Investor sentiment in the tourism stock market

This study applies time-series analysis to observe investor sentiment in the tourism stock market. We infer that investor sentiment positively affects the capital flows to illustrate the behavioral finance in the tourism stock market. The vector autoregression and autoregressive-moving-average models of time-series analysis are adopted to analyze individual and overall capital flows of herding behavior. The empirical study collected quarterly data on 45 tourism-related stocks in China from 2018 to 2020. Results reaffirm that investor sentiment causes irrational investment and strong fluctuations of capital flows, including those during the Coronavirus 2019 pandemic. In practice, the overreaction of tourism-related stocks is discovered in the tourism market that requires long-term resilience. Theoretically, the rational capital asset pricing model needs adjustments with the sentiment factor based on behavioral finance theory. © 2022 Elsevier B.V.

Global environmental equities and investor sentiment: the role of social media and Covid-19 pandemic crisis

According to researchers, information generated from social media provides useful data for understanding the behaviour of various types of financial assets, using the sentiment expressed by these network users as an explanatory variable of asset prices. In a context in which investment based on sustainability and environmental preservation values is vital, there is no known scientific work that analyses the relationship between social networks and environmental investment, which is closely related to the 2030 Agenda for Sustainable Development. In this study, we aim to identify how investor sentiment, generated from social networks, influences environmental investment and whether this influence depends on the time variable, as well the role of the pandemic crisis and the Russia-Ukraine war. Our results show different forms of behaviour for the different periods considered, with the proximity between the two types of variables being time-varying. For shorter periods, proximity occurred mainly during the pandemic crisis, repeatedly revealing that sentiment is a risk factor in environmental investment and in particular how important the information generated from social networks can be in pricing environmental assets. For longer periods, no common stochastic trends were identified. The mechanisms generating the series are thus characterised by a certain autonomy. © 2023, The Author(s).

Investor sentiment based on scaled PCA method: A powerful predictor of realized volatility in the Chinese stock market

In this study, we construct an investor sentiment indicator (SsPCA) to predict stock volatility in the Chinese stock market by applying the scaled principal component analysis (sPCA). As a new dimension reduction technique for supervised learning, sPCA is employed to extract useful information from six individual sentiment proxies and obtain the common variations to characterize the investor sentiment (SsPCA). The empirical results indicate that SsPCA is a significant and powerful volatility predictor both in and out of sample. We also employ the partial least squares (PLS)-based investor sentiment index, three extra sentiment measures in past studies, and six individual sentiment proxies for comparison, and find SsPCA outperforms them on predicting stock volatility in the Chinese stock market. More importantly, the predictability of SsPCA remains significant before and after the famous financial crises (the sub-prime mortgage crisis and Chinese stock market turbulence) and the spread of the pandemic (COVID-19). Additionally, our findings imply that SsPCA still plays an essential role in predicting sock volatility after considering the leverage effect. The robustness of SsPCA in volatility forecasting is further verified in various industry indices of the Chinese stock market. Finally, we state that the strong predictability of SsPCA is highly related to its dimensionality reduction. Our results indicate that SsPCA is a robust volatility predictor from various aspects and performs better compared with existing sentiment indicators. © 2022 Elsevier Inc.

Investor sentiment and the Chinese new energy stock market: A risk–return perspective

The new energy industry is crucial for solving the problem of pollution, and its development requires support from the stock market. This paper proposes a Chinese investor sentiment index based on the Long Short-Term Memory (LSTM) deep learning method, and investigates the effect of investor sentiment on new energy stock returns as well as value at risks (VaR) behavior before and during COVID-19. It also compares these effects on traditional energy companies to identify differences between the new energy and traditional companies. The empirical results show that investor sentiment has significant effects on stock returns and VaR of both new and traditional energy companies but the effects are stronger in the new energy industry. The effects of investor sentiment have increased during COVID-19, and investors pay more attention on risks than returns during COVID-19. These results provide guidance for small and medium-sized investors in China to optimize their investment strategies and alleviate losses associated with extreme risks. © 2022 Elsevier Inc.

