Uneingeschränkter Zugang

Trading using Hidden Markov Models during COVID-19 turbulences


Zitieren

Burhan, H. A., & Eylem, A. (2021). Adaptive Market Hypothesis and Return Predictability: A Hidden Markov Model Application in Borsa Istanbul. Sosyoekonomi.10.17233/sosyoekonomi.2021.02.02 Search in Google Scholar

Carrasco Sierra, A., Cobos Flores, M.J., Fuentes Duarte, B., Hernández Comi, B.I. (2020). Successful Management System by a Metalworking Mexican Company During COVID-19 Situation. Analysis Through a New Index (Case Study). International Journal of Entrepreneurial Knowledge, 8(2), 42-55.10.37335/ijek.v8i2.116 Search in Google Scholar

Cepoi, C. O. (2020). Asymmetric dependence between stock market returns and news during COVID-19 financial turmoil. Finance Research Letters, 36, 101658.10.1016/j.frl.2020.101658 Search in Google Scholar

Chandrika, P. V., Visalakshmi, K., & Srinivasan, K. S. (2020, March). Application of Hidden Markov Models in Stock Trading. In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 1144-1147). IEEE.10.1109/ICACCS48705.2020.9074387 Search in Google Scholar

Cuellar Andersson, J., & Fransson, L. (2016). Algorithmic Trading Based on Hidden Markov Models–Hidden Markov Models as a Forecasting Tool When Trying to Beat the Market. Search in Google Scholar

De la Torre, O. V., Galeana-Figueroa, E., & Alvarez-García, J. (2018). Using Markov-Switching models in Italian, British, US and Mexican equity portfolios: a performance test. Electronic Journal of Applied Statistical Analysis, 11(2), 489-505. Search in Google Scholar

Dima, A. & Vasilache, S. (2009). ANN Model for Corporate Credit Risk Assessment. Proceedings - 2009 International Conference on Information and Financial Engineering, ICIFE 2009. 94 - 98. 10.1109/ICIFE.2009.33.10.1109/ICIFE.2009.33 Search in Google Scholar

Fei, F., Fuertes, A. M., & Kalotychou, E. (2017). Dependence in credit default swap and equity markets: Dynamic copula with Markov-switching. International Journal of Forecasting, 33(3), 662-678.10.1016/j.ijforecast.2017.01.006 Search in Google Scholar

Gavurova, B., Ivankova, V., Rigelsky, M., Přívarová, M. (2020). Relations Between Tourism Spending and Global Competitiveness – an Empirical Study in Developed OECD Countries. Journal of Tourism and Services, 21(11), 38-54.10.29036/jots.v11i21.175 Search in Google Scholar

Hassan, M. R., Ramamohanarao, K., Kamruzzaman, J., Rahman, M., & Hossain, M. M. (2013). A HMM-based adaptive fuzzy inference system for stock market forecasting. Neurocomputing, 104, 10-25.10.1016/j.neucom.2012.09.017 Search in Google Scholar

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),192-196.10.1109/ISDA.2005.85 Search in Google Scholar

He, Z., O’Connor, F., & Thijssen, J. (2018). Is gold a Sometime Safe Haven or an Always Hedge for equity investors? A Markov-Switching CAPM approach for US and UK stock indices. International Review of Financial Analysis, 60, 30-37.10.1016/j.irfa.2018.08.010 Search in Google Scholar

Jurafsky, D. & Martin, J.H. (2021), Hidden Markvo Model, Stanford Online Course available at: http://web.stanford.edu/~jurafsky/slp3/A.pdf. Search in Google Scholar

Kim, E. C., Jeong, H. W., & Lee, N. Y. (2019). Global Asset Allocation Strategy Using a Hidden Markov Model. Journal of Risk and Financial Management, 12(4), 168.10.3390/jrfm12040168 Search in Google Scholar

Korzeb, Z., & Niedziółka, P. (2020). Resistance of commercial banks to the crisis caused by the COVID-19 pandemic: the case of Poland. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(2), 205-234. https://doi.org/10.24136/eq.2020.010.10.24136/eq.2020.010 Search in Google Scholar

Liu, N., Xu, Z., & Skare, M. (2021). The research on COVID-19 and economy from 2019 to 2020: analysis from the perspective of bibliometrics. Oeconomia Copernicana, 12(2), 217–268. https://doi.org/10.24136/oc.2021.009.10.24136/oc.2021.009 Search in Google Scholar

Liu, M., Huo, J., Wu, Y., & Wu, J. (2021). Stock Market Trend Analysis Using Hidden Markov Model and Long Short Term Memory. arXiv preprint arXiv:2104.09700. Search in Google Scholar

