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This study investigates the role of free cash flows and (cross-sectional and time-series) price momentum in predicting future stock returns. Past returns and free cash flows each positively predict future stock returns after controlling for the other, suggesting that cash flows and momentum both contain valuable and distinctive information about future stock returns. A strategy of buying past winners with high free cash flows and shorting past losers with low free cash flows significantly outperforms the traditional momentum trading strategy. The enhanced performance is not sensitive to investor sentiment, time variations, or transaction costs. Further analysis shows that the incremental cash flow effects are largely attributable to net distributions to equity/debt holders. Overall, our findings shed light on the role of corporate fundamentals in technical trading strategies. [ABSTRACT FROM AUTHOR]

Textual data require an analytical trade-off between breadth and depth. Automated approaches locate patterns across large swaths of data points but sacrifice qualitative insight because they are not well equipped to deal with context-determined ways to express meaning, like figurative language. To strengthen the power of automated text analysis, researchers seek hybrid methodologies that combine computer-augmented analysis with sociocultural researcher insights based on qualitative textual interpretation. This article demonstrates a new method, which the authors term metaphor-enabled marketplace sentiment analysis (MEMSA). Building on existing automated text analysis methodologies linking word lists to sentiments, MEMSA adds metaphors that associate topics with sentiments across domains. Using MEMSA, researchers can leverage the sentiment potential of these located metaphors and scale insights to the level of big textual data by employing a dictionary approach enhanced by a specific and useful linguistic property of metaphors: their predictable structure in text (something is something else). This article shows that metaphors add associative detail to sentiments, revealing the targets and sources of sentiments that underlie the associations. Understanding nuanced market sentiments enables marketers to identify sentiment-based trends embedded in market discourse, so they can better formulate, target, position, and communicate value propositions for products and services. [ABSTRACT FROM AUTHOR]

We examine the evolutionary effects of sentiment words in financial text and their implications for various business outcomes. We propose an algorithm called Word List Vector for Sentiment (WOLVES) that leverages both a human-defined sentiment word list and the word embedding approach to quantify text sentiment over time. We then apply WOLVES to investigate the evolutionary effects of the most popular financial word list, Loughran and McDonald (LM) dictionary, in annual reports, conference calls, and financial news. We find that LM negative words become less negative over time in annual reports compared to conference calls and financial news, while LM positive words remain qualitatively unchanged. This finding reconciles with existing evidence that negative words are more subject to managers' strategic communication. We also provide practical implications of WOLVES by correlating the sentiment evolution of LM negative words in annual reports with market reaction, earnings performance, and accounting fraud. [ABSTRACT FROM AUTHOR]

We test the role of dividends and investor sentiment in the relation between idiosyncratic risk and expected returns because Pastor and Veronesi (J Financ 58:1749–1789, 2003) find evidence that dividends reduce firm-specific uncertainty by sending information to the market participants through dividends. Also, Baker and Wurgler (J Financ 61:1645–1680, 2006) document that the negative relation between idiosyncratic risk and expected return only exists under the optimistic sentiment. We first document that the negative relation between idiosyncratic risk and expected return is more concentrated for stocks without dividends than stocks with dividends. We further find that the role of dividends in the relation between idiosyncratic risk and expected return is not affected by investor sentiment. These findings are robust to weighing schemes of returns and firm characteristics such as beta, size, book-to-market ratio, momentum, and liquidity. [ABSTRACT FROM AUTHOR]

This study conducts a detailed investigation into the interplay between major sporting events, specifically the ICC Cricket World Cups and FIFA Football World Cups, and their potential impact on the relationship between dividend announcements and stock market returns. Beyond the customary exploration of investor sentiment and its connection to stock market returns, our research thoroughly examines the effects of these significant sports events on the stock market's reaction to dividend announcements. Drawing on extensive FTSE 350 index data spanning January 1990 to December 2021, we employ event study methodology as the primary analytical framework. To bolster the reliability of our findings, we apply the Generalized Method of Moments (GMM) estimation method, addressing potential endogeneity concerns. Our results uncover a distinct pattern—the stock market exhibits a less favourable response to dividend increases announced following England's victories in major sporting events, such as the FIFA Football World Cup and ICC Cricket World Cup, compared to instances where they faced defeat. Additionally, we observe a more negative market response to dividend decreases announced following England's losses in these pivotal sporting events, as opposed to England emerging victorious in these key contests. This research contributes valuable insights into the intricate relationship between sports passion and market dynamics, offering implications for both scholarly discourse and investment strategy formulation. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to identify and rank the financial, nonfinancial, and behavioral components affecting companies' ERC. Methodology: Using data from 153 companies listed on the Tehran Stock Exchange over the period 2013 to 2022, and employing data mining techniques and two methods: stepwise forward regression and regression decision tree, the influential components on ERC were identified and ranked. Findings: The results of the stepwise forward regression method indicated that the components of auditor's opinion type, earnings per share (EPS), stock liquidity, growth opportunities, earnings stability, inflation rate, sales growth, operating profit ratio, relative strength index per share, and market index return respectively influence ERC. The regression decision tree method results also showed that the components of earnings stability, stock liquidity, EPS, stock price synchronicity, stock trading imbalance, adjusted trading volume, psychological line index, relative strength index per share, earnings conservatism, and financial statement restatements respectively influence ERC. The Wilcoxon test results also showed that the ranking of components influencing ERC is not the same in the two methods. Additionally, comparing the mean absolute error of the two methods indicated that the regression decision tree method identifies and ranks the influential components on ERC more accurately. Conclusion: The results of both methods confirm the high impact of EPS, stock liquidity, earnings stability, and relative strength index per share on the ERC of companies. [ABSTRACT FROM AUTHOR]

