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We use a random forest model to classify firms' financial constraints using only financial variables. Our methodology expands the range of classified firms compared to text-based measures while maintaining similar levels of informativeness. We construct two versions of our constraint measures, one using many firm characteristics and the other using a small set of more primitive characteristics. Using our measures, we find that institutional investors hold a lower percentage of shares in equity-focused constrained firms, while retail investors show a preference for them. Equity issuance and investment of constrained firms also increases during periods of high investor sentiment. [ABSTRACT FROM AUTHOR]

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]

This paper investigates the impact of unscheduled news announcements on market contagion during the COVID‐19 pandemic. Using coexceedance of stock returns as a metric for market contagion effect, we assess the contribution of news releases from the United States and China on the financial contagion of a representative group of global equity markets through a quantile analysis framework. The empirical results are mixed: news events originating in the United States have a greater impact on market contagion compared with those originating in China, especially at lower quantiles. Stock markets respond asymmetrically to good news versus bad news, and the latter lead to a sharper common fall among the markets than the boost to the market caused by good news. We also find evidence that conditional variance and investor sentiment play some role in the spread of financial market crises, despite differences in extent and direction. [ABSTRACT FROM AUTHOR]

While the oil market‐investors sentiment (IS) has been considerably investigated, almost all studies have focused on the assumption of a constant relationship, and no attention has been given to the causality analysis in a time‐varying approach. To fill this gap, this study investigates the predictive power between IS and oil price based on a time‐varying Granger causality test. Using data over the period 1987–2020, we find evidence of significant bidirectional asymmetric time‐varying causal influences between investor sentiment and oil prices, suggesting that oil prices may predict investor sentiment and vice versa. Besides, the results suggest that bearish (bullish) investor sentiment has positive (negative) influences on oil prices during major economic and political events. In contrast, oil price exerts an influence on the sentiment which switches between positive and negative from one period to another. Further analysis shows that uncertainty related to the oil and equity markets can be a driver of the predictive power of oil prices on the bearish IS. [ABSTRACT FROM AUTHOR]

This study investigates the role of two prominent concepts in finance: limits to arbitrage and investor sentiment in stock prices. The study examines how changes in market-wide investor sentiment and limits to arbitrage can affect the performance of nine UK stock market anomalies. The extant literature relating to investor sentiment focuses mainly on the US stock market, whilst research on the UK market typically examines aggregated index-level data. In addition, previous studies have focused on examining investor sentiment and limits to arbitrage separately. Using data from UK-listed companies over the period January 1997 to December 2019, the study finds that five stock market anomalies were related to changes in UK investor sentiment and produced significantly higher returns following periods of high investor sentiment, while the effect of limits to arbitrage was mostly limited. However, the interaction analysis provided support to the limits to arbitrage theory and demonstrated that the effect of high investor sentiment on stock market anomalies was more pronounced when combined with high limits to arbitrage and had less effect during periods characterised by low limits to arbitrage. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine whether the audit committee moderates the relationship between audit quality and market reactions. Design/methodology/approach: Using fixed effects and the GMM model for robustness, the study used 472 publicly listed firms on South Africa's Johannesburg stock exchange spanning a period of six years from 2014 to 2019. Findings: Results obtained show that audit quality impacts market reactions through share price and adjusted market returns. And, that the audit committee moderates the relationship between audit quality and market reactions in South Africa's publicly listed firms. An effective audit committee is expected to play a crucial role in overseeing the audit process, ensuring the independence of auditors and promoting transparency and accountability which in turn impacts asset prices. Research limitations/implications: The study implies that governments and regulatory bodies in other developing economies could strengthen regulations about companies' Acts, how firms regulate themselves and more so audit committees. Firms can also strive to make sure that audit committees are staffed with experts to promote higher audit quality and investor attention to get access to the much-alluded capital. Originality/value: To the best of the authors' knowledge, the study adds value by being the first to explore the subject matter of the importance of audit committees in defining audit quality and market reactions in publicly listed firms. The research adds to the body of knowledge on corporate governance and audit quality. It provides a case study specific to the South African context, contributing to the global literature on these topics. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine the ramifications of corporate environmental, social and governance (ESG) investing in zombie firms and considers how external funding support may moderate this relationship given the sustainable nature of ESG performance, which often incurs costs. Design/methodology/approach: Panel regression analyses used data from China's A-share listed companies from 2011 to 2019, resulting in a data set comprising 6,054 observations. Findings: Despite firms' additional financial burdens, corporate ESG investing emerges as a catalyst in resurrecting zombie firms by attracting investor attention. Further analysis underscores the significance of funding support from entities such as the government and banks in alleviating ESG cost pressures and enhancing the efficacy of corporate ESG investing. Notably, the positive impact of corporate ESG investing is most pronounced in non-heavily polluting and non-state-owned firms. The results of classification tests reveal that social (S) and governance (G) investing yield greater efficacy in revitalizing zombie firms compared to environmental (E) investing. Practical implications: This research enriches the discourse on corporate ESG investing and offers insights for governing zombie firms and shaping government policies. Originality/value: By extending the domain of ESG research to encompass zombie firms, this paper sheds light on the multifaceted role of corporate ESG investing. Furthermore, this study comprehensively evaluates the influence of external funding support on the positive outcomes of ESG investing, thereby contributing to the resolution of the longstanding debate on the relationship between ESG performance and corporate financial performance, particularly with regard to ESG costs and benefits. [ABSTRACT FROM AUTHOR]

