Using Markov Chains in Prediction of Stock Price Movements: A Study on Automotive Industry
Stock price prediction is on the agenda of most researchers based on the uncertainty in its nature. In past two decades, the literature on the development of prediction models for stock prices has extended dramatically. These studies mostly focused on specific industries such as banking and finance, petroleum, manufacturing, and automotive. In line with prior studies, the aim of this study is also to investigate the efficiency of Markov Chains Model, which is one of the most commonly applied models, in predicting the stock price movements for the firms operating in automotive industry and to reveal the possible contribution it can make to the decision making process of investors. Automotive industry is not only a major and industrial force worldwide, but also is a locomotive power that serves to many other industries. Thus, this study considers the firms operating in automotive industry and daily closing stock prices of all 13 automotive companies are collected for the calendar year of 2015. By defining three possible states (decrease, increase, and no change), individual state transition probability matrixes are formed for each company. Then, using the probabilities provided with these matrixes, different investment strategies are evaluated for the first five working days of 2016. According to the results of analysis, it is concluded that applying Markov Chains generates a positive income or at least minimizes the loss.
Abugri, B.A. (2008) “Empirical relationship between macroeconomic volatility and stock returns: Evidence from Latin American markets”, International Review of Financial Analysis, Vol. 17, pp. 396–410.
Abu-Mostafa, Y. S. and Atiya, A. F. (1996). “Introduction to financial forecasting”. Applied Intelligence, Vol. 6 No.3, pp. 205–213
Albuquerque, R. A., Francisco, E. De. and Marques, L. B. (2008). “Marketwide private information in stocks: Forecasting currency returns”. Journal of Finance, Vol. 63 No.5, pp. 2297–2343.
Anderson, T. W. and Goodman, L. A. (1957) "Statistical inference about Markov chains”. The Annals of Mathematical Statistics, pp. 89-110.
Balvers, R.J., Cosimano, T.F. and McDonald, B. (1990) “Predicting stock returns in an efficient market”, The Journal of Finance, Vol. 45 No. 4, pp. 1109-1128.
Boyacıoğlu, M.A. and Avcı, D. (2010) “An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange”, Expert Systems with Applications, Vol. 37, pp. 7908–7912
Campbell, J. Y., Lettau, M., Malkiel, B. and Xu, Y. (2001). “Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk”. Journal of Finance, Vol. 56, pp 1–43.
Chang, T. S. (2011) “A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction”, Expert Systems with Applications, Vol. 38, pp. 14846–14851
Choji, D.N., Eduno, S. N. and Kassem, G.T. (2013), “Markov Chain Model Application on Share Price Movement in Stock Market”, Computer Engineering and Intelligent Systems, Vol.4, No.10, pp. 84-95
Errunza, V. and Hogan, K. (1998). “Macroeconomic determinants of European stock market volatility”. European Financial Management, Vol. 4, pp. 361–377.
Fama, E.F. (1965), “Random walks in stock market prices”, Financial Analysts Journal, Vol. 21 No. 5, pp. 55-59.
Fielitz, B. D. and Bhargava, T. N. (1973). “The behavior of stock-price relatives—a Markovian analysis”. Operations Research, Vol. 21 No. 6, pp. 1183-1199.
Gupta, M. P. (2006). Quantitative techniques for decision making. PHI Learning Pvt. Ltd.
Hamilton, J. D. and Lin, G. (1996). “Stock market volatility and the business cycle”. Journal of Applied Econometrics, Vol. 11, pp. 573–593.
Hillier, F. S., and Lieberman, G. J. (2005) Introduction to operations research, 8th edition, McGraw Hill, New York, NY
Howard, R. A. (1971). Dynamic Probabilistic Systems. Volume I: Markov Models. Volume II: Semi-Markov and Decision Processes.
Kara, Y., Boyacioglu, M.A. and Baykan, O.K. (2011), “Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of Istanbul stock exchange”, Expert Systems with Applications, Vol. 38 No. 5, pp. 5311-5319.
Kim, M.J., Min, S.H. and Han, I. (2006), “An evolutionary approach to the combination of multiple classifiers to predict a stock price index”, Expert Systems with Applications, Vol. 31 No. 2, pp. 241-247.