Non-fungible tokens (NFTs) have experienced wild market fluctuation during the past years, which leads to the high volatility of NFT's daily price. This paper examines two potential volatility drivers of NFTs: macroeconomic fundamentals and investor attention. We employ the global and local economic policy uncertainty (EPU) indices as the economic fundamentals' proxies. The investor attention is represented by the Google search volumes (GSV) or NFTs attention index. Based on the empirical results of a modified generalized autoregressive conditional heteroskedasticity –mixed-data sampling (G-M) model, we find that either economic fundamentals or investor attention can increase the volatility of NFTs significantly. The monthly global EPU index adjusted by the current GDP and weekly GSV contain complementary information. Macroeconomic fundamentals and investor attention can jointly model the volatility of NFTs better than considering only one explanatory variable, as suggested by the G-M model with two explanatory variables. The results remain robust to alternative Twitter-based EPU indices and the ongoing COVID-19 pandemic period. [ABSTRACT FROM AUTHOR]

This study uses a quantile-on-quantile regression framework to establish a link between investor sentiment proxied by market turnover and stock returns in G7 countries. The QQ analysis results reveal that the impact of investor sentiment on future stock returns is asymmetric and varies across quantiles of stock returns and sentiment. Overall, a significantly negative relationship is observed for all G7 countries. Interestingly, when the market is very bullish, and investors are pessimistic, investor sentiment positively influences future returns, except for Japan. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of financial availability on illegal fundraising activities in China. By collecting 14,481 judgement documents from 2013 to 2019 in China Judgements Online and extracting data, we find that financial availability effectively reduces the amount of money invested, the actual loss and the number of related court cases. This effect remains robust under various robustness specifications. In addition, we find that the attention of investors can increase the negative impact of financial availability on illegal fundraising. [ABSTRACT FROM AUTHOR]

We construct an extreme climate concern indicator (ECC) on the basis of the coverage of the extreme climate news reports. First, ECC significantly negatively forecasts stock market returns in subsequent months. The predictability of ECC returns outperforms alternative confidence indicators and economic predictors over both in-sample and out-of-sample periods. Second, relative to before the Paris Agreement entered into force, extreme climate concerns prominently enhanced the forecasting capabilities after the signing of the Paris Agreement. Third, the return prediction accuracy of ECC in periods of low climate concern is significantly greater than that in periods of high climate concern, which is also consistent with the limited attention of investors. Finally, ECC substantially brings appreciable economic gains to investors, and the relevant empirical results pass a series of robustness tests. [ABSTRACT FROM AUTHOR]

Today, with a growing emphasis on sustainable economic development, corporate environmental, social and governance (ESG) performance is attracting increasing attention and favor from investors. This triggers a question: can good ESG performance of listed companies mitigate the "up and down" of the stock market by drawing investor attention? This paper utilizes the data from China's A-share listed companies from 2011 to 2020, with investor attention as a mediating variable, to explore how the ESG performance of listed companies influences abnormal stock price volatility. The findings suggest that stronger ESG performance of listed companies significantly reduces abnormal stock price volatility, in which investor attention plays a partial mediating role. This paper confirms the robustness of the findings through multiple robustness and endogeneity tests. Heterogeneity analysis reveals that listed companies with good ESG performance during the growth period are more likely to significantly mitigate abnormal stock price volatility. Similarly, firms that maintain commendable ESG performance in bear markets significantly reduce abnormal stock price volatility. These findings enrich the theoretical research on the impact of ESG performance on abnormal stock price volatility, provide empirical evidence for listed companies to emphasize ESG investment and encourage investors to consider ESG ratings. Additionally, the study provides a new perspective for government agencies to utilize corporate ESG performance to maintain the sound development of the capital market. [ABSTRACT FROM AUTHOR]

This work explores if behaviour-based asymmetries are likely to impact deal valuation in the life sciences by examining positive public sentiment as a proxy for market behaviour when negotiating under asymmetric conditions to examine heterogeneity in research & development collaboration (RDC) deal data. We use public sentiment as a proxy for behaviour along with stage of development-based RDC deal data to search for latent classes in the deal data using finite mixture modelling. The analysis reveals a nuanced picture: public sentiment emerges as a significant predictor of deal value, but only for approximately 15% of the data set. This subset exclusively includes firms in the Preclinical stage, where projects have moved past discovery but are yet to commence human studies. Interestingly, the research finds that sentiment's impact on deal valuation is particularly pronounced in this stage, suggesting heightened market sensitivity. With recent research demonstrating that knowledge asymmetry and behaviour impact valuation volatility, we take this further by capturing latent classes within the data which demonstrates how behaviour is most influential in deal pricing considerations. We argue that our research demonstrates the impact of asymmetry and market behaviour on a subset of RDCs where products are known, but likelihood of success is difficult to determine. [ABSTRACT FROM AUTHOR]

Although the 2022 cryptocurrency market crash prompted despair among investors, the rallying cry, "wagmi" (We're all gonna make it.) emerged among cryptocurrency enthusiasts in the aftermath. Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors? Using natural language processing techniques applied to Twitter data, this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors. The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors. In particular, cryptocurrency enthusiasts' tweets became more neutral and, surprisingly, less negative. This result appears to be primarily driven by a deliberate, collectivist effort to promote positivity within the cryptocurrency community ("wagmi"). Considering the more nuanced emotional content of tweets, it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors. Moreover, cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash, with a relative increase in tweet frequency of approximately one tweet per day. An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts. [ABSTRACT FROM AUTHOR]

