International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749

Forecasting the Long-Run Behavior of the Stock Price of Some Selected Companies in the Malaysian Construction Sector: A Markov Chain Approach

Wajeeh Mustafa Sarsour
School of Mathematical Sciences, Universiti Sains Malaysia, Penang, Malaysia.

Shamsul Rijal Muhammad Sabri
School of Mathematical Sciences, Universiti Sains Malaysia, Penang, Malaysia.

DOI https://doi.org/10.33889/IJMEMS.2020.5.2.024

Received on August 11, 2019
  ;
Accepted on December 18, 2019

Abstract

The fluctuations in stock prices produce a high risk that makes investors uncertain about their investment decisions. The present paper provides a methodology to forecast the long-term behavior of five randomly selected equities operating in the Malaysian construction sector. The method used in this study involves Markov chains as a stochastic analysis, assuming that the price changes have the proparty of Markov dependency with their transition probabilities. We identified a three-state Markov model (i.e., increase, stable, fall) and a two-state Markov model (i.e., increase and fall). The findings suggested that the chains had limiting distributions. The mean return time was computed for respective equities as well as to determine the average duration to return to a stock price increase. The analysis might aid investors in improving their investment knowledge, and they will be able to make better decisions when an equity portfolio possesses higher transition probabilities, higher limiting distribution, and lowest mean return time in response to a price increase. Finally, our investigations suggest that investors are more likely to invest in the GKent based on the three-state model, while VIZIONE seems to be a better investment choice based on a two-state model.

Keywords- Stock price, Markov chain, Transition matrix, Expected mean return time.

Citation

Sarsour, W. M., & Sabri, S. R. M. (2020). Forecasting the Long-Run Behavior of the Stock Price of Some Selected Companies in the Malaysian Construction Sector: A Markov Chain Approach. International Journal of Mathematical, Engineering and Management Sciences, 5(2), 296-308. https://doi.org/10.33889/IJMEMS.2020.5.2.024.

Conflict of Interest

The authors confirm that there is no conflict of interest to declare for this publication.

Acknowledgements

The authors would like to express their sincere thanks to the editor and anonymous reviews for their time and valuable suggestions. This research was supported in part by the School of Mathematical Sciences at Universiti Sains Malaysia.

References

Aguilera, A.M., Ocaña, F.A., & Valderrama, M.J. (1999). Stochastic modelling for evolution of stock prices by means of functional principal component analysis. Applied Stochastic Models in Business and Industry, 15(4), 227-234.

Akbari, A. (2013). The study and evaluation of stocks’ valuation models in Tehran stock exchange. European Online Journal of Natural and Social Sciences, 2(3), 2152-2160.

Amiri, A., Ravanpaknodezh, H., & Jelodari, A. (2016). Comparison of stock valuation models with their intrinsic value in Tehran stock exchange. Marketing and Branding Research, 3, 24-40. SSRN: https://ssrn.com/abstract=3340384.

Bairagi, A., & Kakaty, S. (October, 2015). Analysis of stock market behaviour: a Markov chain approach. International Journal Recent Scientific Research, 6(10), 7061-7066.

Bhat, U.N. (1984). Elements of applied stochastic processes. Second edition. New York: John Wiley & Sons.

Bhusal, M.K. (October, 2017). Application of Markov chain model in the stock market trend analysis of Nepal. International Journal of Scientific & Engineering Research, 8(10), 1733-1745.

Hassan, M.R., & Nath, B. (2005, September). Stock market forecasting using hidden Markov model: a new approach. In 5th International Conference on Intelligent Systems Design and Applications (ISDA'05) (pp. 192-196). IEEE. Warsaw, Poland. DOI: 10.1109/ISDA.2005.85

Mettle, F.O., Quaye, E.N.B, & Laryea, R.A. (2014). A methodology for stochastic analysis of share prices as Markov chains with finite states. SpringerPlus, 3, 657. doi:10.1186/2193-1801-3-657.

Obasi, R., Abdullahi, S.R., & Ayila, J. (2018). The behaviour of stock market return in two west African capital market: a Markovian analysis. International Journal of Organization & Business Execellence, 3(1), 21-51.

Onwukwe, C.E., & Samson, T.K. (2014). On Predicting the long run behaviour of Nigerian bank stocks prices: a Markov chain approach. American Journal of Applied Mathematics and Statistics, 2(4), 212-215.

Sahoo, P.K., & Charlapally, K. (2015). Stock price prediction using regression analysis. International Journal of Scientific & Engineering Research, 6(3), 1655-1659.

Xi, X., Mamon, R., & Davison, M. (2014). A higher-order hidden Markov chain-modulated model for asset allocation. Journal of Mathematical Modelling and Algorithms in Operations Research, 13, 59-85.

Zhang, D., & Zhang, X. (2009). Study on forecasting the stock market trend based on stochastic analysis method. International Journal of Business Management, 4(6), 163-170.

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