Using Markov Chains in Prediction of Stock Price Movements: A Study on Automotive Industry

Autores

  • Görkem Sarıyer Yasar University
  • Ece Acar Yasar University
  • Mustafa Gürol Durak Yasar University

Palavras-chave:

Stock Price Prediction, Markov Chains, Automotive Industry

Resumo

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.

Referências

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.

Downloads

Publicado

2018-12-31

Como Citar

Sarıyer, G., Acar, E., & Durak, M. G. (2018). Using Markov Chains in Prediction of Stock Price Movements: A Study on Automotive Industry. International Journal of Contemporary Economics and Administrative Sciences, 8(2), 178–199. Obtido de http://ijceas.com/index.php/ijceas/article/view/255

Edição

Secção

Articles