We investigate investor sentiment regarding mandatory corporate social responsibility (CSR) as a public policy. Using the event study methodology, we analysed the cumulative abnormal returns (AR) of companies impacted by Section 135 of the Indian Companies Act 2013. Our findings suggest that setting a CSR expenditure threshold may lead companies lagging in CSR to over‐invest, potentially hindering value maximisation. Specifically, we observed that the cumulative AR for companies lagging in CSR are lower than those leading in CSR. Therefore, mandating CSR practices may be counterproductive for value creation. This event study is one of the first to evaluate the impact of mandatory CSR as public policy on CSR‐leading and lagging firms. [ABSTRACT FROM AUTHOR]

Under the constraints of low-carbon transformation goals, energy enterprises have significantly increased their debt default risk levels due to carbon price fluctuations. This article first analyzes the contagion mechanism of debt default risk among energy enterprises, and based on this, constructs a debt default risk contagion model among energy enterprises considering carbon price fluctuations, and then simulates and analyzes the evolution characteristics of debt default risk contagion among energy enterprises. The research results indicate that: (1) As the proportion of carbon emission cost increment and investor sentiment index increase, the stability of the debt network of energy enterprises strengthens. As the ratio of commercial credit among energy enterprises and influence of energy enterprises increase, the impact of debt risk gradually intensifies. (2) The investor sentiment index has a strengthening effect on the influence of energy enterprises, the proportion of commercial credit among energy enterprises, and the proportion of carbon emission cost increment. The commercial credit ratio between energy enterprises and its influence has a mutually reinforcing effect. (3) The investor sentiment index has suppressed debt default risk for various energy enterprises. The joint risk suppression effect of the proportion of carbon emission cost increment and the influence of energy enterprises in petroleum and petrochemical enterprises is more prominent. The joint risk constraint ability between the proportion of carbon emission cost increment and investor sentiment index in coal enterprises is stronger. [ABSTRACT FROM AUTHOR]

This study uses unique South Korean data to demonstrate whether the public disclosure of audit hour (effort) information influences investor sentiment, proxied by stock price volatility. Over the 2005–2018 sample period, empirical results show that clients that secure increasing levels of audit hours enjoy lower stock price volatility. Furthermore, incrementally higher levels of audit hours reduce stock price volatility to a greater extent for Big4 clients, compared to Non-Big4 clients. Results are consistent after performing various additional tests including endogeneity, fixed/year effects, and after controlling for the audit fee premium effect. The aforementioned findings are interpreted from an audit demand theory perspective. More specifically, following South Korea's unique audit hour disclosure policy, market participants can make audit quality assertions using audit hour information, which influences investment/disinvestment speculation. Given that audit hour information reporting is rare internationally, the results have important audit policy and business planning implications. [ABSTRACT FROM AUTHOR]

Purpose: The primary objective of this research is to explore the potential of utilizing Global Consciousness Project (GCP) data as a tool for understanding and predicting market sentiment. Specifically, the study aims to assess whether incorporating GCP data into econometric models can enhance the comprehension of daily market movements, providing valuable insights for traders. Design/methodology/approach: This study employs econometric models to investigate the correlation between the Standard & Poor's 500 Volatility Index (VIX), a common measure of market sentiment and data from the GCP. The focus is particularly on the largest daily composite GCP data value (Max[Z]) and its significant covariation with changes in VIX. The research employs interaction terms with VIX and daily returns from global markets, including Europe and Asia, to explore the relationship further. Findings: The results reveal a significant relationship with the GCP data, particularly Max[Z] and VIX. Interaction terms with both VIX and daily returns from global markets are highly significant, explaining about one percent of the variance in the econometric model. This finding suggests that variations in GCP data can contribute to a better understanding of market dynamics and improve forecasting accuracy. Research limitations/implications: One limitation of this study is the potential for overfitting and P-hacking. To address this concern, the models undergo rigorous testing in an out-of-sample simulation study lasting for a predefined one-year period. This limitation underscores the need for cautious interpretation and application of the findings, recognizing the complexities and uncertainties inherent in market dynamics. Practical implications: The study explores the practical implications of incorporating GCP data into trading strategies. Econometric models, both with and without GCP data, are subjected to an out-of-sample simulation where an artificial trader employs S&P 500 tracking instruments based on the model's one-day-ahead forecasts. The results suggest that GCP data can enhance daily forecasts, offering practical value for traders seeking improved decision-making tools. Originality/value: Utilizing data from the GCP is found to be advantageous for traders as noteworthy correlations with market sentiment are found. This unanticipated finding challenges established paradigms in both economics and consciousness research, seamlessly integrating these domains of research. Traders can leverage this innovative tool, as it can be used to refine forecasting precision. [ABSTRACT FROM AUTHOR]

The registration-based initial public offering (IPO) system is a pivotal initiative in China's comprehensive capital market reform, but little is known about its effectiveness. Our study aims to explore the impact of the registration-based IPO reform on enterprises' environmental, social, and governance (ESG) greenwashing behaviors. Drawing on data from 2,490 enterprises listed on the Chinese growth enterprise market and the main board of the Shenzhen Stock Exchange during 2012-2022, we find that the registration-based IPO reform significantly aggravates ESG greenwashing with an effect of 0.1696. Building upon this evidence, we explore the underlying mechanism. Peer competition and retail investor sentiment play a partial mediating role and become two channels through which the registration-based IPO reform may aggravate enterprises' ESG greenwashing. Analysts' attention can alleviate the impact of registration-based IPO reform on enterprises' ESG greenwashing while management myopia intensifies it, and they both play a moderating role. Moreover, the aggravating effect of the registration-based IPO reform on ESG greenwashing is more pronounced in enterprises belonging to heavily polluting industries, in the Western region, and with low information transparency. Thus, our findings offer new insights into enterprises' ESG greenwashing behaviors in the context of the registration-based IPO reform. [ABSTRACT FROM AUTHOR]