The tourism literature widely documents how emotions, mood and optimism drive the demand for tourism. However, the literature is mute on whether sentiment plays a role in the supply side. We use a forward-looking forecasting model to investigate the interplay between various sentiment measures and travel. The simulation robustly accounts for the effect of trade, real GDP, and foreign direct investment on travel services. The result shows a long-term relationship between travel services and customer sentiment, hence, an increase in the bull-bear spread (BBS), in the short run, has a negative influence on travel services, but this link fades in the long run. Consumer confidence, on the other hand, has a positive impact on travel services in both the short and long run. Our findings advocate the consideration of sentiments when modelling travel services export. The forecasting model also reveals that travel services will decline in the future. [ABSTRACT FROM AUTHOR]

Purpose: This study delves into Bitcoin's return dynamics to address its pronounced volatility, particularly in extreme market conditions. We analyze a broad range of explanatory variables, including traditional financial indicators, innovative cryptocurrency-specific metrics and market sentiment gauges. We uniquely introduce the Conference Board Leading Economic Indicator (LEI) to the cryptocurrency research landscape. Design/methodology/approach: We employ quantile regression to examine Bitcoin's daily and monthly returns. This approach captures timescale dependencies and evaluates the consistency of our findings across different market conditions. By conducting a thorough analysis of the entire return distribution, we aim to reveal how various factors influence Bitcoin's behavior at different risk levels. The research incorporates a comprehensive set of explanatory variables to provide a holistic view of Bitcoin's market dynamics. Additionally, by segmenting the study period, we assess the consistency of the results across diverse market regimes. Findings: Our results reveal that factors driving Bitcoin returns vary significantly across market conditions. For instance, during downturns, an increase in transaction volume is linked to lower Bitcoin returns, potentially indicating panic selling. When the market stabilizes, a positive correlation emerges, suggesting healthier ecosystem activity. Active addresses emerge as a key predictor of returns, especially during bearish phases, and sentiment indicators such as Wikipedia views reveal shifting investor optimism, depending on market trends. Monthly return analysis suggests Bitcoin might act as a hedge against traditional markets due to its negative correlation with the S&P 500 during normal conditions. Practical implications: The study's findings have significant implications for investors and policymakers. Understanding how different factors influence Bitcoin returns in varying market conditions can guide investment strategies and regulatory approaches. Originality/value: A novel contribution of this study is the identification of Bitcoin's sensitivity to broader economic downturns as demonstrated by the negative correlation between LEI and returns. These insights not only deepen our understanding of Bitcoin market behaviour but also offer practical implications for investors, risk managers and policymakers navigating the evolving cryptocurrency landscape. [ABSTRACT FROM AUTHOR]

Governing greenwashing in environmental, social, and governance (ESG) disclosure is an important issue, but relevant literature is scant. Based on the data on Chinese A‐share listed firms from 2012 to 2021, we investigate the governance role of corporate digital transformation (DT) in ESG greenwashing and its influencing mechanism. We find that DT significantly inhibits ESG greenwashing. Moreover, DT mitigates ESG greenwashing by enhancing corporate green technology innovation (i.e., innovation channel), reducing information asymmetry (i.e., information channel), and increasing trade credit (i.e., resource channel). From the perspective of investor attention, we find that both retail investors' online opinions and institutional investors' site visits strengthen the inhibitory effect of DT on ESG greenwashing. Furthermore, such an inhibitory effect is more pronounced in large firms, industries with high competition, and regions with strong intellectual property protection. This research provides new insights and policy suggestions for governing corporate ESG greenwashing behavior. [ABSTRACT FROM AUTHOR]

