An empirical examination of investor sentiment and stock market volatility: evidence from India

Understanding the irrational sentiments of the market participants is necessary for making good investment decisions.Despite the recent academic effort to examine the role of investors’sentiments in market dynamics,there is a lack of consensus in delineating the structural aspect of market sentiments.This research is an attempt to address this gap.The study explores the role of irrational investors’sentiments in determining stock market volatility.By employing monthly data on market-related implicit indices,we constructed an irrational sentiment index using principal component analysis.This sentiment index was modelled in the GARCH and Granger causality framework to analyse its contribution to volatility.The results showed that irrational sentiment significantly causes excess market volatility.Moreover,the study indicates that the asymmetrical aspects of an inefficient market contribute to excess volatility and returns.The findings are crucial for retail investors as well as portfolio managers seeking to make an optimum portfolio to maximise profits.

A CAUSALITY ANALYSIS OF SOCIETAL RISK PERCEPTION AND STOCK MARKET VOLATILITY IN CHINA

Modem China is undergoing a variety of social conflicts as the arrival of new era with thetransformation of the principal contradiction. Then monitoring the society stable is a huge workload.Online societal risk perception is acquired by mapping on-line public concerns respectively intosocietal risk events including national security, economy ＆ finance, public morals, daily life, socialstability, government management, and resources ＆ environment, and then provides one kind ofmeasurement toward the society state. Obviously, stable and harmonious social situations are the basicguarantee for the healthy development of the stock market. Thus we concern whether the variations ofthe societal risk are related to stock market volatility. We study their relationships by two steps, firstthe relationships between search trends and societal risk perception; next the relationships betweensocietal risk perception and stock volatility. The weekend and holiday effects in China stock market aretaken into consideration. Three different econometric methods are explored to observe the impacts ofvariations of societal risk on Shanghai Composite Index and Shenzhen Composite Index. 3 majorfindings are addressed. Firstly, there exist causal relations between Baidu Index and societal riskperception. Secondly, the perception of finance ＆ economy, social stability, and governmentmanagement has distinguishing effects on the volatility of both Shanghai Composite Index and Shenzhen Composite Index. Thirdly, the weekend and holiday effects of societal risk perception on the stock market are verified. The research demonstrates that capturing societal risk based on on-line public concerns is feasible and meaningful.

BM(book-to-market ratio) factor: mediumterm momentum and long-term reversal

To explain medium-term momentum and long-term reversal,we use the difference between the optional model and the CAPM model to construct a winner-loser portfolio.According to the CAPM model’s zero explanatory ability with respect to stock market anomalies,we obtain an anomaly interpretative model.This study shows that this anomaly interpretative model can explain stock market perceptions and medium-term momentum.Most importantly,BM is a critical factor in the model’s explanatory ability.We present a robustness test,which includes selecting new sample data,adding new auxiliary variables,changing sample years,and adding industry fixed effects.In general,the BM effect does have considerable explanatory power in medium-term momentum and long-term reversal.

Volatility Prediction via Hybrid LSTM Models with GARCH Type Parameters

Since the establishment of financial models for risk prediction,the measurement of volatility at risky market has improved,and its significance has also grown.For high-frequency financial data,the degree of investment risk,which has always been the focus of attention,is measured by the variance of residual sequence obtained following model regression.By integrating the long short-term memory(LSTM)model with multiple generalized autoregressive conditional heteroscedasticity(GARCH)models,a new hybrid LSTM model is used to predict stock price volatility.In this paper,three GARCH models are used,and the model that can best fit the data is determined.