As a colossal developing economy, irrational, and inefficient trades broadly exist in China's stock market and are intensified by the once-in-a-century COVID-19 pandemic. This atypical but prominent event enhances systemic risk and requires a more effective analysis tool that adapts to the investors' sentiment and behavior. Based on the behavioral asset pricing model, this paper verifies the existence of noise traders in China's stock market, measures the intensity of the noise with the NTR indicator, and examines the market noise with IANM. Furthermore, the mechanism of how COVID-19 influences the market noise through investors' behaviors is analyzed with the event study method. The findings show that, based on 92 Chinese companies, the market noise significantly exists, and the noise is associated with psychological biases including over-confidence, herding effects and regret aversion. These biases are affected to varying degrees by COVID-19-related events, leading to notable implications for market stability and investor behavior during crises. Our study provides critical insights for policymakers and investors on managing market risks and understanding behavioral impacts during unprecedented events. [ABSTRACT FROM AUTHOR]

This research examines the impact of the COVID-19 pandemic on the volatility behavior of Amman Stock Exchange (ASE) returns using ARMA–GARCH-type models for three sub-periods: pre-COVID-19, during COVID-19, and post-COVID-19. The research finds that volatility persistence is significant across all periods, with the pandemic period showing the highest impact of shocks. Bad news has no statistically significant impact on volatility in the pre-COVID-19 period or during the pandemic, while in the post-pandemic period, good news significantly influences volatility. Additionally, there exist notable changes in the autocorrelation and the shock structure of the AR and MA components. Considering these alterations in the asymmetric effects, the AR and MA components suggest significant shifts in market dynamics, investor sentiments, and economic policies in response to pandemic experiences. [ABSTRACT FROM AUTHOR]

Based on catering theory, this study employs a difference‐in‐differences model to investigate the impact of capital market liberalization on environmental, social, and governance (ESG) reporting greenwashing using mainland Hong Kong Connection Programs as a quasinatural experiment. Our findings indicate that corporate management increasingly engages in ESG reporting greenwashing in capital market liberalization to cater to foreign investors. This conclusion remains valid after controlling for endogeneity. Mechanistic analysis indicates that investor sentiment increases with capital market liberalization. In response, corporate management intensifies ESG reporting greenwashing to cater to foreign investors, and analysts' focus does not serve as a supervisory mechanism for restraining ESG reports greenwashing. Heterogeneity tests demonstrate that ESG reports greenwashing within the capital market diminishes for mandatory disclosure and state‐owned firms. However, ESG reports greenwashing intensifies among firms facing high financing constraints amid capital market liberalization. Overall, we identify a significant catering effect on ESG reporting amid capital market liberalization, offering a novel theoretical framework on greenwashing in ESG disclosures. [ABSTRACT FROM AUTHOR]

In this paper, we investigate interdependencies between cryptocurrencies and investor sentiment by introducing the asymmetric TVP-VAR frequency connectedness approach. Our empirical results provide evidence of pronounced and time-varying interconnectedness between sentiment and cryptocurrency. In addition, we find that negative short-term interconnectedness dominates positive short-term interconnectedness until mid-2020 when this effect was reversed, persisting until the end of the sample period. While Ripple and Bitcoin are both found to be the main net transmitters of shocks, Stellar, Ethereum, and NEM are considered net receivers of shocks. Overall, our findings suggest that market sentiment is mainly driven by cryptocurrency volatility in both the short and the long run. [ABSTRACT FROM AUTHOR]

Some financial relations have been confirmed to be different overnight and intraday due to different clienteles. In this paper, we assess the impact of investor sentiment on stock market returns in 30 international stock markets overnight and intraday. At the global level, empirical evidence reveals a negative sentiment-return relation in both non-trading and trading hours, and the relation is stronger intraday than overnight, indicating that overnight traders are more rational than intraday traders. The separation between developed and emerging markets does not distort the negative relation or the stronger impact intraday. At the individual market level, results reveal a high degree of heterogeneity in the sentiment-return relation, in terms of both influence direction and magnitude. The heterogeneity can be explained by cross-market differences in cultural dimensions and market integrity, and notably, such influence varies across night and day, suggesting that the influence of the two aspects may be more complex than we used to theorize and therefore, future studies applying the cross-market analytical framework may take different clienteles into account. [ABSTRACT FROM AUTHOR]

The market value of a stock is a function of firm performance and market sentiment. Specific events can alter returns and present several challenges to market performances. Through an event study design, this study examines five real estate investment trusts (REITs) market indices' responses to market shock from the initial COVID-19 outbreak. The results revealed negative cumulative abnormal returns for lodging, health care, and retail REITs over the 20-day trading window. Considering their lower drawdown losses, the remaining REIT sectors showed greater resilience than their counterparts. Block bootstrapping confirmed the returns were significantly worse for all sectors, with retail and lodging sectors having the poorest returns. This study advances the literature on pandemic-related market crashes along with how COVID-19 affected cross-sector REIT equities compared to other market indices. REIT firms can use the findings to develop more effective policy measures as responses to pandemic market conditions. [ABSTRACT FROM AUTHOR]

