Developing an Intelligent Model to Predict Stock Trend Using the Technical Analysis

Document Type : Research Paper

Authors

1 M.Sc. student, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Assistant Prof., Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

3 Head of the Management Department at Faculty of Economics & Social Sciences, Shahid Chamran University of Ahvaz. Ahvaz, Iran

Abstract

Objective: The aim of this study is to predict trend in stock using both analytical methods of stock prediction and intelligent machine learning methods on the case study of the Tehran Stock Exchange index.
Methods: The proposed method consists of the following steps: at first, required data are collected. Afterwards, the data are evaluated using 25 analytical methods certified by Tehran stock exchange, Inc. Then, 10 highest rank methods are selected based on feature selection technique leading to a decrease in dimensions.
Results: The output of the final step is given to five intelligent machine learning methods, i.e., linear support vector machines, Gaussian kernel support vector machines, decision trees, Naïve Bayes and K nearest neighbors.
Conclusion: Eventually, majority voting approach is used to make the final decision. The advantage of the proposed technique is the flexibility to use any technical analysis methods which means there is almost no limitation for this approach. Moreover, the feature selection technique is utilized for technical analysis and these methods are prioritized.
 
 

Keywords

Main Subjects

References

References
Alavi, S. E., Sinaei, H. & Afsharirad, E. (2015). Predict the trend of stock prices using machine learning techniques, International Academic Journal of Economics, 2 (12), 1-11.
Alpaiden, E. (2004). Introduction to machine learning. First Edition. The MIT Press Cambridge Massuchusetts, London, England.
Chen, K.-Y., & Ho, C.-H. (2005). An improved support vector regression modeling for Taiwan Stock Exchange market weighted index forecasting. International Conference on Neural Networks and Brain. Beijing, China.
Dorodi, D., & Abrahimi, S. B. (2017). Presenting a new hybrid method for predicting the Stock Exchange price index. Financial Research Journal, 18 (4), 612-632. (in Persian)
Edwards, R. D., Magee, J. & Bassetti, W. C. (2007). Technical analysis of stock trends: CRC Press.
Enke, D., Thawornwong, S. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert systems with Applications, 29 (4), 927-940.
Fakhari, H. Valipour Khatir, M. & Mousavi, M. (2017). Investigating Performance of Bayesian and Levenberg-Marquardt Neural Network in Comparison Classical Models in Stock Price Forecasting. Financial Research Journal, 19 (2), 229-318. (in Persian)
Ford, N., Batchelor, B., & Wilkins, B. R. (1970). A learning scheme for the Nearest Neighbor Classifier. Information Sciences, 2 (2), 139-157.
Hassan, M. R., Nath, B., & Kirley, M. (2007). A fusion model of HMM, ANN and GA for stock market forecasting. Expert systems with Applications, 33 (1), 171-180.
Hafezi, R., Shahrabi, J., & Hadavandi, E. (2015). A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price. Applied Soft Computing, 29, 196-210.
Kim, E., Kim, W., & Lee, Y. (2003). Combination of multiple classifiers for the customer's purchase behavior prediction. Decision Support Systems, 34 (2), 167-175.
Kuo, R. J., Chen, C., & Hwang, Y. (2001). An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy sets and systems, 118 (1), 21-45.
Liu, C., Wang, J., Xiao, D., & Liang, Q. (2016). Forecasting S&P 500 Stock Index Using Statistical Learning Models. Open Journal of Statistics, 6 (06), 1067.
Mitchel, T. M. (1997). Machine Learning. First Edition, McGraw Hill Science.
Nabizade, A., Gharehbaghi, H. &  Behzadi, A. (2016).Index Tracking Optimization under down Side Beta and Evolutionary Based Algorithms. Financial Research Journal, 19 (2), 319-340. (in Persian)
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with Applications, 42 (1), 259-268.
Saad, E. W., Prokhorov, D. V., & Wunsch, D. C. (1998). Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on neural networks, 9 (6), 1456-1470.
Schöneburg, E. (1990). Stock price prediction using neural networks: A project report. Neurocomputing, 2 (1), 17-27.
Tsai, C.-F., Lin, Y.-C., Yen, D. C., & Chen, Y.-M. (2011). Predicting stock returns by classifier ensembles. Applied Soft Computing, 11 (2), 2452-2459.
Vapnik, V. (2013). The nature of statistical learning theory: Springer science & business media.
Xu, X., Zhou, C., & Wang, Z. (2009). Credit scoring algorithm based on link analysis ranking with support vector machine. Expert systems with Applications, 36 (2), 2625-2632.
Yang, H., Chan, L., & King, I. (2002). Support vector machine regression for volatile stock market prediction. International Conference on Intelligent Data Engineering and Automated Learning. Manchester, UK.
 
Financial Research Journal
Volume 20, Issue 2
2018
Pages 249-264

Files

Share

How to cite

Statistics