Financing sources for urban construction have garnered significant attention globally. Among various financing methods, the urban construction investment bond (UCIB) is unique to China. The UCIB credit spread, which represents the compensation for credit risk, has become a focal point for researchers. However, owing to shortcomings of previous approaches, few scholars have accurately assessed the impact of implicit government guarantees on credit spreads. This study introduces an innovative approach that uses orthogonal decomposition to extract proprietary information from credit ratings, reflecting implicit government guarantees. After accounting for bond factors, local government financing vehicle factors, and macroeconomic conditions, the implicit government guarantee substantially reduces the UCIB's credit spread. This conclusion remains robust when controlling for investor attention, regional factors, or duration. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) is used to identify the person’s opinion from unstructured information. It is broadly applied to predict stock market movement direction to recognize the public opinion towards a company or products. The conventional techniques designed for SA do not provide higher accuracy, which impacts the reliability of stock market prediction. In order to improve the prediction performance, a Gensim Lovins Truncative Morisita–Horn’s Broken-stick Regression-based Recursive deep neural network (GLTMBR-RDNN) is introduced for predicting the future outcomes in the stock market with a lesser error rate and minimal time. The customer reviews are collected from a large database. The GLTMBR-RDNN includes different layers for learning the input reviews. In the GLTMBR-RDNN technique, the first preprocessing of the text is carried out in the first hidden layer by removing stop words, stem words, truncation and so on. First, the Gensim tokenizer is applied in the preprocessing step to partition the text into a number of words. The proposed GLTMBR-RDNN technique uses a Sklearned model for stop word removal. Followed by Lovins truncative stemming process is carried out in the preprocessing step. Finally, the Normalization process is done for transforming the words into a standard form. After preprocessing, Morisita–Horn’s Broken-stick Regression process is performed in the second hidden layer for predicting the future stock market value based on the classification of customer reviews by setting the breakpoint to the similarity score between the reviews. In this way, future stock market values are efficiently identified with enhanced classification accuracy in the output layer. The result of the suggested GLTMBR-RDNN technique is analyzed using metrics such as accuracy, F-measure, precision, recall and prediction time based on various input reviews. The results prove that the introduced GLTMBR-RDNN technique improves the performance of accuracy with less prediction time when compared to existing methods. [ABSTRACT FROM AUTHOR]

We examine how investor emotions and Bitcoin price influence each other using intraday data and textual analysis. We extract emotions from a popular online chatting window at one of the largest cryptocurrency exchanges in Korea. To control for global factors, we analyse relative Bitcoin prices and the differences between the Korean exchange and other global prices. The identified emotions predict the return and volatility of Bitcoin price one hour ahead. The results are economically significant: simple arbitrage trading strategies using the relationship between emotions and Bitcoin prices generate profits. Consequently, investor emotions drive Bitcoin prices, suggesting irrational crypto-markets that rational speculators may exploit. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic’s onset introduced unforeseen disruptions to the Chinese stock market. This study uses all A-share companies from 20 January to 26 April 2020, as research samples to explore the impact of public attention on investor sentiment and the stock market during the pandemic. Our findings indicate that the heightened public attention with pandemic-related information triggers disorderly trading behaviours, which ultimately leads to a decline in stock returns, enhanced stock market volatility, and a climb in trading volume. Mechanism tests further find that the level of public concern about COVID-19 mainly affects investor sentiment, which in turn has a negative impact on the stock market. Additionally, heterogeneity analyses reveal that during the COVID-19 pandemic, this negative effect of public attention on stock market is more pronounced in labour-intensive firms and the tertiary sector. Our conclusions provide practical insights and guidance for financial regulatory agencies to maintain stock market stability during sudden public events. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between investor sentiment and flow performance sensitivity of mutual funds in China. We first find that fund flow is positively related to performance in the past quarter and negatively associated with performance in the current quarter. Using a machine learning technique to establish proxies for investor sentiment, we show that positive sentiment mitigates this negative correlation by relieving investors' anxiety about risk and decreasing redemption. The effect is more pronounced in retail dominated funds, volatile markets and growth style funds. This finding is robust after controlling for investor attention, sentiment divergence and percentile return rankings. [ABSTRACT FROM AUTHOR]

To optimise the regulatory approach, the China Securities Regulatory Commission (CSRC) introduced the double‐random inspection policy (DRIP), which mandates that the provincial branches of the CSRC randomly select at least 5% of local listed firms each year and randomly assign inspectors to conduct on‐site inspections of their information disclosure and corporate governance practices. This paper investigates the real effect of the DRIP on corporate financial fraud. Performing a multi‐period synthetic difference‐in‐differences model (SDID), we first find that the random inspections of CSRC have a positive causal effect on the probability of exposing corporate financial fraud. Furthermore, our heterogeneity analysis reveals that this effect is more pronounced for private firms and firms with poor accounting information quality. We then delve into the mechanisms through which random inspections affect corporate financial fraud. Our findings suggest that random inspections influence corporate behaviour by increasing media and investor attention, as well as prompting the issuance of inquiry letters by stock exchanges. Finally, we examine the economic consequences of random inspections and find that random inspections by the CSRC reduce firms' stock price crash risk. [ABSTRACT FROM AUTHOR]

We investigate the impact of the announcements by firms to adopt blockchain technology on stock prices and the different characteristics of stock prices under different types of blockchain technologies. Simultaneously, we propose two impact mechanisms: one is investor sentiment, and the other is the firm's governance of social media. We find that the firm's stock price has shown an increasing trend since the release of the announcement of the adoption of blockchain technology. At the same time, firms engaged in blockchain research, development and investment, and that simultaneously deploy other financial technologies, as well as firms that deploy blockchain technology for non‐speculative purposes, have better stock prices. In addition, the layout of a firm's blockchain may affect its stock price by influencing investor sentiment and public opinion. [ABSTRACT FROM AUTHOR]