Based on the emerging capital market of China, this paper uses the data of Chinese listed companies to test the market value management behavior of CEOs under the incentive of industry tournaments. The empirical results show that the CEOs industry tournament improves the value of listed companies by attracting investor attention. In the test of the incentive mechanism of the industry tournament, we find that in the samples with lower industry mobility and state ownership and where the CEO is new, retiring, or a founder, the industry pay gap has a weaker incentive effect on the CEO. In the test of the CEOs' efforts to manage investor attention, this paper finds that CEOs are incentivized to disclose more financial and nonfinancial information, increase advertising spending, and hold more shareholder meetings. Discussed with other incentives, compensation incentives and equity incentives enhance the motivational role of industry championships for CEOs. This study explores in depth the possible impact of industry tournaments on CEO behavioral decisions and also provides a new perspective for corporate to manage investor attention. This provides important reference value for corporate CEO compensation, information disclosure supervision, and investor decision‐making. [ABSTRACT FROM AUTHOR]

In this paper we provide new evidence of investor inattention by showing that personal occurrences such as birthdays are able to drive attention away from the stock market. We document that individual investors significantly reduce their trading activity in the 3 days around their birthday. The reduction in the propensity to trade is larger for more active traders, in the event of a decade birthday and when this celebrative event falls on a Friday. Results are robust to analyses focusing only on days when investor attention should be at its peak, as expressed by excess news coverage and trading volumes. [ABSTRACT FROM AUTHOR]

ESG performance has become vital for business in recent years. Consumers, stakeholders and investors alike seek companies that can show that they are environmentally and socially responsible, especially in periods of uncertainty. Using a new measure of uncertainty, the Climate Policy Uncertainty Index developed by Gavriilidis (Measuring climate policy uncertainty, 2021), this study investigates environmental, social and governance (ESG) performance, firm performance and carbon dioxide emission performance in the context of major developments related to climate policy and events. Further, the study provides insights into how ESG performance moderates the impact of climate policy uncertainty on firm performance and carbon dioxide emission performance. Finally, this paper examines how investor sentiment influences ESG performance during periods of high uncertainty. Based on a large sample of 2640 firms (6462 firm-year observations) from the US Fortune 1000 list covering the years 2008–2018, the empirical results demonstrate that climate policy uncertainty positively affects ESG performance and negatively affects firm performance and carbon dioxide emission performance. In addition, we find that firms enhance their ESG performance when investors are pessimistic, especially during periods of high uncertainty. Finally, we show that ESG performance does not moderate the effects of climate policy uncertainty on firm performance and carbon dioxide emission performance. These findings may help managers and policymakers to boost ESG performance to reap the benefits of ESG activities during periods of uncertainty related to changes in climate policy. Further, the results identify a promising channel via investor power that the government and ESG reporting advocates can utilise to encourage firms to act responsibly during periods of uncertainty. [ABSTRACT FROM AUTHOR]

The Federal Open Market Committee (FOMC) is a component of the Federal Reserve System responsible for overseeing open market operations. The FOMC meets roughly eight or more times per year to assess the economy of the United States. After each meeting, the FOMC releases a statement to the press outlining its assessment of the US economy and its monetary policy stance. The sentiment of these statements may have an influence on the US economy and financial markets. Using sentiment and correlational analyses, this research examines how the sentiment of these statements affects the US economy and financial markets by analyzing how FOMC statement sentiment is correlated with the Consumer Price Index (CPI), the National Financial Conditions Index (NFCI), and the Adjusted National Financial Conditions Index (ANFCI). We find evidence to suggest that there is a moderate negative correlation between an FOMC statement's sentiment and the US City Average CPI value associated with the month before and the month after the statement's release. We also find that there is no evidence to suggest there exists a correlation between an FOMC statement's sentiment and the NFCI value associated with the week before or the week after the statement's release. However, we do find evidence to suggest that there is a moderate negative correlation between an FOMC statement's sentiment and the ANFCI value associated with the week before and the week after the statement's release. We also found that out of the three models we tested (linear regression, vine copula regression, and Gaussian copula regression), the Gaussian copula regression model performs the best when forecasting the CPI and the ANFCI. Additionally, we find that when forecasting CPI values, the models that include FOMC statement sentiment are more accurate than the models that exclude FOMC statement sentiment. [ABSTRACT FROM AUTHOR]

We utilize a unique setting associated with the mandatory closure of the government‐to‐business revolving door to examine whether and how an exogenous rise in firm‐level political uncertainty affects the mispricing of earnings. The tension that underlies our study stems from two opposing effects. To the extent that such uncertainty can trigger opinion divergence (rational attention) among investors, it is expected to delay (accelerate) price discovery and increase (decrease) security mispricing. Our identification strategy draws on the difference‐in‐differences analysis associated with the Chinese regulation in 2013 that mandated the resignation of corporate independent directors with a government background. Consistent with the dominance of the opinion divergence effect, we observe that these involuntary resignations unintentionally increase delays in share price responses following earnings announcements. These findings are more evident among firms that enjoy more benefits from independent directors with a government background. Further analyses confirm that these involuntary resignations trigger more opinion divergence rather than rational attention among investors by showing significant increases in analyst forecast diversity but no changes in analyst coverage following such resignations. We provide novel evidence that market information efficiency could deteriorate as an unintended consequence of the escalation of firm‐level political uncertainty. [ABSTRACT FROM AUTHOR]