Machová, R., Korcsmáros, E., Esseová, M., Marča R. (2021). Changing Trends of Shopping Habits and Tourism During the Second Wave of COVID-19 – International Comparison. Journal of Tourism and Services, 22(12), 131-149.10.29036/jots.v12i22.256 Search in Google Scholar

Landmesser, J. (2021). The use of the dynamic time warping (DTW) method to describe the COVID-19 dynamics in Poland. Oeconomia Copernicana, 12(3), 539-556. https://doi.org/10.24136/oc.2021.018.10.24136/oc.2021.018 Search in Google Scholar

Marcucci, J. (2005). Forecasting stock market volatility with regime-switching GARCH models. Studies in Nonlinear Dynamics & Econometrics, 9(4).10.2202/1558-3708.1145 Search in Google Scholar

Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., & Mosavi, A. (2020). Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access, 8, 150199-150212.10.1109/ACCESS.2020.3015966 Search in Google Scholar

Nguyen, N., & Nguyen, D. (2015). Hidden markov model for stock selection. Risks, 3(4), 455-473.10.3390/risks3040455 Search in Google Scholar

Nguyen, N., & Nguyen, D. (2021). Global stock selection with hidden Markov model. Risks, 9(1), 9.10.3390/risks9010009 Search in Google Scholar

Oelschläger, L., & Adam, T. (2020). Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models. arXiv preprint arXiv:2007.14874.10.1177/1471082X211034048 Search in Google Scholar

Pardal, P., Dias, R., Šuleř, P., Teixeira, N., & Krulický, T. (2020). Integration in Central European capital markets in the context of the global COVID-19 pandemic. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(4), 627-650. https://doi.org/10.24136/eq.2020.027.10.24136/eq.2020.027 Search in Google Scholar

Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257-286.10.1109/5.18626 Search in Google Scholar

Ryou, H., Bae, H. H., Lee, H. S. & Oh, K. J. (2020). Momentum investment strategy using a hidden Markov model. Sustainability, 12(17), 7031.10.3390/su12177031 Search in Google Scholar

Rydén, T., Teräsvirta, T., & Åsbrink, S. (1998). Stylized facts of daily return series and the hidden Markov model. Journal of applied econometrics, 13(3), 217-244.10.1002/(SICI)1099-1255(199805/06)13:3<217::AID-JAE476>3.0.CO;2-V Search in Google Scholar

Talla, J. T. (2013). Impact of macroeconomic variables on the stock market prices of the Stockholm stock exchange (OMXS30). Jonkoping International Business School, 01-48. Search in Google Scholar

Tudor, N. L. (2014, May). Intelligent system for time series prediction in stock exchange markets. In International Conference on Business Information Systems (pp. 122-133). Springer, Cham.10.1007/978-3-319-06695-0_11 Search in Google Scholar

Uysal, A. S., & Mulvey, J. M. (2021). A Machine Learning Approach in Regime-Switching Risk Parity Portfolios. The Journal of Financial Data Science, 3(2), 87-108.10.3905/jfds.2021.1.057 Search in Google Scholar

Varenius, M. (2020). Using Hidden Markov Models to Beat OMXS30. Search in Google Scholar

Vieriu, R. L., Goraş, B., & Goraş, L. (2011, June). On HMM static hand gesture recognition. In ISSCS 2011-International Symposium on Signals, Circuits and Systems (pp. 1-4). IEEE.10.1109/ISSCS.2011.5978699 Search in Google Scholar

Visser, I., & Speekenbrink, M. (2010). depmixS4: an R package for hidden Markov models. Journal of statistical Software, 36(7), 1-21.10.18637/jss.v036.i07 Search in Google Scholar

Waduge, N., & Ganegoda, U. (2018, December). Forecasting Stock Price of a Company Considering Macroeconomic Effect from News Events. In 2018 3rd International Conference on Information Technology Research (ICITR) (pp. 1-5). IEEE.10.1109/ICITR.2018.8736133 Search in Google Scholar

Waliszewski, K., & Warchlewska, A. (2021). Comparative analysis of Poland and selected countries in terms of household financial behaviour during the COVID-19 pandemic. Equilibrium. Quarterly Journal of Economics and Economic Policy, 16(3), 577-615. https://doi.org/10.24136/eq.2021.021.10.24136/eq.2021.021 Search in Google Scholar

Zhang, J., Li, L., & Chen, W. (2021). Predicting stock price using two-stage machine learning techniques. Computational Economics, 57(4), 1237-1261.10.1007/s10614-020-10013-5 Search in Google Scholar