ABSTRACT This study investigates how cognitive biases influence Environmental, Social, and Governance (ESG) investment decisions in China's policy‐driven financial markets. Analyzing 2748 fund observations (2018–2023) and original behavioral survey data from 500 fund managers, we develop a novel ESG allocation deviation index to quantify systematic deviations in ESG portfolio positioning from market‐neutral benchmarks. Employing panel GARCH, quantile regression, and mixed‐effects models, we find that: (1) policy‐driven herding significantly amplifies short‐term fund return volatility, with the effect being more pronounced in bear markets; (2) ESG allocation deviation exhibits an inverted‐U relationship with fund returns, suggesting diminishing marginal benefits beyond an optimal level; (3) negative ESG shocks generate 1.88 times greater volatility than positive shocks, consistent with loss aversion; (4) manager characteristics—particularly gender, education, and experience—significantly moderate ESG‐related biases. By integrating archival fund‐level evidence with survey‐based behavioral evidence within a unified framework, this study advances the understanding of how institutionally conditioned cognitive biases shape ESG investment outcomes in emerging markets.

Endogenous dispersion and volatility in stock returns

The stock market undergoes changes due to macroeconomic factors. One possible effect is changes in market volatility and changes in returns across assets. This paper assesses the changes in stock returns and changes in market volatility for companies in the CSI 300 Index. The analysis shows positive relationship between the changes in stock returns and market volatility, and this conclusion is true regardless of time. High market volatility increases risk, which leads to uncertainty. As a result, investors expect higher returns to compensate for the risk. Investor sentiment may also be affected by this risk, which in turn, further impacts stock prices and returns. Stock market economists and theorists generally agree on the risk and return trade off. The results of this paper will be added to the literature on the risk and return trade-off of the CSI 300. Investors will have the opportunity to optimize their investment, and policymakers will be able to minimize risk better. In simple terms, both investors and policymakers will have better results due to clear understanding of the market volatility.

We propose methods to improve the forecasts from generalized autoregressive score (GAS) models (Creal et al., 2013; Harvey, 2013) by localizing their parameters using decision trees and random forests, which exploit information in state variables from within and possibly beyond the model. The proposed methods allow the researcher to draw on information from multiple state variables simultaneously and avoid the curse of dimensionality faced by kernel-based approaches. We apply the new models in four distinct empirical analyses, and in all applications the proposed new methods significantly outperform the baseline GAS model. In our applications to stock return volatility and density prediction, the optimal GAS tree model reveals a leverage effect and a variance risk premium effect. Our study of stock-bond dependence finds evidence of a flight-to-quality effect in the optimal GAS forest forecasts, while our analysis of high-frequency trade durations uncovers a volume-volatility effect.

The growing emphasis on environmental sustainability and occupant well-being has made sustainable retrofitting a critical strategy in real estate sustainability. This study investigates whether sustainable retrofits deliver financial value for investors by examining their capital market implications in U.S. Real Estate Investment Trusts (REITs). Using quarterly data from 1994Q1 to 2022Q1, our baseline results show that a higher share of sustainable retrofit permits significantly reduces return volatility, indicating a risk-mitigating benefit valued by investors. Further analysis reveals that following the Paris Agreement, and during periods of regulatory risk exposure, capital markets respond to sustainable retrofit activities with both higher returns and lower volatility. These findings suggest that investor beliefs − shaped by awareness of sustainability and regulatory risks − are the primary mechanism driving the observed performance effects. Additionally, we find that sustainable retrofits mitigate the negative impact of market illiquidity on REIT volatility, highlighting their role in enhancing investor confidence. By providing the first empirical evidence on the investor-relevant benefits of sustainable retrofits, this study contributes to the literature on sustainable real estate investments and offers practical guidance for investors, managers, and policymakers aiming to promote environmentally and socially responsible practices within the sector.

