Research on HMM-Based Efficient Stock Price Prediction

Abstract

Stock market is one of the most important parts of the investment market. Compared with other industries, the stock market not only has a higher rate of return on investment but also has a higher risk, and stock price prediction has always been a close concern of investors. Therefore, the research on stock price prediction methods and how to reduce the error of stock price prediction has become a hot topic for many scholars at home and abroad. In recent years, the development of computer technology such as machine learning and econometric method makes the stock price prediction more reliable. Due to the hidden Markov nature of stock price, this paper proposes a stock price prediction method based on hidden Markov model (HMM). To be specific, since the data of stock price have continuity in time series, it is necessary to extend the discrete HMM to the continuous HMM, and then put forward the up and down trend prediction model based on the continuous HMM. The first-order continuous HMM is extended to the second-order continuous HMM, and the stock price is predicted by combining the prediction method of fluctuation range. As a result, the proposed second-order continuous HMM-based stock price prediction model is simulated on Hang Seng Index (HSI), one of the earliest stock market indexes in Hong Kong. The evaluation results on six months HSI show that the predicted value of the proposed model is very close to the actual value and outperforms three benchmarks in terms of RMSE, MAE, and R2.

Forecasting Equity using LSTM Value-at-Risk Estimation

“…SGD Regressor: Stochastic gradient descent (SGD) is a method for updating a model with a decreasing strength schedule based on estimates of the loss gradient at each sample (Fig. 7) [24].…”

Integration of Ontology Transformation into Hidden Markov Model

“…Hidden Markov model (HMM) was created by Rabiner and Juang after being introduced by Rabiner and Juang [15]. It is employed for classification, prediction, comparison, and speech and pattern recognition [16][17][18][19][20].…”