From sentiment to systemic risk: Information transmission in Asia-Pacific stock markets

This study provides a comprehensive sentiment connectedness analysis in Asia-Pacific. We implement a time-frequency framework and a quantile connectedness approach while analyzing the impact of three crises: the global financial crisis, the Chinese Stock market turbulence (2015-2016), and the COVID-19 pandemic. We find a significant sentiment spillover across markets, though the magnitude is more pronounced in the long run. Although sentiment connectedness is higher during extreme states of the sentiment than in the average state, the systemic risk intensifies further when the sentiment is exceptionally high. Notably, Japan appears to contribute moderately to the sentiment network, while China is the lowest contributor. The three crises strengthened the total sentiment connectedness, while the COVID-19 pandemic had the most substantial impact. Our sentiment network findings have insightful implications on cultural and behavioral factors that drive sentiment systemic risk in Asia-Pacific.

COVID-19 pandemic and capital markets: the role of government responses

This paper analyzes the moderation effect of government responses on the impact of the COVID-19 pandemic, proxied by the daily growth in COVID-19 cases and deaths, on the capital market, i.e., the S&P 500 firm's daily returns. Using the Oxford COVID-19 Government Response Tracker, we monitor 16 daily indicators for government actions across the fields of containment and closure, economic support, and health for 180 countries in the period from January 1, 2020 to March 15, 2021. We find that government responses mitigate the negative stock market impact and that investors' sentiment is sensitive to a firm's country-specific revenue exposure to COVID-19. Our findings indicate that the mitigation effect is stronger for firms that are highly exposed to COVID-19 on the sales side. In more detail, containment and closure policies and economic support mitigate negative stock market impacts, while health system policies support further declines. For firms with high revenue exposure to COVID-19, the mitigation effect is stronger for government economic support and health system initiatives. Containment and closure policies do not mitigate stock price declines due to growing COVID-19 case numbers. Our results hold even after estimating the spread of the pandemic with an epidemiological standard model, namely, the susceptible-infectious-recovered model.

Local Housing Market Sentiments and Returns: Evidence from China

This paper examines the impacts of local housing sentiments on the housing price dynamics of China. With a massive second-hand transaction dataset, we construct monthly local housing sentiment indices for 18 major cities in China from January 2016 to October 2020. We create three sentiment proxies representing the local housing market liquidity and speculative behaviors from the transaction dataset and then use partial least squares (PLS) to extract a recursive look-ahead-bias-free local housing sentiment index for each city considered. The local housing sentiments are shown to have robust predictive powers for future housing returns with a salient short-run underreaction and long-run overreaction pattern. Further analysis shows that local housing sentiment impacts are asymmetric, and housing returns in cities with relatively inelastic housing supply are more sensitive to local housing sentiments. We also document a significant feedback effect between housing returns and market sentiments, indicating the existence of a pricing-sentiment spiral which could potentially enhance the ongoing market fever of Chinese housing markets. The main estimation results are robust to alternative sentiment extraction methods and alternative sentiment proxies, and consistent for the sample period before COVID-19.

Forecasting global stock market volatilities in an uncertain world

We investigate the predictive relationship between uncertainty and global stock market volatilities from a highfrequency perspective. We show that uncertainty contains information beyond fundamentals (volatility) and strongly affects stock market volatility. Using several crucial uncertainty measures (i.e., uncertainty and implied volatility indices), we prove that the CBOE volatility index (VIX) performs best in point (density) forecasting;the financial stress index (FSI) in directional forecasting. Furthermore, VIX's predictive power improved dramatically after the COVID-19 outbreak, and the VIX-based portfolio strategy enables mean-variance investors to achieve higher returns. There are two empirical properties of VIX: (i) it helps reduce significantly forecast variance rather than bias;and (ii) its forecasts encompass other uncertainty forecasts well. Overall, we highlight the importance of considering uncertainty when exploring the expected stock market volatility.

