Abstract:

Stock market forecasting has always been difficult for investors, academics, and businesses. The uncertainty created by the COVID-19 epidemic has further added to the difficulty. The goal of this study is to see if using the transition matrix in the Markov chain, the stock return percentages of the energy sectors, which are becoming increasingly important in all aspects of our lives, can be used to estimate the risky situation on the stock markets in the COVID-19 period compared to the previous day. The daily stock movement fluctuations of 18 Turkish energy businesses trading in the BIST100 index over one year (2020/04-2021/04) are examined in this study. The transitions between the states, as well as their numbers, were determined in the study, and then the transition probability matrix was produced. Finally, based on previous data, the price movement for the following day was forecasted with a high degree of certainty. By comparing real and synthetic data, the accuracy of Markov chain predictions can be proved. The results demonstrate that utilizing Markov chains to anticipate stock market movements has a 77.77 percent success rate in the COVID-19 timeframe. The study's findings are intended to be beneficial to businesses and investors.

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