Corporations face multifaceted environmental uncertainties (EU) in today's dynamic global economic environment. Such uncertainties profoundly affect corporate operations and pose significant challenges to their environmental, social, and governance (ESG) performance. Therefore, using data from Chinese A-share listed corporations from 2009 to 2021, we empirically analyze the impact of the EU on ESG performance. The results demonstrate that the EU significantly negatively impacts ESG performance, indicating that corporations frequently find it difficult to implement and maintain high-standard ESG policies and practices effectively. Additionally, they reveal that the EU inhibits the improvement of ESG performance by increasing corporate financing constraints (KZ). Lastly, this study explores the role of government subsidies (GOV), investor attention (IA), and geopolitical risks (GPR) as moderating variables. We discover that GOV can mitigate the negative impact of the EU on KZ because they provide additional resources that help corporations maintain their ESG in uncertain environments. Furthermore, IA can reduce the adverse impact of KZ on ESG. Positive moderating effects result from ESG issues; the capital they provide effectively reduces corporate KZ, thus enabling corporations to maintain good ESG performance despite operating in highly uncertain environments. However, due to the increased asymmetry of information in economic markets caused by geopolitical tensions, GPR exacerbates the negative impact of the EU on ESG performance, thus leading to an increase in KZ. These findings offer new perspectives on understanding how these moderating effects affect corporate ESG strategies. [ABSTRACT FROM AUTHOR]

In the context of China's rapid economic development, corporate pollutant emissions are increasing, underscoring the imperative for further enhancement of corporate environmental performance. Given the significance of high-tech enterprises in the environmental domain, this study examines the impact of investor attention on the corporate environmental performance of Chinese high-tech enterprises. This study utilized data from 463 high-tech enterprises in mainland China from 2011 to 2022 and employed the dynamic panel generalized method of moments (GMM) technique. The findings indicate that investor attention has a negative impact on the corporate environmental performance of high-tech enterprises. This research also finds that both media attention and coverage sentiment mitigate the negative relationship between investor attention and environmental performance of high-tech firms. Heterogeneity analysis reveals that in the sample of IT service industry, heightened media attention exacerbates the negative impact of investor attention on corporate environmental performance, and media coverage sentiment exhibits no significant moderating effect. Within samples of non-polluting and state-owned enterprises, increased media coverage sentiment amplifies the negative impact of investor attention on corporate environmental performance. This paper suggests that policymakers should foster a media environment that supports accurate and thorough environmental reporting while businesses should proactively engage with media to align investor perceptions with sustainable environmental practices. [ABSTRACT FROM AUTHOR]

In decentralized finance (DeFi), stablecoins like DAI are designed to offer a stable value amidst the fluctuating nature of cryptocurrencies. We examine the class of crypto-backed stable derivatives, focusing on mechanisms for price stabilization and exemplified by the well-known stablecoin DAI from MakerDAO. For simplicity, we consider a single-collateral setting. We introduce a belief parameter to the simulation model of DAI in a previous work (DAISIM), reflecting market sentiments about the value and stability of DAI, and show that it better matches the expected behavior when this parameter is set within a particular range of values. Our methods include comparing simulated data with real-world data, focusing on monthly correlations between ETH and DAI prices and scatter plots illustrating the relationship of their price trends over time. We also propose a simple mathematical model of DAI price to explain its stability and dependency on ETH price. Finally, we analyze possible risk factors associated with these stable derivatives to provide valuable insights for stakeholders in the DeFi ecosystem. [ABSTRACT FROM AUTHOR]

The Athens Stock Exchange (ASE) is a dynamic financial market with complex interactions and inherent volatility. Traditional models often fall short in capturing the intricate dependencies and long memory effects observed in real-world financial data. In this study, we explore the application of fractional Brownian motion (fBm) to model stock price dynamics within the ASE, specifically utilizing the Athens General Composite (ATG) index. The ATG is considered a key barometer of the overall health of the Greek stock market. Investors and analysts monitor the index to gauge investor sentiment, economic trends, and potential investment opportunities in Greek companies. We find that the Hurst exponent falls outside the range typically associated with fractal Brownian motion. This, combined with the established non-normality of increments, disfavors both geometric Brownian motion and fractal Brownian motion models for the ATG index. [ABSTRACT FROM AUTHOR]

Recently, liquidity issues in financial markets and portfolio asset management have attracted much attention among investors and scholars, fuelling a stream of research devoted to exploring the role of liquidity in investment decisions. In this paper, we aim to investigate the effects of introducing liquidity in portfolio optimization problems. For this purpose, first we consider three volume-based liquidity measures proposed in the literature and we build a new one particularly suited to portfolio optimization. Secondly, we formulate an extended version of the Markowitz portfolio selection problem, named mean–variance–liquidity, wherein the goal is to minimize the portfolio variance subject to the usual constraint on the expected portfolio return and an additional constraint on the portfolio liquidity. Thirdly, we consider a sensitivity analysis, with the aim to assess the trade-offs between liquidity and return, on the one hand, and between liquidity and risk, on the other hand. In the second part of the paper, the portfolio optimization framework is applied to a dataset of US ETFs comprising both standard and alternative, often illiquid, investments. The analysis is carried out with all the liquidity measures considered, allowing us to shed light on the relationships among risk, return and liquidity. Finally, we study the effects of the introduction of a Bitcoin ETF, as an asset with an extremely high expected return and risk. [ABSTRACT FROM AUTHOR]

This study seeks to investigate the extreme risk spillovers from crude oil to four major Asian stock markets from a market sentiment perspective. A copula-CoVaR (Conditional Value-at-Risk) approach is adopted to construct a dynamic network to capture the contributions of different types of market sentiment to the evolution of oil-stock risk nexus. The empirical evidence shows the presence of significant risk spillover effects from crude oil to equity markets. The dynamics of the oil-to-stock risk spillovers is shown to be significantly driven by market sentiments, mainly market fears in both stock markets and crude oil market triggered by prominent systemic events. As an extreme event unfolds, the oil-to-stock risk spillover dynamics is increasing susceptible to escalating market fears. [ABSTRACT FROM AUTHOR]