Forecasting financial markets with semantic network analysis in the COVID-19 crisis

This paper uses a new textual data index for predicting stock market data. The index is applied to a large set of news to evaluate the importance of one or more general economic-related keywords appearing in the text. The index assesses the importance of the economic-related keywords, based on their frequency of use and semantic network position. We apply it to the Italian press and construct indices to predict Italian stock and bond market returns and volatilities in a recent sample period, including the COVID-19 crisis. The evidence shows that the index captures the different phases of financial time series well. Moreover, results indicate strong evidence of predictability for bond market data, both returns and volatilities, short and long maturities, and stock market volatility.

The Impact of Government Policy Responses to the COVID-19 Pandemic and Brexit on the UK Financial Market: A Behavioural Perspective

At the height of the COVID-19 pandemic in the United Kingdom, the Governor of the Bank of England, while granting an interview, described the pandemic as an unprecedented economic emergency and said that the Bank could go as far as radical money-printing operations. In reaction, the UK financial market, particularly the FTSE 100 and pound sterling, witnessed record-breaking losses. Considering this evidence, we hypothesized that the emotions and moods of investors towards the financial market might have been impacted by the information they obtained from frequent government policy announcements. Furthermore, we proposed that the United Kingdom's final exit from the European Union (Brexit), which coincided with the pandemic, could have worsened the outlook of the UK financial market, as investors began to diversify their portfolios. Consequently, we examined the impact of government's policy announcements on investors' reactions to the concurrence of the COVID-19 pandemic and Brexit. Our findings reveal that the psychology of investors during the pandemic was significantly shaped by frequent policy announcements, which in turn affected overall market behaviour.

Investor sentiment and the Chinese new energy stock market: A risk–return perspective

The new energy industry is crucial for solving the problem of pollution, and its development requires support from the stock market. This paper proposes a Chinese investor sentiment index based on the Long Short-Term Memory (LSTM) deep learning method and investigates the effect of investor sentiment on new energy stock returns as well as value at risks (VaR) behavior before and during COVID-19. It also compares these effects on traditional energy companies to identify differences between the new energy and traditional companies. The empirical results show that investor sentiment has significant effects on stock returns and VaR of both new and traditional energy companies but the effects are stronger in the new energy industry. The effects of investor sentiment have increased during COVID-19, and investors pay more attention on risks than returns during COVID-19. These results provide guidance for small and medium-sized investors in China to optimize their investment strategies and avoid heavy losses associated with extreme risks.

Brand equity and stock performance in time of crisis: evidence from the COVID-19 pandemic

Purpose This paper aims to investigate the impact of brand equity (BE) on stock performance (i.e. stock return, volatility and beta), and compare the performance of a high brand equity stocks (HBES) portfolio with that of the overall market during market downturn, market upturn and total disturbance periods of the COVID-19 pandemic in 2020. Design/methodology/approach Stock performance data and brand valuation estimates are obtained from various sources to assemble a portfolio of HBES and conduct the analyses. Econometric models are estimated to examine the impact of BE on stock performance and compare the HBES portfolio performance versus the overall market. Findings BE was positively associated with stock return and negatively associated with both types of risk (volatility and beta) during the COVID-19 pandemic. Specifically, during the market downturn period, BE was positively related to stock return and negatively related to stock volatility;during the market upturn period, BE was negatively associated with both types of risk;and during the total disturbance period, BE was positively associated with stock return and negatively associated with both types of risk. Finally, the HBES portfolio outperformed the market (S&P 500 index). Research limitations/implications The findings advance the extant research by providing evidence pertaining to brands' role in mitigating the impact of unpredictable market shocks and crises, such as the COVID-19 pandemic, on stock performance. While brands are mostly viewed as drivers of sustained competitive advantage and profitability, their protective role in crisis times is noteworthy. Practical implications The research findings potentially help marketing and brand managers to justify marketing spending and craft their strategies to enhance firm performance during crises similar to COVID-19. Originality/value The marketing-finance interface can benefit from insights offered by the COVID-19 pandemic, as such crises are becoming prevalent and are capable of damaging various stakeholders' outcomes (firms, investors and customers). The empirical examination is separately conducted on the market downturn, market upturn and total disturbance period attributable to the COVID-19 pandemic.