The heating of the Palestine–Israel conflict in October 2023 triggered boycott movements targeting companies that support Israel and generated concerns regarding the financial market stability. This study examines the impact of boycott movements and macroeconomic factors on stock return volatility in Indonesia. Using time-series data from January 2021 to April 2025, the analysis compares the conventional index represented by the IDX Composite (IHSG) and the Islamic index represented by the Indonesia Sharia Stock Index (ISSI). Volatility dynamics are modelled using the ARCH/GARCH, while short-run and long-run relationships are analyzed using the ARDL approach. The results show that the optimal volatility of Islamic stocks exhibiting faster adjustment toward long-run equilibrium. The boycott movement significantly increases long-run volatility in the IHSG, whereas its effect on ISSI volatility is insignificant, indicating greater resilience of Islamic stocks. Inflation and industrial production reduce volatility, while exchange rate depreciation amplifies volatility in both indices. These findings contribute to the market volatility and Islamic finance literature by demonstrating that ethical screening and Sharia-compliant investment structures mitigate the transmission of boycott-induced geopolitical risk. The results offer practical implications for investors and regulators in strengthening Islamic capital markets as a stabilizing mechanism during periods of socio-political uncertainty.

This paper examines whether sustainability-oriented equity investing in India is financially competitive and resilient compared to conventional benchmark investing. The study focuses on two major indices of the National Stock Exchange: the NIFTY 50, representing the broad large-cap market, and the NIFTY100 ESG Index, representing an environmental, social and governance (ESG) screened portfolio. Using monthly data from February 2018 to December 2024, the analysis evaluates return behaviour, volatility and risk-adjusted performance over the full sample and three sub-periods: the pre-COVID phase (2018–2019), the COVID-19 crisis year (2020) and the post-pandemic recovery period (2021–2024). Simple and logarithmic returns are computed from month-end index levels. The study employs descriptive statistics, paired-samples t-tests, variance comparison, Sharpe-style ratios and a CAPM-type regression of ESG returns on NIFTY 50 returns. Over the full period, the NIFTY100 ESG Index exhibits a slightly higher average monthly return (around 1.14%) compared to NIFTY 50 (around 1.05%), with almost identical volatility (about 5% per month). Annualised geometric returns are estimated at approximately 12.78% for the ESG index and 11.66% for NIFTY 50, with similar annual volatility. However, the difference in mean monthly returns is not statistically significant at the conventional 5% level (p ≈ 0.41). During the COVID shock year, the ESG index records higher average monthly returns (about 2.08% versus 1.66%) and slightly lower volatility than NIFTY 50, with the return difference approaching significance at the 10% level (p ≈ 0.084). A regression of ESG log returns on NIFTY 50 log returns yields a beta close to one (β ≈ 0.98) and a small, statistically insignificant positive alpha (around 0.10% per month), indicating a high degree of co-movement with the benchmark index and suggesting that ESG screening does not materially alter systematic market exposure. Overall, the results indicate that ESG investing in India does not involve a performance penalty and may offer modest resilience benefits during periods of market stress. The findings also indicate that ESG-related information may not yet be fully incorporated into asset pricing in the Indian equity market. The findings have implications for investors, corporate decision makers and regulators seeking to foster sustainable and robust capital markets.

Earnings calls (ECs) represent a critical corporate disclosure channel that simultaneously conveys explicit textual content and implicit acoustic signals carrying distinct informational value for financial markets. This paper presents a comprehensive review of methodologies that fuse audio and text features from ECs to enhance market sentiment prediction. We survey the progression from unimodal approaches grounded in natural language processing (NLP) or acoustic modeling to state-of-the-art multimodal architectures that jointly leverage transcribed language and raw speech representations. The emergence of large language models (LLMs) such as FinBERT and GPT-based systems, combined with deep learning (DL)-driven automatic speech recognition (ASR) frameworks including wav2vec 2.0 and HuBERT, has substantially elevated the representational capacity available for this prediction task. Cross-attention fusion mechanisms, late fusion strategies, and gated multimodal units that align textual and prosodic representations are critically examined. Empirical evidence from reviewed studies demonstrates that multimodal fusion consistently outperforms unimodal baselines, yielding relative improvements in directional stock return accuracy and volatility forecasting of up to 15 percentage points. Open challenges including data scarcity, speaker diarization errors, and acoustic-transcript temporal misalignment are discussed alongside promising future research directions. This review offers a structured synthesis of the field and identifies the architectural and data infrastructural prerequisites for production-grade multimodal financial sentiment systems.