Lange, K. (2010). Applied probability. Springer Science & Business Media.
Leung, M. T., Daouk, H. and Chen, A. S. (2000). “Forecasting stock indices: A comparison of classification and level estimation models”. International Journal of Forecasting, Vol. 16, pp. 173–190.
Obodos, E. (2005), “Predicting stock market prices in Nigeria: a preliminary investigation”, MBA Thesis, University of Benin, Benin City.
Pai, P.F. and Lin, C.S. (2005), “A hybrid ARIMA and support vector machines model in stock price forecasting”, Omega, Vol. 33 No. 6, pp. 497-505.
Pierdzioch, C., Döpke, J. and Hartmann, D. (2008) “Forecasting stock market volatility with macroeconomic variables in real time”, Journal of Economics and Business, Vol. 60, pp. 256–276
Ross, S. M. (2014). Introduction to probability models. Academic press.
Schwert, W. G. (1989) “Why does stock market volatility change over time?” Journal of Finance, Vol. 44, pp. 1115–1153.
Stock, J. H. And Watson, M. W. (2007) “Why has US inflation become harder to forecast?”, Journal of Money, Credit and Banking, Vol. 39 No. S1, pp. 3–33.
Tan, T. Z., Quek, C. and See, Ng. G. (2007). “Biological brain-inspired genetic complementary learning for stock market and bank failure prediction”, Computational Intelligence, Vol. 23 No. 2, pp. 236–261.
Taylor, B. W. (1996), Introduction to management science. Mc-Graw-Hill, New York, NY
Taylor, H. M. and Karlin, S. (1994). An introduction to stochastic modeling. Academic press.
Vasanthi, S., Subha, M. V. and Nambi, S. T. (2011). “An empirical study on stock index trend prediction using markov chain analysis”, Journal of Banking Financial Services and Insurance Research, Vol. 1 No. 1, pp. 72-91.
Wang, J. Z., Wang, J.J., Zhangi Z.G. and Guo, S. P. (2011), “Forecasting stock indices with back propagation neural network”, Expert Systems with Applications, Vol. 38, pp. 14346–14355.
Wang, Y.F. (2002), “Predicting stock price using fuzzy grey prediction system”, Expert System with Applications, Vol. 22 No. 1, pp. 33-38.
Winston, W. L. and Goldberg, J. B. (2004). Operations research: applications and algorithms (Vol. 3). Belmont: Thomson Brooks/Cole.
Wu, B. and Duan, T. (2017), “A Performance Comparison of Neural Networks in Forecasting Stock Price Trend”, International Journal of Computational Intelligence Systems, Vol. 10, pp. 336–346.
Copyright (c) 2018 International Journal of Contemporary Economics and Administrative Sciences
This work is licensed under a Creative Commons Attribution 4.0 International License.
The Author(s) must make formal transfer of copyright for each article prior to publication in the International Journal of Contemporary Economics and Administrative Sciences. Such transfer enables the Journal to defend itself against plagiarism and other forms of copyright infringement. Your cooperation is appreciated. You agree that copyright of your article to be published in the International Journal of Contemporary Economics and Administrative Sciences is hereby transferred, throughout the World and for the full term and all extensions and renewals thereof, to International Journal of Contemporary Economics and Administrative Sciences.
The Author(s) reserve(s): (a) the trademark rights and patent rights, if any, and (b) the right to use all or part of the information contained in this article in future, non-commercial works of the Author's own, or, if the article is a "work-for-hire" and made within the scope of the Author's employment, the employer may use all or part of the information contained in this article for intra-company use, provided the usual acknowledgements are given regarding copyright notice and reference to the original publication.
The Author(s) warrant(s) that the article is Author's original work, and has not been published before. If excerpts from copyrighted works are included, the Author will obtain written permission from the copyright owners and shall credit the sources in the article. The author also warrants that the article contains no libelous or unlawful statements, and does not infringe on the rights of others. If the article was prepared jointly with other Author(s), the Author agrees to inform the co-Author(s) of the terms of the copyright transfer and to sign on their behalf; or in the case of a "work-for-hire" the employer or an authorized representative of the employer.
The journal is registered with the ISSN : 1925-4423.
IJCEAS is licensed under a Creative Commons Attribution 4.0 International License.
This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.