Market sentiment to COVID ‐19 and the Chinese stock market

We classify the market sentiment to COVID-19 into expected and unexpected components and then examine their particular impacts on the stock market. We find that unexpected sentiment causes fluctuations in the stock market more than expected sentiment does. However, unexpected sentiment cannot affect stock market informativeness despite the remarkable informational effect of expected sentiment. Moreover, the relation between expected sentiment and stock market fluctuation or informativeness is one-way, whereas there exists a two-way interaction between unexpected sentiment and stock market fluctuation. This further confirms that expected sentiment is informational, whereas unexpected sentiment is quite noisy and informationally harmful.

Investor sentiment based on scaled PCA method: A powerful predictor of realized volatility in the Chinese stock market

In this study, we construct an investor sentiment indicator (SsPCA) to predict stock volatility in the Chinese stock market by applying the scaled principal component analysis (sPCA). As a new dimension reduction technique for supervised learning, sPCA is employed to extract useful information from six individual sentiment proxies and obtain the common variations to characterize the investor sentiment (SsPCA). The empirical results indicate that SsPCA is a significant and powerful volatility predictor both in and out of sample. We also employ the partial least squares (PLS)-based investor sentiment index, three extra sentiment measures in past studies, and six individual sentiment proxies for comparison, and find SsPCA outperforms them on predicting stock volatility in the Chinese stock market. More importantly, the predictability of SsPCA remains significant before and after the famous financial crises (the sub-prime mortgage crisis and Chinese stock market turbulence) and the spread of the pandemic (COVID-19). Additionally, our findings imply that SsPCA still plays an essential role in predicting sock volatility after considering the leverage effect. The robustness of SsPCA in volatility forecasting is further verified in various industry indices of the Chinese stock market. Finally, we state that the strong predictability of SsPCA is highly related to its dimensionality reduction. Our results indicate that SsPCA is a robust volatility predictor from various aspects and performs better compared with existing sentiment indicators.

VIX and major agricultural future markets: dynamic linkage and time-frequency relations around the COVID-19 outbreak

Purpose The purpose of this paper is to examine the volatility spillover and lead-lag relationship between the Chicago Board Options Exchange volatility index (VIX) and the major agricultural future markets before and during the Coronavirus disease 2019 (COVID-19) outbreak. Design/methodology/approach The methods used were the vector autoregression-Baba, Engle, Kraft and Kroner-generalized autoregressive conditional heteroskedasticity method, the Wald test and wavelet transform method. Findings The findings indicate that prior to the COVID-19 outbreak, there was a two-way volatility spillover impact between the majority of the sample markets. In comparison, volatility transmission between the VIX index and the agricultural future market was significantly lower following the COVID-19 outbreak, the authors observed greater coherence at higher frequencies than at lower frequencies, implying that the interdependence between the two VIX indices and the agricultural future market was stronger over a longer time-frequency domain and the VIX's signalling effect on various agricultural future prices after the COVID-19 outbreak was significantly lower. Originality/value The authors conducted the first comprehensive investigation of the VIX's correlation with major agricultural futures, especially during COVID-19. The findings contribute to a better understanding of the risk transmission mechanism between the VIX and major agricultural commodities futures contracts. And our findings have significant implications for investors and portfolio managers, as well as for policymakers who are concerned about the price of agricultural futures.

Corporate governance and stock performance: The case of COVID-19 crisis

This paper analyzes the impact of COVID-19 on firm-level stock behaviors (including stock price volatility, trading volume and stock returns). Using US data, this paper examines whether confirmed cases (and deaths) of COVID-19 or COVID-19-associated online searches affect stock behaviors. The results show that our five COVID-19 proxies are all positively associated with stock price volatility and trading volume and negatively associ-ated with stock returns. This paper further investigates the mitigating effect of corporate governance (viz., board and ownership structures) in this COVID-19 crisis. Overall, the results suggest that good corporate governance can mitigate the impact of COVID-19 on stock price volatility and trading volume but may not help to enhance stock returns. This paper also considers key policies used to tackle the COVID-19 pandemic and finds that government intervention plays an important role in stabilizing stock markets in this COVID-19 crisis. (c) 2021 Elsevier Inc. All rights reserved.

