Effect Of Investor Sentiment Research Articles

Investor sentiment, style investing, and momentum

Investor sentiment is an important condition for style investing in affecting asset price predictability. We find that style returns have predictive power for future stock returns in high sentiment periods, but not low sentiment periods. The correlation between style returns and stock returns explains the variation in momentum profits in high sentiment periods, but not low sentiment periods. Sentiment has an interaction effect with style returns, but not market returns. While positive style returns predict future stock returns under high sentiment, negative style returns do not. The effect of investor sentiment on style investing is independent of prior market returns.

Time-frequency effect of investor sentiment, economic policy uncertainty, and crude oil on international stock markets: evidence from wavelet quantile analysis

ABSTRACT This paper employs the wavelet-based quantile method to examine the time and frequency effect of investor sentiment, economic policy uncertainty, and crude oil on emerging and developed stock markets over the monthly sample range from September 2005 to December 2020. We first explore the relationship between various markets, and our empirical results reveal a strong long-term spillover effect of sentiment on returns from developed to emerging markets. Meanwhile, we find the negative influence of EPU gradually strengthens from the short-term towards the long-term. Further, oil shocks to stock returns are highly related to investment horizons and market circumstances. Specifically, the correlation is negative in the medium-term while positive in the short- and long-term, and more evident when stock markets are in a bearish or bullish state. In short, investors should take the structure dependence in terms of time and frequency when participating in financial markets.

Effects of investor sentiment and country governance on unexpected conditional volatility during the COVID-19 pandemic: Evidence from global stock markets

This paper first investigates the relationship between investor sentiment, captured by internet search behaviour, and the unexpected component of stock market volatility during the COVID-19 pandemic. According to data on 12 major stock markets, our research indicates a positive correlation between the Google search volume index on COVID-19 and the unexpected volatility of stock markets. The result suggests that greater COVID-19-related investor sentiment during this pandemic is associated with higher stock market uncertainty.Our study further examines whether country-level governance plays a role in protecting stock markets during this pandemic and reveals that the unexpected conditional volatility is lower when a country's governance is more effective. The impact of investor sentiment and country governance on unexpected volatility after the initial shock of COVID-19 is also investigated. The findings demonstrate the importance of establishing good country-level governance that can effectively reduce stock market uncertainty in the context of this pandemic, and support continual policy development related to investor protection.

Hubungan Kausalitas antara Sentimen Investor dan Pasar Keuangan Indonesia

This study aims to see the causal relationship that occurs between investor sentiment-based google search and the Indonesian stock-bond market in 2020. The test used in this study is a combined test between the quantile test to see the effect of investor sentiment on optimistic (pessimistic) conditions and the Granger causality test to detect the causal relationship that occurs between variables. The results of the study found that there was a one-way relationship between investor sentiment and the Indonesian stock market where investor sentiment affects the stock market. Interestingly, in the bond market, causal relationship between variables can’t be found. This study also found that the causal relationship between investor sentiment variables and financial markets only occurs when the optimistic and pessimistic conditions of investors aren't in extreme conditions.

MACROECONOMIC VARIABLES, SECTORAL INDEX VOLATILITY, AND INVESTOR SENTIMENT AMONG LISTED FIRMS AT NAIROBI SECURITY EXCHANGE, KENYA

Purpose: From a broader perspective, it is generally accepted that every investor aims to maximize return on their investment. To achieve this, the security market has significantly attracted so much interest from numerous stakeholders around the globe. However, it is difficult to forecast the stock index volatility exhaustively since it is triggered by different factors, which erode the investors’ confidence. The impact of volatility in the stock market is not the same (Liu et al., 1998), and it is transmitted from one sector to the other. Therefore, this study seeks to establish the relationship between the selected macroeconomic variables sectoral index volatility after introducing the moderating effect of investor sentiment.
 Methodology: This review employed Systematic review research design to trace, gather and appraise relevant studies that address the relationship between the dependent and independent variables.
 Findings: The outcomes of the study review the existence of a conceptual framework gap as empirical literature does not offer conclusive results on the sectoral index volatility and how it is influence by macroeconomic variables and investor sentiment. Previous studies were majorly conducted at a different time period in other markets presenting a geographical gap, and without factoring sectoral perspective.
 Unique contribution to theory, practice and policy: The study will be beneficial to investors in portfolio formation for diversification purposes. The models developed from this study will aid the capital market authority and government to regulate listed firms in Kenya to develop policies that minimizes return volatility. The study will add new knowledge on sectoral index volatility to maximize market returns for listed firms in Kenya.

