The Improved Semi-Parametric Markov Switching Models for Predicting Stocks Prices
الموضوعات :hossien naderi 1 , Mehrdad Ghanbari 2 , Babak Jamshidi Navid 3 , Arash Nademi 4
1 - ایلام دانشگاه آزاد ایلام
2 - استادیار گروه حسابداری، دانشکده علوم انسانی، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران
3 - Department of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
4 - عضو هیات علمی دانشگاه آزاد اسلامی واحد ایلام
الکلمات المفتاحية: Strategies for buying and selling, Kernel function, EM algorithm, Markov Switching Model.s,
ملخص المقالة :
The modeling of strategies for buying and selling in Stock Market Investment have been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Semi-parametric Markov Switching models for fore-casting the time series observations based on stock prices. The Semi-parametric Markov Switching models for these models are a class of popular methods that have been used extensively by researchers to increase the accu-racy of fitting processes. The main part of these models is based on kernel and core functions. Despite of existence of many kernel and core functions that are capable in applications for forecasting the stock prices, there is a widely use of Gaussian kernel and exponential core function in these mod-els. But there is a question if other types of kernel and core functions can be used in these models. This paper tries to introduce the other kernel and core functions can be offered for good fitting of the financial data. We first test three popular kernel and four core functions to find the best one and then offer the new strategy of buying and selling stocks by the best selection on these functions for real data.
[1] Aghaeefar, N., Pourzarandi, M., Kazemi, M., and Minoie, M., Applying Optimized Mathematical Algo-rithms to Forecast Stock Price Average Accredited Banks in Tehran Stock Exchange and Iran Fara Bourse, Advances in Mathematical Finance and Applications, 2019:77-94. doi: 10.22034/amfa.2019.580802.1150
[2] Billio, M., Casarin, R., Ravazzolo, F., and Van Dijk, H. K., Interconnections between Eurozone and US booms and busts using a Bayesian panel Markov switching VAR model, Journal of Applied Econometrics, 2016. doi:10.1002/jae.2501
[3] Chan, K. C. G., and Wang, M. C., Semiparametric modeling and estimation of the terminal behavior of recurrent marker processes before failure events, Journal of the American Statistical Association, 2017;112(517): 351-362. DOI: 10.1080/01621459.2016.1140051
[4] Chang, Y., Yongok, C., and Park, J.Y., A New Approach to Model Regime Switching, Journal of Econ-ometrics, 2017;196(1): 127-143. doi:10.1016/j.jeconom.2016.09.005
[5] Chang, J., Tang, C. Y. and Wu, Y., Local Independence Feature Screening for Nonparametric and Sem-iparametric Models by Marginal Empirical Likelihood, Annals of statistics, 2016;44(2):515-539. doi: 10.1214/15-AOS1374
[6] Chen, C. M., Shen, P. S., Wei, J. C., and Lin, L., A semiparametric mixture cure survival model for left-truncated and right-censored data, Biometrical journal, 2017; 59(2): 270-290. doi:10.1002/bimj.201500267
[7] Davallou, M., and Meskinimood, S., Performance examination of trading strategy based on Stochastic Dominance, Financial Knowledge of security analysis (Financial studies), 2019; 12:171-193.
[8] Davoodi, A., and Dadashi, I., Stock price prediction using the Chaid rule-based algorithm and particle swarm optimization (pso), Advances in Mathematical Finance and Applications, 2020;197-213. doi:10.22034/amfa.2019.585043.1184
[9] Di Persio, L., Frigo, M., Gibbs sampling approach to regime switching analysis of financial time series, Journal of Computational and Applied Mathematics, 2016. doi:10.1016/j.cam.2015.12.010
[10] Di Persio, L., Frigo, M., Maximum Likelihood Approach to Markov Switching Models, WSEAS Trans-actions on Business and Economics, 2015; 12: 239-242.
[11] Doaei, M., Mirzaei, S,A., and Rafigh, M., Hybrid Multilayer Perceptron Neural Network with Grey Wolf Optimization for Predicting Stock Market, Advances in Mathematical Finance and applications, 2021; 883-894. doi: 10.22034/amfa.2021.1903474.1452
[12] Franke, J., Stockis, J.P., Kamgaing, J.T., Li, W.K., Mixtures of nonparametric autoregressions, J. Non-parametric statistics, 2011; 23: 287-303. doi: 10.1080/10485252.2010.539686
[13] Gu, X., Ma, Y., and Balasubramanian, R., Semiparametric Time to Event Models in the Presence of Error-Prone, Self-Reported Outcomes-with Application to the Women's Health Initiative, the annals of applied statistics, 2015; 9(2): 714-730. doi: 10.1214/15-AOAS810
[14] Gupta, C., Cobre, J., Polpo, A., and Sinha, D., Semiparametric Bayesian estimation of quantile func-tion for breast cancer survival data with cured fraction, Biometrical journal. Biometrische Zeitschrift, 2016; 58(5): 1164-77. doi:10.1002/bimj.201500111
[15] Nademi, A., and Farnoosh, R., Mixtures of autoregressive-autoregressive conditionally heteroscedas-tic models: semi-parametric approach, Journal of Applied Statistics, 2014, 41(2), 275-293. doi:10.1080/02664763.2013.839129
[16] Nademi, A. and Nademi, Y., Forecasting crude oil prices by a semiparametric Markov switching mod-el: OPEC, WTI, and Brent cases, Energy Economics, 2019; 74: 757-766. doi:10.1016/j.eneco.2018.06.020
[17] Neale, M. C., Clark, S. L., Dolan, C. V., Hunter, M. D., Regime Switching Modeling of Substance Use: Time-Varying and Second-Order Markov Models and Individual Probability Plots, Structural equation modeling: a multidisciplinary journal, 2016; 23(2):221-233. doi: 10.1080/10705511.2014.979932
[18] Pashaei, Z., and Dehkharghani, R., Stock Market Modeling Using Artificial Neural Network and Comparison with Classical Linear Models, Signal and Data Processing, 2021, 17. doi:10.29252/jsdp.17.4.89
[19] Pourzamani, Z., Heidarpour, F., and Mohammadi, M., Comparative study of stock trading strategy in the long term investment, Financial engineering and securities management, 2011:7: 215-230.
[20] Rastegar, M. A., Dastpak, M., Developing a High-Frequency Trading system with Dynamic Portfolio Management using Reinforcement Learning in Iran Stock Market, Financial research journal, 2018; 20.
doi: 10.22059/jfr.2017.230613.1006415
[21] Sharif-far, A., Araghi, M.K., Vanani, I.R., Fallahshams, M., The Assessment of the optimal Deep Learning Algorithm on Stock Price Prediction (Long Short-Term Memory Approach), Quarterly Journal of Financial Engineering and Securities Management,2021.
[22] Von Ganske, J., A Regime Switching Partial Least Squares Approach to Forecasting Industry Stock Returns, Mimeo Edhec-Risk Institute, 2016.