Synergy between Stock Prices and Investor Sentiment in Social Media

The underlying assumption of using investor sentiment to predict stock prices, stock market returns, and liquidity is that of synergy between stock prices and investor sentiment. However, this synergistic relationship has received little attention in the literature. This paper investigates the synergistic pattern between stock prices and investor sentiment using social media messages from stock market investors and natural language processing techniques. At the macro level, we reveal extremely significant positive synergy between investor sentiment and stock prices. That is, when a stock price rises, investor sentiment rises, and when a stock price falls, investor sentiment falls. However, this synergy may be reversed or even disappear over a specific time period. Through a segmented measurement of the synergy between stock prices and investor sentiment over the course of a day, we also find that investor sentiment on social media is forward looking. This provides theoretical support for using investor sentiment in stock price prediction. We also examine the effect of lockdowns, the most draconian response to COVID-19, on synergy between stock prices and investor sentiment through causal inference machine learning. Our analysis shows that external anxiety can significantly affect synergy between stock prices and investor sentiment, but this effect can promote either positive or negative synergy. This paper offers a new perspective on stock price forecasting, investor sentiment, behavioral finance, and the impact of COVID-19 on the stock markets.

The Relationship between Corporate Sustainable Development Performance, Investor Sentiment, and Managerial Overconfidence

In the post-pandemic era, companies are facing challenges in their business development and may pay fewer attention to their sustainable development performance, whereas the investors are looking for better corporate sustainable development. Using a sample of Chinese listed companies during 2010–2018, this paper empirically examines the relation between corporate sustainable development performance, investor sentiment, and managerial overconfidence with econometric tools such as panel data regression and S-GMM estimation. Three kinds of corporate sustainable development activities as measured by Corporate Social Responsibility (CSR) indexes, including consumer rights, employee benefits, and environmental protection, are proved to have a positive impact on investor sentiment. Compared to the SME and GEM Board, investor sentiment in the Main Board is less affected by corporate sustainable development. Furthermore, investor’s high sentiment leads to high managerial confidence in the SME and GEM Board, and managerial overconfidence is self-correcting over time. This paper illustrates why maintaining good corporate sustainable development performance is beneficial for listed companies from a new perspective.

Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic

In this paper, we examine the impact of investor sentiment on Bitcoin returns. Using a large dataset of messages discussed on social media and several financial indicators, we create a sentiment indicator based on computational text analysis and driven by the principal component analysis (PCA) method. We utilize a vector autoregressive analysis and other analytical methods to examine the sentiment index–bitcoin return nexus. Our findings reveal that the sentiment index is a strong predictor of cryptocurrency market returns in the short term. Furthermore, we confirm that during the COVID-19 pandemic, investors' sentiments significantly impacted Bitcoin returns. Our results show that the proposed sentiment index can generate excess returns for investors who utilize it as a return predictor. Our empirical findings suggest important policy implications.

Investor sentiment in the tourism stock market

This study applies time-series analysis to observe investor sentiment in the tourism stock market. We infer that investor sentiment positively affects the capital flows to illustrate the behavioral finance in the tourism stock market. The vector autoregression and autoregressive-moving-average models of time-series analysis are adopted to analyze individual and overall capital flows of herding behavior. The empirical study collected quarterly data on 45 tourism-related stocks in China from 2018 to 2020. Results reaffirm that investor sentiment causes irrational investment and strong fluctuations of capital flows, including those during the Coronavirus 2019 pandemic. In practice, the overreaction of tourism-related stocks is discovered in the tourism market that requires long-term resilience. Theoretically, the rational capital asset pricing model needs adjustments with the sentiment factor based on behavioral finance theory.