The effect of investor sentiment and the structure of shareholder ownership on corporate investment

Purpose This paper aims to investigate the relations amongst investor sentiment, the structure of shareholder ownership and corporate investment.Design/methodology/approach This paper develops a theoretical model, proposes hypotheses based on the predictions of the model and conducts empirical tests. The primary method is panel regression with fixed effects. The sample covers the US data for the period between 1980 and 2018.Findings This paper finds that firms with a higher proportion of retail investors invest more than otherwise similar firms. In the low-sentiment periods, the financially constrained firms invest less than the non-financially constraint firms. The positive effect of residual retail ownership on the investment level is higher for firms with a higher idiosyncratic risk.Practical implications The results suggest that larger share ownership of the relatively informed institutional investors may serve as a mechanism that could reduce the degree of overinvestment caused by higher investor sentiment and the over-optimistic of the relatively uninformed investors.Originality/value This paper provides an incremental theoretical and empirical contribution to the relations amongst investor sentiment, corporate investment and the structure of shareholder ownership.

Effects of investor sentiment on stock volatility: new evidences from multi-source data in China’s green stock markets

The effect of investor sentiment on stock volatility is a highly attractive research question in both the academic field and the real financial industry. With the proposal of China's "dual carbon" target, green stocks have gradually become an essential branch of Chinese stock markets. Focusing on 106 stocks from the new energy, environmental protection, and carbon–neutral sectors, we construct two investor sentiment proxies using Internet text and stock trading data, respectively. The Internet sentiment is based on posts from Eastmoney Guba, and the trading sentiment comes from a variety of trading indicators. In addition, we divide the realized volatility into continuous and jump parts, and then investigate the effects of investor sentiment on different types of volatilities. Our empirical findings show that both sentiment indices impose significant positive impacts on realized, continuous, and jump volatilities, where trading sentiment is the main factor. We further explore the mediating effect of information asymmetry, measured by the volume-synchronized probability of informed trading (VPIN), on the path of investor sentiment affecting stock volatility. It is evidenced that investor sentiments are positively correlated with the VPIN, and they can affect volatilities through the VPIN. We then divide the total sample around the coronavirus disease 2019 (COVID-19) pandemic. The empirical results reveal that the market volatility after the COVID-19 pandemic is more susceptible to investor sentiments, especially to Internet sentiment. Our study is of great significance for maintaining the stability of green stock markets and reducing market volatility.

Investor Sentiment and Stock Market Reactions to COVID‐19: Evidence from China

This paper treats the outbreak of coronavirus disease 2019 (COVID‐19) as a natural experiment that can provide insights into the effects of investor sentiment on stock market reactions. Employing the event study methodology (ESM) and taking the date of the Wuhan lockdown as the event date, we find that average abnormal return (AAR) and cumulative abnormal return (CAR) are significantly negative, and average trading volume excesses far more than before within two days of the outbreak. Further, we establish a difference‐in‐differences (DID) model to investigate the differences between Hubei and non‐Hubei listed companies. The results show that for Hubei listed companies, the change of excessive trading volume (ETV) between pre‐event and post‐event period is significantly higher than that of non‐Hubei listed companies, while there exhibits no relationship between the change of AAR and registration place. Overall, our findings provide new evidence for the interaction of local bias and investor sentiment affecting stock market reactions.

Research on the Effect of Firm-Specific Investor Sentiment on Idiosyncratic Volatility Anomaly: Evidence from the Chinese Market

Research on the Effect of Firm-Specific Investor Sentiment on Idiosyncratic Volatility Anomaly: Evidence from the Chinese Market

Summary of the Researches on the Influence of Investor Sentiment on Stock Returns Under the Background of Big Data

In order to analyze the promoting effect of big data technology on research results, and to explore the impact of investor sentiment on stock returns, this paper combs and summarizes the research results of domestic and foreign scholars on the impact of investor sentiment on stock returns under the background of big data. This paper defines the concepts of big data, investor sentiment and stock returns, analyzes the measurement methods of investor sentiment, and deeply analyzes the overall effect and cross-sectional effect of investor sentiment on stock returns under the background of living alone. The results show that big data technology plays a strong role in promoting the research results, can comprehensively analyze various influencing factors, and investor sentiment has a great impact on stock returns.