Abstract This paper examines the impact of ESG rating launches on corporate climate transition risk exposure using data from Chinese A-share listed companies (2012–2022) and the ESG rating events of SynTaoGF as a quasi-natural experiment. The findings reveal that ESG rating issuance significantly mitigates corporate climate transition risk exposure, with more pronounced effects for non-state-owned corporates, corporate with higher institutional ownership, corporates with a higher proportion of board members with environmental backgrounds, those in heavily polluting industries, and those subject to stricter environmental regulations. Mechanism analysis indicates that ESG rating reduce corporate climate transition risk exposure via three channels: optimizing internal strategies, stimulating green practices, and enhancing external supervision. Further analysis reveals that ESG rating exert a spillover effect in mitigating corporate climate transition risk exposure, which is further amplified by industry competition. Additionally, the study reveals that ESG ratings generate positive economic outcomes by enhancing corporate value, improving stock liquidity, and reducing return volatility. This study offers valuable insights into addressing climate challenges and demonstrates the effectiveness of ESG market-based oversight.

The volatility of cryptocurrency markets has increased substantially in recent years, particularly for Ethereum (ETH), which exhibits fat-tailed distributions and persistent volatility clustering that traditional linear models are unable to capture. This study aims to analyze and model the volatility of ETH/USD using high-frequency hourly data to determine the most appropriate volatility model for describing Ethereum’s intraday market dynamics. The dataset consists of 8,760 hourly closing prices from October 31, 2024 to October 31, 2025, obtained through the CryptoCompare API. The methodological framework includes data preprocessing, log-return transformation, stationarity analysis using the Augmented Dickey–Fuller test, detection of heteroskedasticity via the ARCH–LM test, and estimation of several ARCH and GARCH model specifications. The results show that ETH/USD returns are stationary, non-normally distributed, and exhibit clear volatility clustering. Among the ARCH models, only ARCH(1) adequately captures short-term fluctuations, while ARCH(2) provides no additional benefit. In contrast, GARCH models demonstrate superior performance in capturing both short-term shocks and long-term persistence. Based on AIC, BIC, and log-likelihood values, GARCH(1,2) emerges as the best-performing model, offering the highest flexibility in representing Ethereum’s persistent and reactive volatility patterns. These findings confirm that ETH/USD volatility is predictable and can be modeled statistically. Future research may incorporate asymmetric GARCH extensions or external explanatory variables to improve predictive performance.

[Purpose] While firms must take on a certain level of risk to achieve long－term growthand sustain competitive advantage, excessive risk－taking can increase the cost of capital,weaken financial stability, and heighten the likelihood of bankruptcy. This study focuses onmajor shareholders’ pledging of company stock, a financing mechanism that has recentlydrawn significant attention in corporate governance research, and examines how such pledgingbehavior influences corporate risk－taking. [Methodology] The sample consists of 12,888 firm－year observations from companies listedon the KOSPI and KOSDAQ between 2010 and 2018. The independent variables are definedas the presence of share pledging by major shareholders and the pledge ratio, while the dependentvariable, corporate risk－taking, is measured by the volatility of returns adjusted for the industryaverage. Using multiple regression analysis, this study examines the effect of major shareholders’share pledging on firms’ risk－taking behavior in the Korean capital market. [Findings] The findings reveal that firms with major shareholders who pledge shares, orwith higher pledge ratios, show a significant increase in corporate risk－taking. This suggeststhat major shareholders’ pursuit of personal liquidity affects firms’ strategic investmentdecisions by inducing more aggressive policies. However, the effect is mitigated in firms withstronger governance mechanisms, highlighting the moderating role of institutional safeguards. [Implications] This study identifies stock pledging not merely as a private financing tool butas a potential corporate risk factor that directly influences firm－level decision－making. Thisstudy contributes to the literature by identifying share pledges as a hidden risk factor that affectscorporate decision－making and by providing policy implications for enhancing disclosurerequirements and regulatory oversight to protect minority shareholders and creditors.