Numerous variables are involved in determining the market capitalization and price of cryptocurrencies. So, It is impossible to ignore the feedback of each variable on the other ones. Also, Bitcoin management is challenging due to inter-dependencies and feedback between economic and social drivers. To better understand the dynamics that different variables have on Bitcoin, a system dynamics approach has been developed to investigate the complex interactions between factors related to Bitcoin and paper money and how these factors may affect the stability of Bitcoin. This paper aims to examine the impact of government support for Bitcoin in the future, news and sentiments effect on Bitcoin market price, and the future orientation of Bitcoin. For this purpose, a simulation period is considered from 2017 to 4500 days later. Moreover, In this study, policies have been designed to check the impact of policies on variables in the long term by keeping other variables constant. The results show that if governments support Bitcoin as an official trading currency in the future, the market price of Bitcoin and its trading volume will grow upward and fluctuate so that its market price will go up to about 90 thousand dollars. [ABSTRACT FROM AUTHOR]

This study examines the financial implications of religious restrictions by analyzing how changes in the Shariah compliance status of stocks affect their price and trading volume. Employing the market model in an event study approach and using data from Malaysian firms between 2000 and 2016, the study focuses on 30 announcements from the Security Commission Malaysia’s List of Shariah Compliant Securities (LSCS), resulting in 370 additions and 284 deletions from the compliance list. The findings indicate that the inclusion of stocks in the compliance list generates long-term demand driven by Islamic institutional and Muslim retail investors, leading to a permanent increase in abnormal returns and trading volume. Conversely, the removal of stocks from the compliance list induces short-term negative abnormal returns and increased trading volume, reflecting selling pressure. These results contribute to understanding the financial consequences of religious restrictions, highlighting the influence of Shariah compliance on stock market performance and investor behaviour. [ABSTRACT FROM AUTHOR]

The stock market operates on informed decisions based on information gathered from heterogeneous sources, encompassing diverse beliefs, strategies, and knowledge. This study examines the validity of rational bubbles in stock market prices, focusing on eight African stock markets: South Africa, Nigeria, Kenya, Egypt, Morocco, Mauritius, Ghana, and Botswana. Utilizing newly developed econophysics-based unit root tests and the Dynamic Conditional Correlation Multivariate Generalized Autoregressive Conditional Heteroskedasticity (DCC MGARCH) models, the authors analyzed daily data from 1996 to 2022. Our findings indicate that these markets experienced bubbles at various points, often followed by bursts. These bubbles coincided with significant economic changes, suggesting a strong link between stock market behavior and economic growth. For instance, financial crises, political instability, and global economic downturns significantly influenced bubble formation and bursts in these markets. The study reveals that market-specific events, such as regulatory changes and shifts in investor sentiment, also contributed to the occurrence of bubbles. Three key policy options are proposed to address bubbles in the studied markets including, enhancing regulatory frameworks to monitor and mitigate bubble formation, improving financial literacy among investors to promote informed decision-making, and strengthening economic policies to stabilize macroeconomic conditions and reduce vulnerability to external shocks. By implementing these measures, policymakers can enhance market stability and foster sustainable economic growth in African stock markets. [ABSTRACT FROM AUTHOR]

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We examine five summer Olympics and identify stocks that media outlets hype as benefiting from the Olympics (Olympic stocks). There is a seven-year period from the time that a country first learns it has won the Olympic bid to the start of the games (Olympic time period). We predict that the excitement of the Olympics along with the greater media attention impacts the valuation and risk of Olympic stocks. Consistent with this prediction, we show that Olympic stocks earn higher returns than their matched counterparts and comove more strongly with each other over the Olympic time period. Olympic stocks also exhibit increases in trading volume and stock volatility on days when media outlets have stories linking the firm to the Olympic Games. However, we find no evidence that the Olympic Games translate into stronger fundamentals for Olympic firms or stronger fundamental comovements. These findings suggest that investors are not purchasing the stocks based on an analysis of fundamentals, but are purchasing them based on their Olympic attribute. To confirm that event-based groupings occur in other settings, we show that comovement increases for stocks classified by the media as "stay-at-home" stocks at the start of the COVID-19 pandemic. This paper was accepted by Eric So, accounting. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2021.02218. [ABSTRACT FROM AUTHOR]

This article explores the intricate relationship between gambling preferences and stock market dynamics, with a focus on the repercussions of a more lenient price limit regulation. Specifically, we examine the effects of expanding the price limit range from 10% to 20% for stocks listed on the second board market of the Shenzhen Stock Exchange. Employing a difference-in-differences methodology for causal inference, our research reveals a notable surge in price volatility, particularly evident among lottery stocks. To validate these findings, we employ various statistical techniques, including propensity score matching tests, parallel trend hypothesis tests, placebo tests, and alternative volatility measurements. Our study unveils that looser price limits contribute to heightened stock price volatility through two primary channels: the inherent lottery characteristics of certain stocks and the increased attention from retail investors. Moreover, we identify that the amplified volatility is more pronounced within non-state-owned enterprises and small-cap entities. In summary, our results indicate that price limits play a crucial role in stabilizing emerging stock markets by mitigating the gambling tendencies of retail investors. This research sheds light on the positive aspects of price limits regulation, offering valuable insights for market policymakers and participants alike. [ABSTRACT FROM AUTHOR]

