Search Results (232)

This study provides new evidence on the predictability of confidence indices in China’s stock returns. We demonstrate that, during the sample period from 2005:01 to 2022:12, the business confidence index (BCI) positively and significantly predicted subsequent stock market returns, outperforming mainstream economic predictors and other confidence indices. Further, for the pricing effectiveness of the stock market, the BCI and investor sentiment provide complementary sources of information. The predictive power of confidence indices for stock market returns declined significantly during the COVID-19 pandemic. Meanwhile, confidence indices predicted better during bear market periods compared to bull market periods. Finally, in practical investment applications, the BCI and alternative confidence index produce appreciable economic gains for investors. These empirical results also pass the robustness test. Full article

Against the backdrop of rising corporate social irresponsibility (CSI) incidents in China’s capital market, this study examines how CSI affects short-window market reactions and through which investor-side mechanisms this effect operates. Using A-share listed companies in Shanghai and Shenzhen from 2017 to 2021, we construct a CSI index adapted to the Chinese institutional setting and employ an event-study framework combined with mediation and moderation models. The results show that CSI is associated with significantly more negative cumulative abnormal returns. Mechanism tests indicate that CSI is negatively associated with investor sentiment, and lower investor sentiment is associated with more negative market reactions, implying a negative indirect path through investor sentiment. Investor attention further conditions this relationship: when investor attention is higher, the negative market reaction to CSI is stronger, although the baseline interaction result should be interpreted cautiously because its significance is marginal. These conclusions are supported by Heckman two-step estimation, alternative sample construction, and alternative event-window tests. Additional analysis shows that prior CSR reputation can mitigate investor punishment after CSI events, suggesting an insurance effect. Full article

This study examines the impact of public attention on financial markets during the Israel–Hamas conflict, focusing on the TA35 stock index and the Israeli Shekel (ILS) exchange rate over the period October 2023 to April 2025. By distinguishing between global and domestic Google search activity, the analysis investigates whether the origin of attention differentially affects market performance and currency dynamics. Public attention is treated as a real-time proxy for investor sentiment and perceived risk. Methodologically, the study combines Google Trends data with EGARCH(1,1) models to capture both return effects and asymmetric volatility responses. To enhance robustness, Principal Component Analysis (PCA) is applied separately to global and domestic search datasets, generating latent indices that reflect conflict-related and humanitarian narratives. These indices are subsequently incorporated into the empirical models. The findings reveal that global search intensity related to conflict topics exerts a significant negative effect on stock returns and contributes to currency depreciation, reflecting heightened uncertainty and risk aversion. In contrast, domestic search activity is associated with stabilizing or positive effects, suggesting local resilience and confidence. PCA-based models improve explanatory power and confirm that the geographical origin of attention plays a crucial role in shaping financial outcomes. Additionally, the results indicate that attention-driven shocks influence volatility asymmetrically, amplifying downside risk during periods of intensified global concern. Overall, the study contributes to the literature by integrating behavioral indicators into financial risk modeling and providing a novel, real-time framework for assessing how digital attention transmits geopolitical risk into asset prices. Full article

Objective: Contrary to the traditional notion of risk–return trade-off, prior studies document that financially distressed stocks tend to earn lower future returns than their healthier peers. Extending this strand of literature, this study revisits the distress risk anomaly in UK stocks and further examines whether proximity to the 52-week high and lottery-like characteristics of stocks help explain the financial distress anomaly, if any. Data and methods: In this paper, we analyse the distress risk anomaly using a sample of 4514 UK stocks over the period 2000–2021. The analysis is conducted using both the portfolio-sorting method and Fama–MacBeth cross-sectional regressions. Key findings: The empirical findings confirm the persistence of the financial distress anomaly, showing that high-distress stocks earn lower returns than their low-distress counterparts. Consistent with a mispricing explanation, this inverse distress–return relationship is more pronounced for stocks that are difficult to arbitrage and is stronger following periods of market optimism. Furthermore, the analysis reveals that both the 52-week high effect and lottery-like trading, independently and jointly, contribute to the poor performance of financially distressed stocks. This suggests that underreaction and overreaction interact to shape the observed overvaluation of distressed stocks. These findings remain robust to a battery of robustness checks. The results have several important implications for investors, researchers, and regulators. Full article