Abstract The common belief that real estate investments are an inflation hedge has been repeatedly challenged by scientific research. It was found that this issue requires in-depth research. Individual researchers have expanded their research to include various specific themes. However, there has been no research focused on the empirical relationship between the inflation rate and the real return on housing market investments. This study aimed to examine this relationship in detail within the framework of the five research questions formulated. Therefore, the article addresses this issue, considering different investment horizons (from 1 to 15 years) and 16 local housing markets in Poland. The research period covered was from 3Q 2006 to 4Q 2024. Based on hedonic price indices of 1m², the moving observation window method was used to obtain time series of real returns on local markets. Then, linear correlation and determination coefficients were calculated, and appropriate statistical tests were applied. It was found that: 1) the examined relationship was heterogenous, and dependent of the investment horizonas well as inflation regime; 2) the extent to which the CPI explained real return volatility was diverse; 3) it is not possible to identify a threshold (maximum) CPI for which real return remained non-negative; 4) for most investment horizons, the median CPI in periods when real return was positive did not differ significantly from the median CPI in periods when real return was negative; 5) for most local markets, the average real return in a high-inflation regime was significantly higher than in a low-inflation regime.

Type of the article: Research ArticleAbstractForeign exchange markets have intrigued not only corporations engaged in export and import, but also individuals and other entities seeking to achieve decent risk-adjusted returns and protect themselves from future currency exchange rate exposure. Hence, researchers are drawn to examine the volatility of returns and identify diversification and hedging opportunities to mitigate country and financial risks of the five largest trading currencies with respect to the Indian currency, the rupee. The study used historical daily exchange rate data for the Indian currency with respect to American dollar, euro, British pound, Japanese yen, and Australian dollar, spanning from January 1, 2008 to December 31, 2025.American dollar has the highest average daily return among the five currencies, followed closely by euro and pound. Pound exhibits the highest standard deviation, and its volatility suggests greater uncertainty for investors dealing in these transactions. High correlations between dollar-euro and euro-pound indicate that they are influenced by similar economic factors or market sentiments. Frequent structural breaks highlight the possibility for currency exchange rates to shift dramatically due to unforeseen events. This is a crucial insight for risk management, as it signals the need for dynamic hedging strategies that can adapt to sudden changes in market conditions. Investors and policymakers can leverage these findings to optimize currency portfolios and reduce financial risk, especially when seeking diversification benefits and long-term stability amidst global market shifts.

This study investigates the probability of consumer default across both secured and unsecured assets, with a particular focus on borrower behavior and the role of moral hazard in shaping individual credit risk. It examines how different borrower decisions, such as investing in secured and unsecured projects after loan disbursement, affect default outcomes, especially under limited lender supervision. The Ornstein–Uhlenbeck process is used to capture the dynamics of risky asset returns and identifies the conditions under which borrowers are likely to switch from safer to riskier investments. We assume that borrowers may allocate loan funds to both secured and unsecured projects, thereby recognizing that credit risk assessment inherently involves behavioral factors that are difficult to quantify. Monte Carlo simulations are used to assess how return volatility influences borrower decision-making, showing that higher uncertainty increases the probability of returns exceeding the repayment obligation, thereby incentivizing risk-shifting behavior. The results indicate that unsecured lending is more exposed to strategic risk shifting and experiences more frequent and severe default outcomes than secured lending. As a result, this study recommends that microfinance institutions prioritize collateral-backed lending as a more effective strategy for mitigating credit risk and reducing exposure to borrower opportunism.

