Stock market forecasting using hidden Markov model: a new approach

2005 ◽  
Author(s):  
M.R. Hassan ◽  
B. Nath
Keyword(s):  
Stock Market ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
New Approach ◽  
Stock Market Forecasting

Predicting the effect of Googling investor sentiment on Islamic stock market returns

2020 ◽  
Vol 13 (2) ◽  
pp. 165-193
Author(s):  
Yousra Trichilli ◽  
Mouna Boujelbène Abbes ◽  
Afif Masmoudi
Keyword(s):  
Stock Market ◽  
Markov Model ◽  
Financial Markets ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Investor Sentiment ◽  
Market Returns ◽  
Stock Market Returns ◽  
Content Type ◽  
The Relationship

Purpose The purpose of this paper is to evaluate the capability of the hidden Markov model using Googling investors’ sentiments to predict the dynamics of Islamic indexes’ returns in the Middle East and North Africa (MENA) financial markets from 2004 to 2018. Design/methodology/approach The authors propose a hidden Markov model based on the transition matrix to apprehend the relationship between investor’s sentiment and Islamic index returns. The proposed model facilitates capturing the uncertainties in Islamic market indexes and the possible effects of the dynamics of Islamic market on the persistence of these regimes or States. Findings The bearish state is the most persistent sentiment with the longest duration for all the MENA Islamic markets except for Jordan, Morocco and Qatar. In addition, the obtained results indicate that the effect of sentiment on predicting the future Islamic index returns is conditional on the MENA States. Besides, the estimated mean returns for each state indicates that the bullish and calm states are ideal for investing in Islamic indexes of Bahrain, Oman, Morocco, Kuwait, Saudi Arabia and United Arab Emirates. However, only the bullish state is ideal for investing Islamic indexes of Jordan, Egypt and Qatar. Research limitations/implications This paper has used data at a monthly frequency that can explain only short-term dynamics between Googling investor’s sentiment and the MENA Islamic stock market returns. Moreover, this work can be done on the stock markets while taking into account the specificity of each activity sector. Practical implications In fact, the findings of this paper are helpful for academics, analysts and practitioners, and more specifically for the Islamic MENA financial investors. Moreover, this study provides useful insights not only into the duration of the relationship between the indexes’ returns and the investors’ sentiments in the five states but also into the transition probabilities which have implications for how investors could be guided in their choice of future investment in a portfolio with Islamic indexes. Findings of this paper are important and valuable for policy-makers and investors. Thus, predicting the effect of Googling investors’ sentiment on the MENA Islamic stock market dynamics is important for portfolio diversification by domestic and international investors. Moreover, the results of this paper gave new insights into financial analysts about the dynamic relationship between Googling investors’ sentiment and Islamic stock market returns across market regimes. Therefore, the findings of this study might be useful for investors as they help them capture the unobservable dynamics of the changes in the investors’ sentiment regimes in the MENA financial markets to make successful investment decisions. Originality/value To the best of the authors’ knowledge, this paper is the first to use the hidden Markov model to examine changes in the Islamic index return dynamics across five market sentiment states, namely the depressed sentiment (S1), the bullish sentiment (S2), the bearish sentiment (S3), the calm sentiment (S4) and the bubble sentiment (S5).

scholarly journals Enhancing stock market prediction with extended coupled hidden Markov model over multi-sourced data

2018 ◽  
Vol 61 (2) ◽  
pp. 1071-1090 ◽  
Author(s):  
Xi Zhang ◽  
Yixuan Li ◽  
Senzhang Wang ◽  
Binxing Fang ◽  
Philip S. Yu
Keyword(s):  
Stock Market ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Stock Market Prediction

scholarly journals Stock Market Trend Prediction Using Hidden Markov Model

2020 ◽  
Author(s):  
Deneshkumar Venugopal ◽  
Senthamarai Kannan Kaliyaperumal ◽  
Sonai Muthu Niraikulathan
Keyword(s):  
Stock Market ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Stock Prices ◽  
Hidden Markov ◽  
Decision Makers ◽  
Trend Prediction ◽  
Steady State Probability ◽  
Forecasting Methods ◽  
The One

In Recent years many forecasting methods have been proposed and implemented for the stock market trend prediction. In this Chapter, the trend analyses of the stock market prediction are presented by using Hidden Markov Model with the one day difference in close value for a particular period. The probability values π gives the trend percentage of the stock prices which is calculated for all the observe sequence and hidden sequences. This chapter helps for decision makers to make decisions in case of uncertainty on the basis of the percentage of probability values obtained from the steady state probability distribution.

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