Abstract
Understanding of the stock market and ability to forecast the price move play the key role in the wealth generation for every investor. This paper attempts to apply Markov chain model to forecast the behavior of the single stocks from S&P 100 index. We provide the description of the discrete Markov model that aims to forecast upward or downward move based on historical statistics of stocks’ visit to particular state which is constructed using technical analysis. S&P 100 data from January 2008 to December 2015 was used to build the model. The analysis of the model on real-life out-of-sample data from January 2016 to August 2020 provides the proof that use of proposed model will generate higher profits in comparison with the buy-and-hold investment approach.
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Tyvodar, O., Prystavka, P. (2022). Discrete Markov Model Application for Decision-Making in Stock Investments. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 236. Springer, Singapore. https://doi.org/10.1007/978-981-16-2380-6_27
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DOI: https://doi.org/10.1007/978-981-16-2380-6_27
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