This paper examines the cross-sectional distribution of equity returns for service-oriented firms in the hospitality sector, focusing on the role of trading on equity (leverage) in their capital structures. Fama-MacBeth regressions were applied across four sub-industry portfolios sorted by book-to-market equity ( BE it /ME it ) to assess the relationship between return volatility and a range of leverage proxies across different time periods and capital structure configurations. Additionally, the study ranks the most and least volatile sub-sector portfolios within the sample. Some portfolios exhibited deviations from the predictions of pecking order theory, while others aligned with theoretical expectations regarding levered equity returns and financial leverage. Overall, the analysis reveals that BE it /ME it firms experience substantial return volatility across most portfolio sorts, and that, on average, higher levels of short-term leverage are associated with an elevated risk–return tradeoff.

Accurate market risk measurement is a crucial aspect of stock portfolio management, particularly in volatile market conditions. One commonly used method for measuring market risk is Value-at-Risk (VaR). However, the conventional VaR approach often fails to capture the dynamics of volatile volatility. Therefore, this study aims to measure stock market risk using a GARCH-based Value-at-Risk approach and test the model's reliability using the Kupiec Proportion of Failures Test. The data used are daily stock price data processed into logarithmic returns. Return volatility is estimated using the GARCH(1,1) model, and the VaR value is calculated based on conditional volatility at a 5 percent significance level. VaR backtesting is then performed to identify violations and evaluate the model's validity using the Kupiec Test. The results of the study show that out of 653 observations, there were 27 VaR violations, with a Kupiec statistic value of 1.0909 and a p-value of 0.2963. A p-value greater than the significance level indicates that the VaR–GARCH model is statistically valid and able to measure market risk well. This study concludes that the VaR–GARCH approach is a reliable method in measuring stock market risk and can be used as a supporting tool in investment decision-making and risk management.

Do shifts into high stock-market volatility foreshadow recessions rather than merely accompany them? Prior work shows volatility rises in downturns and can help shorthorizon forecasts, but the timing of discrete volatility regime changes relative to business-cycle turning points is less understood. Using quarterly data for the United States, United Kingdom, Japan, Germany, Italy, and France (1960–2019; country-specific start dates), we estimate a bivariate Markov-switching model that jointly classifies high/low output growth and high/low return volatility, and tests restrictions on the transition structure. In the United States, United Kingdom, Japan, and France, entry into the high-volatility state typically precedes recession onset by one to two quarters. For Germany and Italy, the output and volatility state processes are approximately independent. These results suggest that volatility-regime switches are a mediumhorizon early-warning signal, consistent with uncertainty and risk-premium repricing that tighten funding conditions in more market-based financial systems. • Volatility regime shifts often precede recessions in four of six economies. • A bivariate Markov-switching model jointly tracks growth and volatility regimes. • Likelihood ratio tests compare independence, volatility-leads, and growth-leads. • Germany and Italy show near-independence between output and volatility states. • Volatility switches offer a medium-horizon early-warning signal for downturns.

The Efficient Market Hypothesis (EMH) posits that asset prices adjust rapidly to new information, leaving no scope for systematic excess returns. The COVID-19 pandemic represented an unprecedented global shock, generating substantial volatility across financial markets, including those in emerging African economies. This study examines the volatility dynamics and informational efficiency of the Nigerian Stock Exchange (NGX) All-Share Index before and after the COVID-19 lockdown. Using daily data from January 2018 to December 2022, the analysis applies GARCH (1,1) models under Gaussian, Student’s t, and Generalized Error Distribution assumptions to evaluate return dependence and volatility persistence across sub-periods. The empirical results confirm that returns are stationary in both pre- and post-lockdown phases. However, significant lagged return effects are observed across periods, indicating short-run return dependence inconsistent with strict weak-form efficiency. Volatility clustering is pronounced in the pre-lockdown period but weakens after the lockdown, with relatively rapid mean reversion of shocks in both phases. These findings suggest that the COVID-19 shock intensified volatility temporarily without fundamentally restructuring the informational dynamics of the Nigerian market. The study contributes to the emerging-market literature by distinguishing between volatility stabilisation and informational efficiency, demonstrating that reduced volatility persistence does not necessarily imply the elimination of return predictability. Strengthening market transparency and improving information dissemination remain essential for enhancing long-term market efficiency and resilience.