Nonlinear ARDL approach for asymmetric effects of investor sentiment on asset pricing in an emerging Asian economy: the Malaysian experience

Nonlinear ARDL approach for asymmetric effects of investor sentiment on asset pricing in an emerging Asian economy: the Malaysian experience

Mean–variance relationship and uncertainty

This study investigates the impact of uncertainty on the mean-variance relationship. We find that the stock market's expected excess return is positively related to the market's conditional variances and implied variance during low uncertainty periods but unrelated or negatively related to conditional variances and implied variance during high uncertainty periods. Our empirical evidence is consistent with investors' attitudes toward uncertainty and risk, firms' fundamentals and leverage effects varying with uncertainty. Additionally, we discover that the negative relationship between returns and contemporaneous innovations of conditional variance and the positive relationship between returns and contemporaneous innovations of implied variance are significant during low uncertainty periods. Furthermore, our results are robust to changing the base assets to mimic the uncertainty factor and removing the effect of investor sentiment.

Investor Sentiment, Market Volatility, and IPO Initial Returns

This research seeks to determine the impact of investor sentiment and market volatility on IPO initial return when an issuer conducts an Initial Public Offering (IPO). This research lasted ten years, from 2009 to 2020, with a total issuer of 237 conducting Initial Public Offerings (IPOs) on the Indonesia Stock Exchange. The total number of issuers conducting an initial public offering (IPO) met the purposive sampling criteria is 285. Multiple linear regression analysis, with control variables such as Gap of Days, Firm Age, IPO Size, and Firm Size, is used to determine the effect of Investor Sentiment and Market Volatility on IPO Initial Returns. The findings showed that Investor Sentiment and Market Volatility positively affected the IPO Initial Return during the study period. This research aims to increase investor awareness of the importance of IPO stock price, increasing the initial return in the future. In Indonesia, Otoritas Jasa Keuangan (OJK) plays a critical role in policies that facilitate Indonesian investors' monitoring of the volatility of stock price changes in the market.JEL: M2, M21

The Effect of Investor Sentiment on the BIST Stock Index

Bu çalışmanın amacı, yatırımcı duyarlılığını temsilen kullanılan TCMB Tüketici Güven Endeksi, Bloomberg HT Tüketici Güven Endeksi ve VIX Endeksi ile BIST 100 Endeksi arasındaki ilişkinin araştırılmasıdır. Çalışma Ocak 2007-Ağustos 2020 dönemini kapsamaktadır ve aylık veriler kullanılmıştır. BIST 100 Endeksi ile Bloomberg HT Tüketici Güven Endeksi ve VIX Endeksi arasında eşbütünleşme ilişkisi tespit edilmiştir. BIST 100 Endeksi ile TCMB Tüketici Güven Endeksi arasında ise eşbütünleşme ilişkisi bulunamamıştır. Yapılan nedensellik analizlerine göre, BIST 100 Endeksinden her iki tüketici güven endeksine doğru tek yönlü nedensellik, VIX Endeksinden ise BIST 100 Endeksine doğru yine tek yönlü nedensellik ilişkisi bulunmaktadır.

Media-Coverage-Related Investor Sentiment during the COVID-19 Pandemic

This study tests the relationship between investor sentiment generated by COVID-19-related media coverage and BIST100 Index returns. In this context, the study is based on the ground laid by Tetlock’s (2007) study, which stated that media content and stock market activity are correlated. The effect of investor sentiment on the BIST 100 Index is examined in this study for the period 11 March 2020 – 19 August 2020 through the news on the COVID-19 pandemic. In this context, panic, fear, media coverage and vaccine indices are used in the study as investor sentiment proxies based on media coverage of COVID-19. The Dolado-Lütkepohl causality test and the ARDL method were used to investigate the effects of indices on the BIST100 Index return and subsequently, the cross-correlation relationship has been examined to check the robustness of the results. It is found that media-coverage-based investor sentiment indices are related to BIST100 Index returns. However, our evidence does not support investor sentiment theory.

Investor sentiment and market dynamics: Evidence from index futures markets

ABSTRACT This study presents an investor sentiment proxy with a greater explanatory power in determining price changes in the Korean futures market and examines the effect of investor sentiment on futures price changes. We also consider how investor-trading behaviour affects the association between investor sentiment and futures prices. Our empirical results demonstrate that the multiple-variable method is appropriate for examining the explanatory power of investor sentiment in the KOSPI200 index futures market. Investor sentiment significantly affects futures price changes, indicating its important role in futures price dynamics. Futures market sentiment has a greater effect on futures price changes during a positive net position state period. Furthermore, we find that foreign institutional investors’ trading behaviour is positively related to changes in futures prices.

