de Gestao, 26(3), 212-236. https://doi.org/10.1108/rege10-2018-0096
  15. Huang, Y., Fernando Capretz, L., and Ho, D. (2019). Neural network models for stock selection based on fundamental analysis. IEEE, Canada, (May), 1-4.
  16. Ican, O., and Celik, T. B. (2017). Stock market prediction performance of neural networks: A literature review. International Journal of Economics and Finance, 9(11), 100. https://doi.org/10.5539/ijef.v9n11p100
  17. Khaidem, L., Saha, S., and Dey, S. R. (2016). Predicting the direction of stock market prices using random forest, 1-20. Retrieved from http://arxiv.org/abs/1605.00003
  18. Kumari, S. K., Kumar, P., Priya, J., Surya, S., and Bhurjee, A. K. (2019). Mean-value at risk portfolio selection problem using clustering technique: A case study. AIP Conference Proceedings, 2112(June). https://doi.org/10.1063/1.5112363
  19. Kusuma, R. M. I., Ho, T.-T., Kao, W.-C., Ou, Y.-Y., and Hua, K.-L. (2019). Using deep learning neural networks and candlestick chart representation to predict stock market, 1-13. Retrieved from http://arxiv.org/abs/1903.12258
  20. Leong, Y. C., and Zaki, J. (2018). Unrealistic optimism in advice taking: A computational account. Journal of Experimental Psychology: General, 147(2), 170-189. https://doi.org/10.1037/xge0000382
  21. Malagrino, L. S., Roman, N. T., and Monteiro, A. M. (2018). Forecasting stock market index daily direction: A Bayesian Network approach. Expert Systems with Applications, 105, 11-22. https://doi.org/10.1016/j.eswa.2018.03.039
  22. Malkiel, B. G. (2003). Efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82. https://doi.org/10.1257/089533003321164958
  23. Montmarquette, C., and Viennot-briot, N. (2012). Econometric models on the value of advice of a financial advisor. Centre Interuniversitaire de Recherche En Analyse Des Organisations.
  24. NSE. (2018). NSE. Retrieved from https://www.nseindia.com/products/content/equities/equities/equities.htm
  25. Pang, X., Zhou, Y., Wang, P., Lin, W., and Chang, V. (2018). An innovative neural network approach for stock market prediction. Journal of Supercomputing, (January), 1-21. https://doi.org/10.1007/s11227-017-2228-y
  26. Putra, A. I. L. W., Putra, A. D., Dewi, M. S., and Radianto, D. O. (2019). Differences in intrinsic value with stock market prices using the price earning ratio (per) approach as an investment decision making indicator (Case study of manufacturing companies in Indonesia period 2016-2017). Aptisi Transactions on Technopreneurship, 1(1), 82-92. https://doi.org/10.34306/att.v1i1.61
  27. Pyo, S., Lee, J., Cha, M., and Jang, H. (2017). Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets. PLoS ONE, 12(11), 1-17. https://doi.org/10.1371/journal.pone.0188107
  28. Qiu, M., and Song, Y. (2016). Predicting the direction of stock market index movement using an optimized artificial neural network model. PLoS ONE, 11(5), 1-11. https://doi.org/10.1371/journal.pone.0155133
  29. Report, T. (2016). Automated stock market trading system using machine learning. Automated Stock Market Trading System, (May 2015). https://doi.org/0.13140/RG.2.1.1998.3520 https://doi.org/10.13140/RG.2.1.1998.3520
  30. Rudin, C. (2012). A profitable approach to security analysis using machine learning: an application to the prediction of market behavior following earnings Reports. 15.097 Prediction: Machine Learning and Statistics (MIT-OCW), 1-22. Retrieved from https://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/projects/MIT15_097S12_proj2.pdf
  31. Selvamuthu, D., Kumar, V., and Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1). https://doi.org/10.1186/s40854-019-0131-7
  32. Song, I. (2014). New quantitative approaches to asset selection and portfolio construction. ProQuest Dissertations and Theses, 213. https://doi.org/10.13005/ojc/290419
  33. Sung, M.-C., Ma, T., Hsu, M.-W., Johnson, J. E. V., and Lessmann, S. (2016). Bridging the divide in financial market forecasting: Machine learners vs. financial economists. Expert Systems with Applications, 61, 215-234. https://doi.org/10.1016/j.eswa.2016.05.033
  34. Text, F. (2018). Accern and truerisk labs announce the next generation of unique machine-learning trading signals and portfolios, (July), 1-3.
  35. Tsai, C.-F. and Wang, S.-P. (2009). Stock price forecasting by hybrid machine learning techniques. Proceedings of the International MultiConference of Engineers and Computer Scientists, 1. Retrieved from http://www.iaeng.org/publication/IMECS2009/IMECS2009_pp755-760.pdf
  36. Wen, M. I. N., Li, P., Zhang, L., and Chen, Y. A. N. (2019). Stock market trend prediction using high-order information of time series. IEEE Access, 7, 28299-28308. https://doi.org/10.1109/ACCESS.2019.2901842
  37. Witten, I. H., Frank, E., and Hall, M. A. (2011a). Data Mining Practical Machine Learning Tools and Techniques (3rd ed.). Elsevier Inc.
  38. Witten, I. H., Frank, E., and Hall, M. A. (2011b). Data Mining Practical Machine Learning Tools and Techniques, 3rd Editio, 174-176.
  39. www.moneycontrol.com. (2018). MoneyControl.com. Retrieved from http://www.moneycontrol.com
  40. Yong, C. C., and Taib, S. M. (2009). Designing a decision support system model for stock investment strategy. Proceedings of the World Congress on Engineering and Computer Science 2009 Vol I, WCECS 2009, October 20-22, 2009, San Francisco, USA.
  41. Zhang, L., Aggarwal, C., and Qi, G.-J. (2017). Stock price prediction via discovering multi-frequency trading patterns. KDD '17: Proceedings of the 23rd ACM, 2141-2149. https://doi.org/10.1145/3097983.3098117