This study sought to assess the mediating effect of market liquidity risk on the relationship between systematic risks and stock market return volatility among firms listed at the NSE, Kenya. Volatility in the stock market in Kenya has been on the rise in the recent years. Further, research gaps exist in the literature in the Kenyan context which creates the need to undertake this research. The study was anchored on positivism philosophy supported by correlational research design. The target population was all 62 NSE listed companies listed between 2014 and 2024. Secondary data was gathered using record sheet. The data was gathered from NSE, KNBS, CMA and world bank reports. The data was analyzed through timeseries moderating multiple regression model. Further, descriptive statistics were utilized to show how the variable were. The analysis showed that on the effect of systematic risks on market liquidity, the lagged systematic variables showed statistically insignificant coefficients (p>0.05). Therefore, systematic risks had no significant mediating effect on market liquidity risk. After including market liquidity as a predictor of stock market volatility alongside systematic risks, lagged market liquidity risk yielded a negative but statistically insignificant coefficient (β= -0.0352, p = 0.459). Therefore, effect of the mediator on stock market return volatility was not significant. This showed that market liquidity risk had no mediating effect on the relationship between systematic risks and stock market return volatility. The study concludes that market liquidity risk had no significant mediating effect on the relationship between systematic risks and stock market return volatility of firms listed at the NSE Kenya.  The study recommends that the Capital Markets Authority (CMA) and NSE implement reforms to boost market depth and liquidity. The CMA and NSE should also prioritize broadening market participation through targeted investor education programs, which would help cultivate a more diverse and active investor base. Additionally, automating trade processes is strongly suggested as a means of improving execution efficiency and reducing friction in price discovery, particularly during periods of market stress. Keywords: Market liquidity risk, systematic risks, stock market return volatility, firms, Nairobi Securities Exchange, Kenya

The study assessed the moderating effects of capital inflows on the relationship between systematic risks and stock market return volatility among firms listed at the NSE, Kenya. Volatility in the stock market in Kenya has been on the rise in the recent years. Capital inflows can impact stock market volatility by affecting overall market liquidity and investor sentiment. Sudden changes in capital flows, such as large-scale foreign selling or buying, can exacerbate market volatility as prices adjust to accommodate the influx or outflow of funds. Empirical studies found conflicting findings and displayed research gaps that this study sought to fill. The study was anchored on positivism philosophy and correlational research design. The target population was all 62 NSE listed firms listed between 2014 and 2024. Secondary data was collected from NSE, KNBS, CMA and world bank reports using data collection sheet. The data was analyzed through descriptive statistics and multiple regression. The study found that individual interaction terms were insignificant, including inflation (β = -0.0172, p = 0.428), exchange rate (β = 0.0368, p = 0.306), and interest rate (β = -0.0215, p = 0.389). Hence, capital inflows had no significant moderating effect on the relationship between systematic risks and stock market return volatility. The study concludes that capital inflows have no significant moderating effect on the relationship between systematic risks and stock market return volatility of firms listed at the NSE Kenya. The study recommends that regulatory bodies such as the CMA and CBK develop policies that encourage productive and long-term capital inflows. The CMA and CBK should establish early warning mechanisms that monitor capital flow volatility and its potential spillover effects on equity market stability. Market regulators should also enhance investor education initiatives so that market participants are better equipped to respond rationally to changes in capital flow patterns, thereby reducing sentiment-driven volatility in the Kenyan stock market. Keywords: Capital inflows, systematic risks, stock market return volatility, Nairobi Securities Exchange, Kenya