The Application of Technical Analysis in Stock Price Forecasting: Non-linear Probability Models and Artificial Neural Networks

Document Type : Research Paper

Authors

1 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

2 School of Progress Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

Stock price forecasting is one of the main challenges in stock market which investors and analysts are faced with. To forecast the future prices and future trend, different tools have been used among which we can refer to technical and fundamental analysis. It is noticed that technical analysis has good performance in short-time forecasting. Hence, in this paper, technical analysis has been used to estimate the probability function of stock prices. To forecast the direction of stock price movement in the following day, artificial neural networks (ANN), Logit, Probit, and extreme value models are utilized. To evaluate the performance of proposed models, daily values of Iran Khodro company stock are considered as a real case study. The nonparametric test of equality of ratios shows that the difference between the forecasting results of different models is not statistically significant. However, according to forecasting error criterion, the Probit model is more efficient than other mentioned models.
 

Keywords

References

احمدخان­بیگی، سهیل و عبدالوند، ندا، «پیش­بینی قیمت سهام با رویکرد ترکیبی شبکه عصبی مصنوعی و الگوریتم رقابت استعماری مبتنی بر تئوری آشوب»، فصلنامه راهبرد مدیریت مالی، (3)5، صص 27-73.
سارنج، علیرضا؛ کریمی، تورج و شهرامی بابکان، مجید، (1396). «کاربرد تئوری مجموعه­های راف برای پیش­بینی قیمت سهام (مطالعه موردی: بانک صادرات ایران)»، فصلنامه راهبرد مدیریت مالی، (3)5، صص 119-144.
سلمانی، سوده، (1389). «بررسی ناهمگنی در بورس اوراق بهادار تهران بر اساس رویکرد تکنیکال»، مجله مهندسی مالی و مدیریت پرتفوی، (2)، صص 139-165.
سوری، علی، (1393). «اقتصادسنجی (2)». تهران: فرهنگ­شناسی.
شمس، ناصر و پارسائیان، سمیرا، «مقایسه عملکرد مدل فاما و فرنچ و شبکه­های عصبی مصنوعی در پیش­بینی بازده سهام در بورس تهران»، مجله مهندسی مالی و مدیریت اوراق بهادار، (18)، صص 103-118.
فلاح­پور، سعید؛ گل­ارضی، غلامحسین و فتوره­چیان، ناصر، (1392). «پیش­بینی روند حرکتی قیمت سهام با استفاده از ماشین بردار پشتیبان بر پایه الگوریتم ژنتیک در بورس اوراق بهادار تهران»، تحقیقات مالی، (15)، صص 269-288.
فلاح­شمس، میرفیض و کردلوئی، حمیدرضا، (1390). «آزمون مدل‌های لاجیت و شبکه عصبی مصنوعی جهت پیش­بینی دستکاری قیمت در بورس اوراق بهادار تهران»، مجله مهندسی مالی و مدیریت اوراق بهادار، (7)، صص 37-69.
محمدی، شاپور؛ راعی، رضا و رحیمی، محمدرضا، (1397). «پیش‌بینی دامنه تغییرات طلا با استفاده از مدل ترکیبی ARIMA و شبکه عصبی»، مجله مهندسی مالی و مدیریت اوراق بهادار، (34)، صص 335-357.
نبوی چاشمی، سیدعلی و حسن­زاده، آیت­الله، (1390). «بررسی کارایی شاخص MA در تحلیل تکنیکال در پیش­بینی قیمت سهام»، مجله دانش مالی تحلیل اوراق بهادار، (10)، صص 86-106.
واعظ قاسمی، محسن و رمضانپور چهارده، سعید، (1397). «پیش­بینی ورشکستگی شرکت­های پذیرفته شده در سازمان بورس و اوراق بهادار با استفاده از شبکه عصبی مصنوعی»، فصلنامه دانش سرمایه گذاری، (26)، صص 277-296.
AhmadKhanBeygi, S. & Abdolvand, N. (2017). Stock Price Prediction Modeling Using Artificial Neural Network Approach and Imperialist Competitive Algorithm Based On Chaos Theory. Financial Management Strategy, 5(3), 27-73 (in Persian).
Armano, G., Marchesi, M., & Murru, A. (2005). A hybrid genetic-neural architecture for stock indexes forecasting. Information Sciences (170), 3 - 33.
Briza, A. C. & Naval Jr, P. C. (2011). Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data. Applied Soft Computing(11), 1191 - 1201.
Cao, Q. Leggio, K. B. & Schniederjans, M. j. (2005). A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market. Computers & Operations Research (32), 2499 - 2512.