This study examines how the relationship between perceived financial accounting disclosures (FAD) and investor reactions to negative financial news (IRNFN) is conditioned by two individual-level moderators among 310 retail investors holding shares in project-based organisations (PBOs) listed on the Dubai Financial Market and Abu Dhabi Securities Exchange. The two moderators are framing bias susceptibility, a cognitive predisposition to be influenced by presentational form, and AI channel reliance (AICR), the extent to which investors rely on AI-mediated information channels—including algorithmic news aggregators, robo-advisory tools, AI-curated social media feeds, and automated sentiment-scored financial alerts—for receiving and interpreting corporate disclosures. Drawing on Behavioural Finance Theory and the Theory of Planned Behaviour, the study investigates whether the strength of the FAD–IRNFN association depends on these cognitive and informational processing conditions. The measurement model was estimated using confirmatory factor analysis in AMOS 25, and the moderation hypotheses were tested through path analysis with mean-centred composite scores and bias-corrected bootstrap inference, with a latent interaction robustness check reported in parallel. AI channel reliance emerged as a substantial moderator of the FAD–IRNFN relationship, while framing bias provided a smaller, marginally significant moderating effect. The findings are consistent with the theoretical expectation that, in AI-mediated information environments, the perceived quality and presentation of complex disclosures are associated with stronger, rather than weaker, investor reactions to negative news. Because the design is cross-sectional and based on self-reported data, the results are interpreted as associations rather than causal effects, with implications for disclosure regulation, corporate communication, and AI platform design in the UAE and comparable emerging markets. Full article

Against the backdrop of intensifying global climate change, climate policy uncertainty (CPU) and investor sentiment have become critical factors influencing the stability of financial markets. In this study, a quantitative index of investor sentiment is constructed using stock trading volume, turnover rate, price-to-earnings ratio, circulating market value, and the consumer confidence index. The QVAR-DY model is employed to analyze the risk contagion mechanisms among CPU, investor sentiment, and China’s financial sub-markets across different quantiles. Furthermore, five machine learning models—LSTM, BiLSTM, CNN, XGBoost, and LightGBM—are used to forecast risk spillover indices, and their performance is compared with three benchmark models (ARIMA, Persistence, and HistMean) to systematically evaluate the advantages of machine learning models in capturing tail risk spillover effects. The findings reveal significant cross-market risk contagion in financial markets, characterized by asymmetry. The level of risk spillover under extreme conditions is substantially higher than under normal conditions, indicating high sensitivity to extreme events and major policies. CPU exhibits the most pronounced spillover effect on the money market, while investor sentiment has the greatest impact on the stock market. The stock, real estate, and commodity markets act simultaneously as sources of risk and receivers of shocks. In terms of forecasting performance, LightGBM performs best under normal conditions, whereas LSTM achieves the highest prediction accuracy under extreme conditions. Full article

From the perspective of interactions between energy firm behavior and government intervention strategies, this study develops a contagion control model for energy firm debt default risk utilizing cellular automata and complex network theory. This research investigates the spatio-temporal evolution of risk transmission and evaluates the efficacy of various mitigation protocols through computational simulation. The research results indicate that: (1) An escalation in both the transmission likelihood and the rate of immunity decay significantly amplifies the propagation strength of debt default risks. Conversely, the stability of the energy firm network is bolstered as the probabilities of immunity and recovery increase. (2) The contagion intensity for debt default risk is positively correlated with market noise, the risk appetite of energy firms, and their corporate influence. It is negatively correlated with risk awareness, creditworthiness, regulatory intensity, and policy subsidies. Furthermore, it exhibits an inverted U-shaped relationship with investor sentiment. (3) Within the interconnected network of energy firms, risk contagion can be effectively mitigated not only by enhancing risk perception and credit standing but also by guiding risk preference and managing firm influence. Furthermore, the integration and adjustment of government intervention strategies, such as regulatory intensity and policy subsidies, can more efficiently accelerate the eradication of debt default risk among energy firms. Full article

This study aims to investigate the integration of generative artificial intelligence (GAI) and advanced large language models (LLMs) in financial sentiment research, focusing on improving the accuracy and robustness of financial sentiment classification from investor-generated textual data. The research employs advanced large language models, including XLNet, FinBERT, T5, Gemma-7B, Llama-2, and Llama-3, specifically fine-tuned to address the intricacies of financial language. We utilize generative AI models, such as GPT-4, GPT-3.5, and GPT-2, for data augmentation to mitigate scarcity. The fine-tuned Gemma-7b model proved to be the most successful, with a greater Success Rate (S-rate). The Gemma-7b model showed significant enhancements in performance after fine-tuning, highlighting its capacity to grasp the intricacies of financial emotion. This methodology provides a robust framework for financial sentiment classification and supports the extraction of meaningful sentiment signals from financial text. The results demonstrate the effectiveness of advanced LLMs for financial sentiment analysis and highlight their potential for supporting future research and analytical applications in financial text mining. Full article