This research examined the effects of Turkish banking risks on investor behavior. This context, the period of nine deposit banks covering the period between 2008Q3-2019Q3 was analyzed using panel regression estimators. Researchers found that systemic and systematic risks negatively affected investor sentiment in the real sector. The rise in systemic and market risks of banks has led to an increase in investor pessimism. The findings showed that the real sector confidence index can be used as an effective early warning system for financial instability. It has shown that banking sector risks have the potential to spread to the entire economy through real sector investment behavior. The results revealed that stock returns positively affect real-sector investment behavior. Accordingly, positive developments in the stock markets encouraged the real-sector to invest. In the research, the effects of selected macro variables on investor sentiment were analyzed. The forecast results documented that inflation rates, the current account balance, and the VIX uncertainty index negatively affected real-sector investor behavior. On the contrary, it was determined that the effect of the MSCI Europe index on investor sentiment was positive. [ABSTRACT FROM AUTHOR]

Candlestick charts provide a visual representation of price trends and market sentiment, enabling investors to identify key trends, support, and resistance levels, thus improving the success rate of stock trading. The research presented in this paper aims to overcome the limitations of traditional candlestick pattern analysis, which is constrained by fixed pattern definitions, quantity limitations, and subjectivity in pattern recognition, thus improving its effectiveness in dynamic market environments. To address this, a two-layer clustering method based on a candlestick sequence simlarity matching model is proposed for identifying valid candlestick patterns and constructing a pattern library. First, the candlestick sequence similarity matching model is used to address the pattern matching issue; then, a two-layer clustering method based on the K-means algorithm is designed to identify valid candlestick patterns. Finally, a valid candlestick pattern library is built, and the predictive ability and profitability of some patterns in the library are evaluated. In this study, ten stocks from different industries and of various sizes listed on the Shanghai Stock Exchange were selected, using nearly 1000 days of their data as the test set. The predictive ability of some patterns in the library was evaluated using out-of-sample data from the same period. This selection method ensures the diversity of the dataset. The experimental results show that the proposed method can effectively distinguish between bullish and bearish patterns, breaking through the limitations of traditional candlestick pattern classification methods that rely on predefined patterns. By clearly distinguishing these two patterns, it provides clear buy and sell signals for investors, significantly improving the reliability and profitability of trading strategies. [ABSTRACT FROM AUTHOR]

GARCH-EVT-Copula is proved to have some advantages in improving the accuracy of predicting returns. Investor sentiment described by four indicators (market-turnover ratio, advance-decline ratio, new-highs/lows ratio, and Arms index) attracts increasing attention from researchers. In view of these two factors, firstly, we construct the ARMA-GARCH-Sent (AGS) model with investor sentiment indicator by principal component analysis (PCA). Secondly, considering the advantages of extreme value theory (which can deal with extreme deviations in the value of the probability distribution), we present the ARMA-GARCH-Sent-EVT (AGSE) model to describe the daily logarithmic return series of stocks. In addition, the Copula model is used to construct the multivariate distribution of daily logarithmic stock return series to capture their asymmetric and nonlinear characteristics. Furthermore, we propose an ARMA-GARCH-Sent-EVT-Copula (AGSEC) portfolio model with Copula. In order to highlight the advantages of our model, we make a comparative analysis of three models: the original ARMA-GARCH-Copula (AGC) model, the ARMA-GARCH-Sent-Copula (AGSC) model and the AGSEC model. Finally, we use the data of SHSE (Shanghai Stock Exchange) and SZSE (Shenzhen Stock Exchange) for empirical analysis and compare the dynamic portfolio strategies of the three models. The results show that our model AGSEC with investor sentiment is superior to the other two models, namely, it has higher return rates under the same constraint conditions, which means investor sentiment can assist investors in better navigating market dynamics and plays an increasingly important role in shaping portfolio performance and risk management. [ABSTRACT FROM AUTHOR]

The efficient use of text data is very important in investor sentiment research and other fields. Through the sentiment classification of text data containing investor sentiment, we can effectively and accurately identify the sentiment contained in the text. This paper takes the futures market forecast text published by 21 futures companies as the data source and constructs a sentiment classification model of the market forecast text based on BERT (Bidirectional Encoder Representations from Transformers) according to the characteristics of the market forecast text. The sentiment classification of the market forecast text is carried out by using the sentiment classification model of the market forecast text based on BERT and a classification model based on the classical classification algorithm. The classification effects of different models are compared. The results show that the optimized BERT model has the best classification effect. This enriches the research methods of investor sentiment measurement in the financial field and improves the accuracy of this kind of sentiment measurement result. [ABSTRACT FROM AUTHOR]