Fallahpour, S. Golarzi, G. & Fatourechian, N. (2014). Prediction of the stock price trend using SVM based on genetic algorithm in Tehran stock exchange. Financial Research (15), 269 - 288 (in persian).
FallahShams, M. & KordLouie, H. (2011). Logit analysis and artificial neural network models to predict price manipulation in Tehran Stock Exchange. The Journal of Portfolio Management and Financial Engineering(7), 37 - 69 (in persian).
Jadhav, S. Dange, B. & Shikalgar, S. (2018). Prediction of Stock Market Indices by Artificial Neural Networks Using Forecasting Algorithms. In International Conference on Intelligent Computing and Applications (pp. 455-464). Springer, Singapore.
Kara, Y. Boyacioglu, M. & baykan, O. K. (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), 5311 - 5319.
Khashei, M. & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing (11), 2664 - 2675.
Lin, T. H. (2009). A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis,logit,probit and neural networks models. Neurocomputing(72), 3507 - 3516.
Marcjasz, G. Uniejewski, B. & Weron, R. (2018). On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks. International Journal of Forecasting.
Mohammadi, Sh. Raeie, R. & Rahimi, R. (2018). Interval Forcasting for Gold Price with hybrib model of ARIMA and Artificial Neural Network. The Journal of Portfolio Management and Financial Engineering(34), 335-357 (in persian).
Monfared, J. H. AhmadAliNejad, M. & Metghalchi, S. (2011). The Comparison of neural network and time series Baks- Jenkins models in forecasting Tehran Stock Exchange price index. The Journal of Financial and Management Engineering Exchange(11), 1-16 (in persian).
Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York: New York Institute of Finance.
NabaviChashmi, A. & HassanZadeh, A. (2011). Study of Indicator Performance in technical analysis of stock price forecasting. The Journal of Financial Knowledge of Securities Analysis(10), 83 - 106 (in persian).
Salamani, S. (2010). Study of heterogeneity in the stock market based on technical analysis. The Journal of Portfolio Management and Financial Engineering (2), 139 - 165 (in persian).
Saranj, A. Karimi, T. & Shahramin Babakan, M. (2017). "The Application of Rough Set Theory in Stock Price Forecasting (Case Study: Iran Saderat Bank). Financial Management Strategy, 5(3), 119-144 (in persian).
Shams, N. & Parsaiyan, S. (2011). The Comparison between performance of Fama and French and artificial neural networks models in predicting stock returns in Tehran stock exchange. The Journal of Financial and Management Engineering Exchange(18), 103 - 118 (in persian).
Souri, A. (2014). Econometrics (2). Tehran: Farhangshenasi (in persian).
Ullah Khan, A. Bandopadhyaya, T. K. & Sharma, S. (2008). Comparisons of Stock Rates Prediction Accuracy using Different Technical Indicators with Backpropagation Neural Network and Genetic Algorithm Based Backpropagation Neural Network. First International Conference on Emerging Trends in Engineering and Technology, 16-18 July 2008,India, 575 - 580.
Vaez-Ghasemi, M. & Ramezanpour Chardeh, S. (2018). Predicting bankruptcy of companies listed on the Stock Exchange using the artificial neural network. The Journal of Investment Knowledge (26), 277-296 (in Persian).
Zhai, Y. Hsu, A. & Halgamuge, S. K. (2007). Combining News and Technical Indicators in Daily Stock Price Trends Prediction. Lecture Notes In Computer Science, 1087 - 1096.

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History

  • Receive Date: 09 February 2018
  • Revise Date: 20 September 2018
  • Accept Date: 09 October 2018

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