This study examines whether retail social media sentiment and community attention explain daily net capital flows into U.S. spot Bitcoin exchange-traded funds (ETFs), and whether issuer brand visibility conditions that relationship. We construct a balanced panel of $N=10$ ETFs over $T=514$ trading days (January 2024 to January 2026) and combine it with 162,819 cleaned Reddit posts to derive three AI-driven discourse variables: engagement-weighted sentiment, community attention, and a novel issuer-specific BrandScore. Entity fixed-effects regressions show that neither aggregate sentiment nor BrandScore level alone significantly predicts fund-level flows; however, the Sentiment $\times$ BrandScore interaction is significant ($\widehat{\beta }=2.930$, $p=0.038$), indicating that sentiment becomes economically meaningful only when attached to a visible issuer. This interaction survives two-way (entity + date) fixed effects ($p=0.012$) and winsorization ($p=0.004$). Panel quantile regressions reveal distributional heterogeneity in the brand-sentiment channel. Rolling 90-day window estimation confirms the mechanism is episodic, with the interaction achieving significance in 62.8% of subsample windows. These results provide suggestive evidence for a brand-filtered sentiment transmission mechanism in digital asset markets. Full article

The study develops and empirically evaluates a forecasting-orientated structural model in which future Bitcoin historical volatility is modelled as being associated with market sentiment and blockchain fundamentals through market uncertainty. Market Sentiment (MS) is specified as a behavioural construct, Blockchain Fundamentals (BF) as network conditions, and Market Uncertainty (MU) as an endogenous regime construct that consolidates signals shaping historical volatility at t+1. Using 262 weekly observations from January 2021 to January 2026, the analysis applies partial least squares structural equation modelling (PLS-SEM) with formative constructs and a forward-dated volatility target to preserve temporal ordering. Paths are evaluated with bootstrapping, effect sizes, and mediation analysis, while predictive performance is assessed using PLSpredict, the cross-validated predictive ability test (CVPAT), benchmark-based comparison, and Diebold-Mariano (DM) tests. MU emerges as the dominant predictor of Future Historical Volatility, denoted as HV(t+1) in the structural model (β = 0.864, p-value < 0.001; f² = 2.036). The effect of BF is largely indirect, with 91.02% of the total effect transmitted via uncertainty, indicating indirect-only mediation. The model explains substantial variation in HV(t+1) (R² = 0.791) and shows predictive relevance (Q² predict = 0.287), while the benchmark-based results indicate mixed but competitive forecasting performance relative to persistence-based and econometric alternatives. These findings are consistent with a regime-based interpretation of Bitcoin volatility and highlight the explanatory and predictive relevance of an integrated behavioural-network-uncertainty architecture. Full article

This study examines whether investor sentiment drives volatility spillovers between socially responsible and traditional mutual funds. The rapid growth of responsible investing in emerging markets raises questions about whether higher costs deliver improved risk or diversification benefits, particularly in volatile, behaviourally driven settings. Using a sentiment-augmented Diebold–Yilmaz connectedness framework, a composite sentiment index is constructed from global and local indicators. The results show that spillovers are time-varying and regime-dependent. During periods of stress and pessimism, responsible funds act as net transmitters of volatility, while traditional funds absorb shocks. In bullish conditions, volatility transmission weakens. Overall, connectedness shifts across market states, and socially responsible funds do not consistently provide stabilising or diversification benefits, as these depend on prevailing sentiment and risk conditions. This study provides new evidence on how sentiment-driven volatility spillovers are transmitted between socially responsible and traditional funds in South Africa, with implications for systemic risk and ESG investment costs. Full article