Predicting the directions of financial markets has been performed using a variety of approaches, and the large volume of unstructured data generated by traders and other stakeholders on social media microblog platforms provides unique opportunities for analyzing financial markets using additional perspectives. Pretrained large language models (LLMs) have demonstrated very good performance on a variety of sentiment analysis tasks in different domains. However, it is known that sentiment analysis is a very domain-dependent NLP task that requires knowledge of the domain ontology, and this is particularly the case with the financial domain, which uses its own unique vocabulary. Recent developments in NLP and deep learning including LLMs have made it possible to generate actionable financial sentiments using multiple sources including financial news, company fundamentals, technical indicators, as well social media microblogs posted on platforms such as StockTwits and X (formerly Twitter). We developed a financial social media sentiment analyzer (FinSoSent), which is a domain-specific large language model for the financial domain that was pretrained on financial news articles and fine-tuned and tested using several financial social media corpora. We conducted a large number of experiments using different learning rates, epochs, and batch sizes to yield the best performing model. Our model outperforms current state-of-the-art FSA models based on over 860 experiments, demonstrating the efficacy and effectiveness of FinSoSent. We also conducted experiments using ensemble models comprising FinSoSent and the other current state-of-the-art FSA models used in this research, and a slight performance improvement was obtained based on majority voting. Based on the results obtained across all models in these experiments, the significance of this study is that it highlights the fact that, despite the recent advances of LLMs, sentiment analysis even in domain-specific contexts remains a difficult research problem. [ABSTRACT FROM AUTHOR]

In this paper, the self‐monitoring learning model FinBERT is used to identify text emotions, and the sliding time window time‐lagged cross‐correlation (WTLCC) method is utilized to screen Baidu Index keywords for the Shanghai Stock Exchange Index and 18 A‐share listed companies. There are five different types of indicators constructed: news media sentiment, public attention, investor sentiment, investor sentiment disagreement, and media sentiment disagreement. To accurately describe the structure of sentimental contagion, this paper combines graph neural network to learn and output the sentimental contagion graph, and then constructs multivariable time series forecasting with graph neural networks (MTGNN) volatility forecasting model, which can extract the spatial–temporal dependence of variables in pairs. The results show that MTGNN model possesses the highest forecasting accuracy, which performs 30.30% lower on average across four evaluation indicators for Shanghai Stock Exchange Index than temporal pattern attention–long short‐term memory model, which ranks second. For all of the models considered in this paper, adding sentimental contagion mechanism can significantly improve the volatility forecasting accuracy. The error of MTGNN is reduced the most, with a 15.21% average reduction for the Shanghai Stock Exchange Index. The contagion relationship among media reports, investor sentiment, and attention can help provide new ideas for enhancing the precision of volatility forecasting from the public opinion environment in the financial market. [ABSTRACT FROM AUTHOR]

This study constructs a Chinese lottery index (LI) based on six popular lottery preference variables by using the partial least squares method and examines the relationship between the LI and future stock market returns during the period from January 2000 to December 2021. We find that the LI can negatively predict stock market excess returns in‐sample and out‐of‐sample. In addition, the LI can generate a large economic gain for a mean–variance investor. Finally, the predictive sources of the LI stem from a cash flow channel and can be explained by the positive volume–volatility relationship and investor attention. [ABSTRACT FROM AUTHOR]

This study examines how a stock security's cash flow duration impacts stock price reactions at earnings announcements. We find that stock price reactions are positively associated with cash flow duration, especially when earnings surprises are negative. Our results imply that long duration creates a leverage effect that magnifies the price reaction around earnings announcements. We further show that the greater stock price reaction for firms with longer cash flow duration could be caused by less accurate analyst forecasts and short-sales constraints. Finally, we find that the effect of cash flow duration generally increases over time, being more notable during financial crisis and less notable when investor sentiment is high. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between product life cycle and book-to-market effect on cross-sectional stock returns. While previous papers suggest that the book-to-market effect is related to a firm's market value and fundamental value, this paper examines the product life cycle, which directly affects future cash flows. We find that the book-to-market effect is stronger for firms with a long product life cycle, which is consistent with the mispricing story in explaining the book-to-market effect. We further find that the role of product life cycle is more critical for firms with high investor limited attention, and that the product life cycle in part explains intangible returns. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of manager-investor interaction quality on stock returns by utilizing an online IPO roadshow dataset and leveraging a word-embedding model. We find that such interactions are positively valued, as reflected in initial returns. The effect is particularly pronounced for firms characterized by higher levels of information asymmetry, greater investor attention, increased question uncertainty, or discussions on topics not covered in prospectus. Additionally, our research reveals that effective management communication leads to increased first-day turnover rates and thus higher returns. These heightened returns persist up to 180 days following the IPO, without displaying a significant long-term reversal associated with interaction quality. These findings underscore the meaningful impact of the quality of manager-investor interactions on firm valuation. [ABSTRACT FROM AUTHOR]

Whether informed investors (e.g., professionals) trade stocks based on the fundamentals or ride on market sentiment is an empirical question, but few attempts have been made due to the lack of field data. We, therefore, conduct a laboratory experiment and examine if informed investors trade stocks based on their anticipation of less‐informed investors' valuation. In our experimental market, informed traders know the fundamental value of the stock; less‐informed traders do not know the accurate value and their valuation may be biased upwards or downwards. We find that informed traders ride on the (mis)valuations of less‐informed traders, and the speculative trades of the informed traders significantly affect stock prices. Our experimental result explains why stock prices are subject to the mass psychology sentiment even in markets with informed investors. [ABSTRACT FROM AUTHOR]