Incorporating ground-breaking technologies such as deep learning (DL) has revolutionized predictive modelling in the rapidly evolving landscape of the finance sector. DL approaches, capable of extracting complex patterns from vast data collections, become an efficient approach for predicting financial trends. By integrating the complex neural network architecture with comprehensive datasets, including investor sentiment, market indicators, and economic variables, finance experts have introduced prediction models well known for their ability to capture the nuanced dynamics of financial markets with remarkable performance. Incorporating DL approaches within the finance sector provided the basis for more informed decision-making, enabling institutions, investors, and analysts to capitalize on emerging opportunities with greater confidence and precision and navigate market volatility. This study develops a novel quasi-oppositional coot algorithm with a deep learning-based predictive method on the financial sector (QOCODL-PMFC) technique. The QOCODL-PMFC technique aims to perform a prediction process on the financial sector. The QOCODL-PMFC method applies min-max normalization to measure the input dataset into a meaningful format to achieve this. Next, the QOCODL-PMFC method designs the QOCO technique for selecting an optimal set of features. The QOCODL-PMFC technique applies the attention bidirectional gated recurrent unit (ABiGRU) model for the prediction process. The Harris Hawks Optimization (HHO) model is utilized to boost the performance of the ABiGRU network. The simulation evaluation of the QOCODL-PMFC technique is tested under a benchmark finance dataset. The experimental values of the QOCODL-PMFC technique exhibit a minimal MSE of 0.7452 over other models. [ABSTRACT FROM AUTHOR]

Alternative data are data gathered from nontraditional sources beyond company filings and analyst research. Alternative data are crucial in investing, offering unique insights and competitive advantages. The demand for alternative data has skyrocketed in the past two decades, due to regulatory changes and the growing importance of intangible assets, such as intellectual property. Alternative data cover various sources, including firm-released information, government-released information, information about investor attention and trading, and third-party information. However, the alternative data landscape is constantly evolving due to alpha decay, technological advancements, regulatory changes, and market efficiency. These challenges require investors to continuously adapt their strategies, discover new data sources, and develop sophisticated analysis techniques to maintain an edge in an increasingly data-driven financial world. [ABSTRACT FROM AUTHOR]

This article examines the concept of the term underground and its association with a range of dance music parties and venues that emerged in Liverpool, UK between 2008 and 2023. Drawing on a critical review of literature relating to the term, underground is positioned as a cultural signifier exhibiting three core characteristics: (1) as secretive resistance; (2) as a countercultural antithesis to mainstream; and (3) as a social imaginary. Using ethnographic material from scene practitioner interviews (n = 35) and an online survey of clubbers (n = 194), the article analyses discourses towards the underground as a concept, and how underground characteristics influence scene cultural practices. The findings reveal participants' use of the term as a descriptor of experiential feelings within temporal spaces. Drawing on the work of Gopaldas, these feelings as situated as sentiments which inform the shared intentions and practices within a local dance music scene. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of retail investor attention on market reaction following analysts' recommendation revisions in the Chinese stock market. The results suggest that the impact of retail attention on post-recommendation revision drift is asymmetric. Specifically, retail attention mitigates post-upgrade announcement drift, whereas it aggravates post-downgrade drift. After a series of arrangements to address the potential endogeneity concerns and ensure robustness, the results still hold. Moreover, we reveal retail attention facilitates the absorption of individual firms' information and increases liquidity to attenuate post-upgrade drift, whereas retail attention induces underreaction due to short-sale restrictions and disposition effects regarding stronger post-downgrade drifts. The results of the additional heterogeneity test provide further evidence of channel tests and reveal that prior positive sentiment can accelerate the integration of good news into stock prices. These findings enrich the existing literature on retail investors' role in the price discovery process following the release of fundamental information. [ABSTRACT FROM AUTHOR]

This paper examines the quantile connectedness between Real Estate Investment Trusts (REITs), housing market sentiment, and stock market sentiment in the U.S. over the period between January 2014 and June 2022 using the quantile vector autoregression (QVAR) model. We find modest spillover effects at the median quantile (8.51%), which become more pronounced at the extreme tails (between 50.51% and 59.73%). The COVID-19 pandemic amplifies these interconnections. REITs are net receivers at the median but net transmitters at extreme quantiles, while stock market sentiment mainly transmits during normal conditions and receives in highly bullish markets. Home purchase sentiment shifts from fluctuating roles before the pandemic to being a net transmitter post-2021. Overall, negative shocks have a greater impact than positive ones, and REITs exhibit stock-like behavior. These findings underscore the importance for fund managers and investors to consider sentiment volatility in both stock and real estate markets, especially during extreme market conditions. [ABSTRACT FROM AUTHOR]

This study employs the economic policy uncertainty index to gauge the level of economic policy uncertainty in China. Utilizing textual data from the growth enterprise market internet community, we construct the growth enterprise market investor sentiment index by applying the deep learning ERNIE (Enhanced Representation through Knowledge Integration) model, thereby capturing investors' sentiment within the growth enterprise market. The dynamic interplay between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is scrutinized via the TVP-SV-VAR (time-varying parameter stochastic volatility vector auto-regression) model, and the asymmetric response of different industries' stock returns within the growth enterprise market to economic policy uncertainty and investor sentiment shock. The findings of this research are that economic policy uncertainty exerts a negative influence on both investor sentiment and returns of the growth enterprise market. While it may trigger a temporary decline in stock prices, the empirical evidence suggests that the impact is of short duration. The influence of investor sentiment on the growth enterprise market returns is characterized by a reversal effect, suggesting that improved sentiment may initially boost stock prices but could lead to a subsequent decline over the long term. The relationship between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is time-variant, with heightened sensitivity observed during bull markets. Lastly, the effects of economic policy uncertainty and investor sentiment on the returns of different industries within the growth enterprise market are found to be asymmetric. These conclusions contribute to the existing body of literature on the Chinese capital market, offering a deeper understanding of the complex dynamics and the factors influencing market behavior. [ABSTRACT FROM AUTHOR]