A Study on the Effect of Real Estate Investor Sentiment on Housing Purchase Intention

A Study on the Effect of Real Estate Investor Sentiment on Housing Purchase Intention

Share pledge transactions as an investor sentiment indicator - Evidence from China

This paper examines how share pledge transactions signal changes in investor sentiment. Based on the investor sentiment model proposed by Gervais and Odean (2001), we develop an investor sentiment model by incorporating the impact of share pledge transactions on stock prices by linking the signal transmission of the share pledges with investor decision making. Using data from the China stock market from 2014 to 2018, we find that share pledge transactions can lead to emotional bias and consequently affect investment decisions. Specifically, stock market volatility increases with changes in both optimistic and pessimistic investor sentiment bias regarding share pledge transactions. From the perspective of behavioral finance, with the Vector Autoregression (VAR) model and the impulse response test, we study the relationship between investor sentiment indicators, including share pledge factors and stock market volatility. Among the investor sentiment indicator components, we find that the composition weights of the share pledge volume and the share pledge ratio are 22.54 %, and 21.49 % respectively. Share pledge transactions have an obvious impact on investor sentiment. The paper also shows that share pledge transactions have an accelerating effect on stock price cyclical fluctuations through the investor sentiment effect. During a stock index crash, such transactions may constitute a potential market factor of instability.

Investor sentiment and accounting conservatism: evidence from Iran

PurposeThe main purpose of this study is to investigate the effect of investor sentiment on accounting conservatism in listed companies in the Tehran Stock Exchange (TSE).Design/methodology/approachIn this paper, two models of Ball and Shivakumar (2006) and Basu (1997) have been used for measuring conditional conservatism in accounting. To measure investor sentiment, the author uses the Baker and Wurgler (2006, 2007) index. The research sample consists of 1,820 observations and 182 firms listed on TSE over a ten-year period between 2011 and 2020. This study uses panel data and multivariate regression analysis to test it hypotheses.FindingsConsistent with this hypothesis that accounting conservatism will increase with investor sentiment, the results showed that Iranian firms recognize economic losses and bad news in a more timely manner during high sentiment periods than during low sentiment periods. This implies that Iranian managers recognize economic losses and bad news in earnings in a more timely manner during periods of high investor sentiment.Practical implicationsThis finding provides significant evidence for investors and financial reporting standard-setters in Iran because by removing accounting conservatism from the conceptual framework, managers are not able to present conservative financial reports, and this can intensify the negative impact of investors sentiment in the Iranian capital market. Managers of Iranian companies can reduce information asymmetry and increase capital market efficiency by accelerating the disclosure of bad news. Thus, managers can strategically recognize losses and prevent investors from making emotional decisions that reduce their wealth.Originality/valueTo the best of the authors’ knowledge, this is the first study to empirically examine the impact of investor sentiment on accounting conservatism in a developing market called Iran. This study contributes to the corporate disclosure literature. Also, the result of this study contributes to standard-setters of accounting standards to improve the mandatory disclosure literature on more conservative accounting earnings.

Deep Neural Network and Time Series Approach for Finance Systems

The stock market is an aggregation of investor sentiment that affects daily changes in stock prices. Investor sentiment remained a mystery and challenge over time, inviting researchers to comprehend the market trends. The entry of behavioral scientists in and around the 1980s brought in the market trading's human dimensions. Shortly after that, due to the digitization of exchanges, the mix of traders changed as institutional traders started using algorithmic trading (AT) on computers. Nevertheless, the effects of investor sentiment did not disappear and continued to intrigue market researchers. Though market sentiment plays a significant role in timing investment decisions, classical finance models largely ignored the role of investor sentiment in asset pricing. For knowing if the market price is value-driven, the investor would isolate components of irrationality from the price, as reflected in the sentiment. Investor sentiment is an expression of irrational expectations of a stock's risk-return profile that is not justified by available information. In this context, the paper aims to predict the next-day trend in the index prices for the centralized Indian National Stock Exchange (NSE) deploying machine learning algorithms like support vector machine, random forest, gradient boosting, and deep neural networks. The training set is historical NSE closing price data from June 1st, 2013-June 30th, 2020. Additionally, the authors factor technical indicators like moving average (MA), moving average convergence-divergence (MACD), K (%) oscillator and corresponding three days moving average D (%), relative strength indicator (RSI) value, and the LW (R%) indicator for the same period. The predictive power of deep neural networks over other machine learning techniques is established in the paper, demonstrating the future scope of deep learning in multi-parameter time series prediction.