Stochastic Modeling for the Analysis and Forecasting of Stock Market Trend using Hidden Markov Model
Asian Journal of Probability and Statistics,
Page 43-56
DOI:
10.9734/ajpas/2022/v18i130436
Abstract
The HMM is generally applied to forecast the hidden system of observation data. In this paper, we deal with the development of HMM for a proper understanding of finance variables in the stock market. Formulation of relationships between and within both the changing share values of Housing Development Finance Corporation Bank Limited (HDFC Bank Ltd) as visible/observed states influenced by the indicators of S&P Bombay Stock Exchange Sensitive Index (Sensex) as invisible/influencing states. Stochastic modeling with hidden Markov models is carried out for exploring various parameters of the model. Mathematical derivations for all the required statistical measures are obtained using the method of moments for the proposed probability distribution. Deducing mathematical formulation of initial probability vector, transition and observed probability matrices were carried out with the empirical data sets. Probability distribution for visible states of various lengths is obtained. It is observed from the empirically analysis that there is the maximum likelihood of rising the share prices of HDFC bank in consecutive two days. Furthermore, an attempt is made to estimate the long-run steady-state behavior of both the SENSEX and HDFC Bank share prices. The share value of HDFC bank will be on rising state from the 19th day onwards and it may be recommended for good investment choice for the long run. The findings of these studies will be valid for effective decision-making in portfolio management.
Keywords:
- Markov chain
- hidden Markov models
- parameters of HMM
- stock market
- portfolio management
How to Cite
References
A Kuo R J, Lee L C and Lee C F. Integration of Artificial NN and Fuzzy Delphi for Stock market forecasting. IEEE International Conference on Systems, Man, and Cybernetics. 1996;2:1073-1078.
Umar Ms and Musa Tm. Stock prices and firm earning per share in Nigeria. JORIND. 2013;11(2):187- 192.
Dar Q, Dar,GF, Ma JH, Ahn YH. Visualization, Economic Complexity Index, and Forecasting of South Korea International Trade Profile: A Time Series Approach. Journal of Korea Trade. 2020;24(1):131-145.
Kimoto T, Asakawa K, Yoda M and Takeoka M. Stock market prediction system with modular neural networks. Proc. International Joint Conference on Neural Networks, San Diego. 1990;1: 1-6.
Rabiner LR. A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE. 1989;77(2):257–286.
Landeta JMI, Cortés CBY, Azúa HM. Markovian decision process to find optimal policies in the management of an orange farm. Investigación Operacional. 2014;35(1):68-77.
Alghamdi R. Hidden Markov models (HMMs) and security applications. International Journal of Advanced Computer Science and Applications. 2016;7(2):39-47.
Hassan MR, Nath B. Stock market forecasting using hidden Markov model: a new approach. In 5th International Conference on Intelligent Systems Design and Applications IEEE, (ISDA'05). 2005;192-196.
Guidolin, Massimo, Allan Timmermann. The architecture of complex weighted networks. SSRN FRB of St. Louis Working Paper No. 2005-002C, FRB of St. Louis, MO, USA; 2006.
Kritzman, M, Page S, Turkington D. Regime shifts: Implications for dynamic strategies (corrected). Financial Analysts Journal. 2012;68(3):22-39.
Gupta, A, and Dhingra B. Stock market prediction using hidden Markov models. In 2012 Students Conference on Engineering and Systems IEEE. 2012;1-4.
Kavitha G, Udhayakumar A, Nagarajan D. Stock market trend analysis using hidden Markov models. arXiv preprint arXiv:1311.4771; 2013.
Nobakht B, Joseph CE, Loni B. Stock market analysis and prediction using hidden Markov models. Paper presented at the 2012 Students Conference on IEEE Engineering and Systems (SCES), Allahabad, Uttar Pradesh, India, March 16–18. 2012;1–4.
Tuyen LT. Markov financial model using hidden Markov model. International Journal of Applied Mathematics and Statistics. 2013;40(10):72-83.
Nguyen, Nguyet Thi. Probabilistic Methods in Estimation and Prediction of Financial Models. (Doctoral dissertation, The Florida State University; 2014.
Nguyen N, Nguyen D. Hidden Markov Model for Stock Selection. Risks. 2016;3:455–473.
Holzmann H, Schwaiger F. Testing for the number of states in hidden Markov models. Computational Statistics & Data Analysis. 2016;100:318-330.
Liu Z, Wang S. Decoding chinese stock market returns: Three-state hidden semi-Markov model. Pacific-Basin Finance Journal. 2017;44:127-149.
Nguyen N. Hidden Markov model for stock trading. International Journal of Financial Studies. 2018;6(2):36.
Huang M, Huang Y, HE K. Estimation and testing non-homogeneity of Hidden Markov model with application in financial time series. Statistics and Its Interface. 2019;12(2):215-225.
Suda D, Spiteri L. Analysis and Comparison of Bitcoin and S and P 500 Market Features Using HMMs and HSMMs. Information. 2019;10(10):322.
Liu D. Markov modulated jump-diffusions for currency options when regime switching risk is priced. International Journal of Financial Engineering. 2019;6(04):1950038.
Functions of Markov process and to a model for ecology. Bulletin of the American Mathematical Society. 1967;73(3):360-363.
Baum L.E. and Sell, G.R. Growth functions for transformations on manifolds. Pac. J. Math. 1968;27(2):211-227.
Forney GD. The Viterbi algorithm. IEEE. 1973;61:268–278.
Viterbi AJ. Error bounds for convolutional codes and an asymptotically optimal decoding algorithm. IEEE transactions on Information Theory. 1967;13(2):260-269.
-
Abstract View: 82 times
PDF Download: 27 times