In behavioral economics, it has widely been documented that there might be a close relationship between overall market sentiment and economic performance, such as GDP per capita. In this paper, we investigate the effects of market sentiment on stock market volatility, which has widely been recognized as an important factor for economic sustainability. In particular, we aim to identify the existence of spillover effects of market sentiments on global stock market volatility. As a first attempt, we chose ten countries from major economic regions over the world (including America, Asia, Europe, and Oceania), and analyzed their interdependence and interconnectedness using a GJR-GARCH-MIDAS model. The results highlight that an individual country's stock market volatility is significantly influenced not only by its own market sentiment (proxied by the consumer confidence index) but also by the overall market sentiments of other countries across the world. The results also highlight significant country-by-country heterogeneity in the time lags of the global spillover effects, which indicates substantial heterogeneity in the behavioral dynamics of individual countries. [ABSTRACT FROM AUTHOR]

This study introduces Fin-ALICE (Artificial Linguistic Intelligence Causal Econometrics), a framework designed to forecast financial time series by integrating multiple analytical approaches including co-occurrence networks, supply chain analysis, and emotional sentiment analysis to provide a comprehensive understanding of market dynamics. In our co-occurrence analysis, we focus on companies that share the same emotion on the same day, using a much shorter horizon than our previous study of one month. This approach allows us to uncover short-term, emotion-driven correlations that traditional models might overlook. By analyzing these co-occurrence networks, Fin-ALICE identifies hidden connections between companies, sectors, and events. Supply chain analysis within Fin-ALICE will evaluate significant events in commodity-producing countries that impact their ability to supply key resources. This analysis captures the ripple effects of disruptions across industries and regions, offering a more nuanced prediction of market movements. Emotional sentiment analysis, powered by the Fin-Emotion library developed in our prior research, quantifies the emotional undertones in financial news through metrics like "emotion magnitude" and "emotion interaction". These insights, when integrated with Temporal Convolutional Networks (TCNs), significantly enhance the accuracy of financial forecasts by capturing the emotional drivers of market sentiment. Key contributions of Fin-ALICE include its ability to perform month-by-month company correlation analysis, capturing short-term market fluctuations and seasonal patterns. We compare the performance of TCNs against advanced models such as LLMs and LSTMs, demonstrating that the Fin-ALICE model outperforms these models, particularly in sectors where emotional sentiment and supply chain dynamics are critical. Fin-ALICE provides decision-makers with predictive insights and a deeper understanding of the underlying emotional and supply chain factors that drive market behaviors. [ABSTRACT FROM AUTHOR]

Digital transformation is a crucial way to promote the modernization of corporate governance capabilities. We investigate the impact of digital transformation on managers' excess perks consumption using a sample of Shanghai and Shenzhen A-share listed companies from 2010 to 2021. Our research shows that digital transformation can significantly inhibit managers' excess perks consumption, and this inhibitory effect is mainly achieved by strengthening internal supervision and improving investors' attention. In addition, this inhibitory effect primarily occurs in the growth and maturity sub-samples and the short CEO tenure sub-samples. [ABSTRACT FROM AUTHOR]

Leveraging deep learning to analyse COVID‐19 image sentiment, this study reveals its significant impact on stock market dynamics. It highlights how vivid imagery prompts marked emotional responses, altering market performance and how news sentiment can modulate this effect. Further, it underscores the pivotal role of forum‐based investor sentiment, particularly affecting small‐minus‐big stocks during downturns and trading week commencements. This research not only advances behavioural finance understanding but also informs management and regulatory strategies. [ABSTRACT FROM AUTHOR]

In acquisitions of private firms, new blockholders (NewBs) are expected to form when substantial stocks are paid. Investors react strongly to a NewB signal, given the perceived monitoring and certification benefits. However, they largely ignore value‐relevant but less salient signals, such as the true quality of the acquisition. Investors' limited attention allows financially weak firms to adopt the NewB strategy and take speculative deals. Our results support this inattention hypothesis: NewB acquirers are financially weaker and earn higher announcement‐period returns, but lower long‐run returns; moreover, acquirers' financial weakness negatively predicts long‐run performance. [ABSTRACT FROM AUTHOR]

Using a sample of Chinese listed firms from 2008 to 2020, we find that manager‐specific upward manipulation of tone in the Management Discussion and Analysis (MD&A) section is associated with greater stock price synchronicity. This suggests that upward tone manipulation decreases the stock's idiosyncratic information content. This relationship between abnormally positive tone and stock price synchronicity is negatively moderated by the firm's revenue growth rate, while investor irrational sentiment positively moderates this relationship. Additionally, positive tone manipulation significantly increases audit aggressiveness and decreases analyst optimism bias. [ABSTRACT FROM AUTHOR]

