Stock price forecasting using hidden Markov model

Ernest Oseghale Amiens and Ifuero Osad Osamwonyi

International Journal of Information and Decision Sciences, 2022, vol. 14, issue 1, 39-59

Abstract: We used hidden Markov model (HMM) with single observation to estimate stock prices of selected manufacturing companies from the Nigerian Stock Exchange. Data from 22 November 2013 to 6 July 2018 were partitioned into two datasets for training and testing. Subsequently, the data were differenced, trained, tested and used to forecast closing prices for 60 days for each equity. The HMM was implemented with Matlab. The research revealed closing price prediction accuracy ranging from 3.33% to 96.67% and trade signal precision ranging from 31.67% to 97.67%. Also, the MAE values range from 0.0013 to 34.2867 while the MAPE values are between 0.1498% and 6.0034%. The hypothesis tested revealed that the model is efficient. Similarly, the comparison test conducted revealed the performance of HMM is better than ARIMA and neural network (NN). The research proposes that hidden Markov model be adopted in the exercise of stock price forecasting.

Keywords: stock forecasting; hidden Markov model; HMM; stock price; manufacturing firms; neural network; auto-regressive integrated moving average; ARIMA; mean absolute percentage error; MAPE; Nigerian Stock Exchange; NSE; forecast accuracy. (search for similar items in EconPapers)
Date: 2022
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