This study examines the role of investor sentiment in asset pricing within a frontier market, focusing on Vietnam. Using a comprehensive dataset covering the period 2015–2025 with 4018 observations, sentiment indices are constructed from both market-based and survey-based indicators. The study employs a quantile causality approach and a Quantile Vector Autoregression (QVAR) model to capture nonlinear, asymmetric, and state-dependent relationships among investor sentiment, stock returns, and market volatility. The empirical results provide several important findings. First, investor sentiment significantly influences stock returns, with stronger effects observed at extreme quantiles corresponding to bearish and bullish market conditions. Second, the impact is heterogeneous across firm sizes, with small-cap stocks exhibiting greater sensitivity to sentiment fluctuations. Third, the impact of investor sentiment on volatility is proxy-dependent and state-dependent. The market-based sentiment measure is generally associated with lower volatility at middle and upper quantiles, whereas the survey-based sentiment proxy shows stronger effects at lower quantiles, particularly during distress periods. Finally, robust bidirectional causality is identified between sentiment and market variables, suggesting the presence of feedback mechanisms between investor behavior and market performance. These findings highlight the importance of behavioral factors in shaping market dynamics in frontier markets characterized by high retail participation and limits to arbitrage. The study contributes to the literature by providing new quantile-based evidence on the nonlinear and asymmetric effects of investor sentiment in Vietnam. Full article

Market indices serve as a benchmark for performance comparison, guide asset allocation decisions, and reflect overall market sentiment and economic conditions, thereby influencing investment strategies by representing a segment of the market. Unquestionably, investor sentiment impacts price movement. In this paper, the objectives were to study the effectiveness of the Money Flow Index (MFI) in enhancing the performance of predictive analysis by capturing market psychology, developing an investment strategy, and analyzing the performance of the method mentioned. This study applies machine learning algorithms with technical indicators and optimizes portfolio allocation based on three notable market indices in Southeast Asia (SEA): SET50 in Thailand, STI in Singapore, and VN30 in Vietnam. Firstly, we combined technical indicators with machine learning—Support Vector Classifier (SVC), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—by comparing datasets with and without MFI over the period from 2013 to 2023. The results showed that XGBoost with MFI delivered the best predictive performance across three indices. These findings indicate that MFI significantly enhances prediction accuracy, even during volatile market conditions (COVID-19). Additionally, the predictions were integrated into the Markowitz Mean-Variance (MV) model to construct an optimal portfolio, which was then benchmarked against an equal-weight portfolio (1/N). Ultimately, the findings demonstrate that incorporating the machine learning predictions into the MV framework efficiently generates wealth. Full article

The green innovation bubble refers to the phenomenon of a “decoupling between patent quantity and quality” that may arise as firms respond to climate risks, posing a potential threat to the effectiveness of green innovation and sustainable development. Based on data from Chinese A-share listed companies from 2015 to 2023, this study examines the impact of climate risk on corporate green innovation bubbles, as well as the underlying transmission mechanisms and boundary conditions, from the perspective of strategic response. The findings indicate that there is a significant positive association between climate risk and the corporate green innovation bubble. Mechanism tests reveal that this effect operates primarily through three mediating channels: increased attention from green investors, amplified ESG rating divergence, and greater analyst coverage. These factors collectively incentivize firms to engage in “strategic green innovation” in response to external pressures. Heterogeneity analysis shows that the effect of climate risk on the green innovation bubble is more pronounced among small and medium-sized enterprises, firms with relatively optimistic investor sentiment, and firms with stronger ESG performance. Moderation analysis further demonstrates that robust internal controls can effectively mitigate the aggravating effect of climate risk on the green innovation bubble. This study uncovers the formation mechanism underlying the coexistence of “quantity expansion” and “quality lag” in corporate green innovation under climate risk. It provides both theoretical and empirical evidence for identifying and addressing innovation bubbles during the green transition, offering policy insights for improving green innovation incentive mechanisms and reducing greenwashing risks. Full article

In the context of political and financial market turmoil, effectively forecasting financial market trends is crucial for investment decisions. Large language models (LLMs) have been applied in extant research to predict market trends, analyze investor sentiments and interpret financial news, all aiming to help investment decision making. However, LLMs face limitations due to training data heterogeneity, restricting multidimensional perspectives and hindering comparative analysis for optimization. This study proposes a “Dual-Agent LLM Debate Mechanism” framework using a Proponent (LLM1: Gemini Pro 3) and an Opponent (LLM2: ChatGPT 5.2) to address single-LLM forecasting gaps: The Proponent generates a baseline forecast (F1) from an Integrated Context, while the Opponent validates and resolves conflicts with the Proponent via up to three rounds of cross-debate to produce a consensus forecast (F2). A controlled experiment was conducted to analyze 75 financial market indicators (FMIs) across five asset categories, revealing that F2 outperforms F1 in accuracy and directional stability, particularly in highly volatile assets like Cryptocurrencies and 10-Year Government Bonds. Paired-sample t-tests confirmed statistical significance, validating the mechanism’s effectiveness. Our study results demonstrate how cross-debate between LLMs enhances forecasting accuracy through structured optimization. Full article