This study explores the influence of air pollution on the corporate bond market in China. An air pollution premium is documented, whereby bonds issued in more polluted areas are associated with higher offering yields at issue in the primary market and higher yield spreads in the secondary market. The statistically and economically significant air pollution premium is robust to a battery of sensitivity checks. The air pollution premium is associated with rising investor attention to climate risk in financial markets. [ABSTRACT FROM AUTHOR]

Traditional asset pricing models face challenges from financial anomalies, prompting exploration through behavioural finance theory. This study analyses the nuanced relationship between individual investor sentiment and key stock market variables. To assess the impact of individual investor sentiment on stock returns, volatilities, and trading volumes using the American Association of Individual Investors (AAII) sentiment index. Using regression models, we examine the relationship between individual investor sentiment and various stock characteristics across 480 components of the Standard & Poor's 500 index. We find a positive relationship between the AAII sentiment index and stock returns and a negative relationship with volatility and trading volume. Our study contributes to understanding the intricate role of individual investor sentiment in financial markets. [ABSTRACT FROM AUTHOR]

This special issue of Business Systems Research (SI of the BSR) is being co-published by the Slovenian Society INFORMATIKA – Section for Operational Research (SSI -SOR). It focuses on recent advances in Operations Research and Management Science (OR / MS), with a particular emphasis on linking OR / MS with other areas of quantitative and qualitative methods in the context of a multidisciplinary framework. The ten papers that were chosen for this Special Issue of the BSR present advancements and new techniques (methodology) in the field of Operations Research (OR), as well as their application in a variety of fields, including risk management, mathematical programming, game theory, gravity, spatial analysis, logistics, circular economy, continuous improvement, sustainability, e-commerce, forecasting, Gaussian processes, linear regression, multi-layer perceptron, and machine learning. [ABSTRACT FROM AUTHOR]

We employ the "Two Sessions," comprising the National People's Congress and the Chinese People's Political Consultative Conference, as a proxy for measuring policy uncertainty. In our analysis, we utilize a regression model, the three-path mediated effect framework, and the Campbell and Shiller decomposition method to delve into the influence of policy uncertainty on asset pricing within China's financial market. Our findings reveal an increase in stock returns during the months leading up to the "Two Sessions," evident at both the market and firm levels. Notably, the extent to which stock returns respond to policy uncertainty is contingent on various firm-specific characteristics, including ownership structure, company size, and profitability. Furthermore, our investigation confirms that investor sentiment serves as a complete mediator in the relationship between policy uncertainty and its impact on asset prices. Additionally, we identify future cash flow as the primary conduit through which policy uncertainty directly exerts its influence on asset prices. [ABSTRACT FROM AUTHOR]

This study established the direction, magnitude, and duration of the causality between CDS and selected Chinese stock sector at industry level. A nonparametric causality-in-quantile test and a CQ correlation test were applied to the data sampling over the daily period January 2, 2019, to January 6, 2023 covering a period marked by global shocks, including the outbreak of COVID-19 and Russia–Ukraine conflict. The empirical results reveal that CDS advances to play its economic role as a risk transfer, and to effectively predict the returns of sectors stock under bad market conditions. Moreover, the time-varying CQ correlations suggest that such amplified connectedness could be driven by extreme market circumstances in both the upper and lower quantiles. The findings provide important recommendations for investors, regulatory authorities, and policymakers to understand the pivotal roles of market sentiments in inducing co-movement between sovereign CDS spreads and selected Chinese stock sector. [ABSTRACT FROM AUTHOR]

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This empirical study delves into the intricate interplay of market dynamics, financial returns and investor satisfaction, examining the roles of market sensitivity (MS), news and events (NE), global economic conditions, country economic conditions and company-specific developments. Notably, global economic conditions and country economic conditions significantly impact financial returns, underscoring the relevance of macroeconomic factors in investment outcomes. The study reveals a distinctly negative effect of company-specific developments on financial returns, emphasising the importance of monitoring individual companies in investment portfolios. MS insignificantly affects financial returns but exhibits a notable negative association with investor satisfaction, highlighting its role in shaping investors' emotional experiences. These findings offer insights for future research, suggesting avenues for exploring the impact of specific NE on financial returns and conducting longitudinal studies to understand evolving investor sentiment, contributing to a deeper understanding of the multifaceted factors influencing financial outcomes and investor satisfaction in the dynamic landscape of financial markets. [ABSTRACT FROM AUTHOR]

This study delves into the impact of reversals and investor attention on cryptocurrency returns before and during the COVID-19 pandemic. We employ the Two Stages Least Squares to analyze a sample of the top 20 cryptocurrencies from January 2016 to April 2021. Our results reveal that investor attention positively influences bitcoin returns in both periods, with a more pronounced effect during the pandemic. Conversely, reversals demonstrate a positive correlation with cryptocurrency returns before the outbreak but a negative relationship during the pandemic. Our robustness test further indicates that investor attention positively affects the returns of small and medium-cap cryptocurrencies, while reversals only exhibit positive consequences for small-cap cryptocurrencies. Additionally, our findings highlight stablecoins as a safe haven during the epidemic. The results suggest that investor attention has little influence on the returns of stablecoins, indicating that these coins are primarily resistant to market sentiment due to their inherent stability. The negative impact of the pandemic on the crypto market demonstrates a downward trend through each wave. Despite aligning with attention-induced price pressure and behavioral finance hypotheses, our results do not support efficient market theory or the notion of heterogeneity among investors. This research provides valuable insights for investors and policymakers in devising effective strategies for the cryptocurrency market. [ABSTRACT FROM AUTHOR]

This study examines investor attention connectedness measures before and after the COVID-19 outbreak. We find that investor attention spillovers persist among global equity markets, and developed markets dominate as shock transmitters. The spillover effect increased significantly amidst the COVID-19 pandemic period due to escalating market turmoil. The empirical results suggest that investor attention interdependencies have important implications for improving our understanding of the shock transmission of global equity markets and co-movement dynamics. Our findings offer additional insights to investors and speculators to design better portfolio strategies by considering the net spillover effects of investor attention between numerous equity markets. [ABSTRACT FROM AUTHOR]

As green development gains traction, digital finance, a major engine of the economy, plays a conducive role in improving green total factor productivity. Against this backdrop, avoiding corporate ESG decoupling is essential in the pursuit of green and quality development of enterprises. With data from Shanghai and Shenzhen A-share listed companies from 2016 to 2022, this study explores the impact of digital finance on corporate ESG decoupling, and the findings reveal that digital finance can suppress corporate ESG decoupling, and the effect is significant at the 1% level. Specifically, digital finance curbs corporate ESG decoupling by alleviating financing restraints on enterprises, increasing investment efficiency, improving the quality of information disclosure, and minimizing managerial myopia; the effect is more pronounced in non-state-owned enterprises, high-tech enterprises, and heavy-polluting enterprises; second, investor attention positively moderates the effect of digital finance on corporate ESG decoupling. The research findings are expected to provide an empirical basis and policy recommendations to allow digital finance to play a more effective role in leading enterprises to healthy and quality development. [ABSTRACT FROM AUTHOR]

The main contribution of this study is to assess whether investor sentiment, as measured through textual analysis of newspaper articles or social media posts, does have an effect on the mean returns and on the variance of financial stocks. The analysis is carried on a basket of the S &P 500 components where stock returns and volatility are modeled within the GARCH family augmented, both in the mean and the variance equation, with an exogenous variable representing the investor sentiment; the latter is measured through specific Bloomberg proprietary scores based on News or Twitter feeds. Empirical results support the hypothesis that these indicators do have a positive impact on stock prices: the Twitter based index positively affects the components returns, confirming the outcomes of existing studies, whereas the news based index has a significant impact on their volatility. We also contribute the literature by performing the same analysis across the 11 sectors of the index, evidencing that investor sentiment has a significant impact on Industrials, Health Care, Consumer Discretionary, Consumer Staples, Information Technology and Communication Services. As a further contribution, we perform an out-of-sample analysis to assess the potential effect of Bloomberg sentiment scores on downside risk measures, such as Value at Risk and Expected Shortfall. This subject is relevant to regulators in order to conceive suitable policy interventions during turmoil periods around specific market sectors or stocks. [ABSTRACT FROM AUTHOR]

This research focuses on how market sentiment affects corporate governance in the Chinese market. The sample covers the years from 2014 to 2023. Market sentiment is estimated using a cross-sectional absolute deviation (CSAD) model, and earnings quality is used as an indicator of the consequences of corporate governance. Both mutual fund shareholding and the number of firm visits by mutual fund analysts are verified as effective corporate governance instruments that work well in a regular market but become ineffective when the market sentiment is high. The reason for this is that managers' expectations change, and they may believe that disclosing good news during high-sentiment market periods significantly increases the share prices and helps them meet their performance requirements. In a high-sentiment market, an incentive contract encourages managers to take on projects with inappropriate risk or even manipulate earnings. One potential solution is to adopt venture capital firms' high-water mark and clawback clauses to prevent managers from focusing on short-term goals rather than seeking long-term business sustainability. [ABSTRACT FROM AUTHOR]

The study examines the direction and strength of the relationship between investor sentiment and stock market returns on the Budapest Stock Exchange using six investor sentiment indicators over the period from 1998 to 2024. The results indicate that the relationship is nonlinear and changes during optimistic and pessimistic periods, affecting the direction and strength of the relationship between sentiment and returns. On average, the returns are lower in the 3, 6, 12, and 18 months following an optimistic sentiment compared to the periods following a pessimistic sentiment. Utilizing these results and analyzing a total of 43,200 model variants across 726,426 trades, it is found that the common characteristic of the outperforming model variants for the BUX-index is trading during periods of neutral market sentiment, while optimal trade closures are associated with the beginning or end of optimistic sentiment. [ABSTRACT FROM AUTHOR]