Search Results (358)

Environmental, social, and governance (ESG) considerations have become an increasingly important component of sustainable finance, investment decision-making, and banking regulation. As financial institutions face growing pressure to integrate sustainability objectives into their business models, understanding how sustainability performance relates to market valuation has become an important issue for investors, regulators, and bank management. Despite the growing ESG literature, evidence regarding the valuation relevance of individual ESG dimensions remains limited, particularly in the European banking sector. This study examines whether ESG dimensions are uniformly associated with the market valuation of European banks or whether financial markets differentiate among individual ESG pillars. Using a panel dataset of European banks covering 2021–2024 and Bloomberg ESG indicators, the study estimates panel econometric models to evaluate the associations between disaggregated ESG pillars and market-based valuation measures. The empirical findings reveal substantial heterogeneity across ESG dimensions. The social pillar is positively associated with market valuation, whereas the environmental pillar is negatively associated, while governance exhibits weak or statistically insignificant associations. The findings remain robust across several alternative model specifications. The results indicate that investors in highly regulated European banking markets differentiate between ESG dimensions, suggesting that financial markets differentiate among ESG dimensions and that analysing ESG at the pillar level provides a more nuanced understanding of market valuation than aggregate ESG measures. Full article

Machine learning technologies are increasingly embedded in financial decision-making processes, yet their influence on investor behavior and market efficiency remains insufficiently understood. This study investigates how machine learning-assisted investment decisions affect investor behavioral biases and asset pricing efficiency and whether these effects exhibit nonlinear characteristics. Using investor-level trading records, survey data, and market data from the Chinese A-share market (N = 12,846 investors; 3876 questionnaires; 3 million+ transactions), we construct measures of machine learning adoption intensity, investor behavioral biases, and asset pricing efficiency. Employing fixed-effects models, instrumental-variable estimation (2SLS), and mediation analysis, we examine the behavioral and market consequences of machine learning adoption. The results reveal a significant U-shaped relationship between machine learning adoption intensity and investor behavioral biases (inflection point: AIDI* = 0.731), and an inverted U-shaped relationship between AI market penetration and asset pricing efficiency (threshold: AIPM* = 0.733). Investor behavioral bias mediates 26.34% of the total effect of AI adoption on pricing efficiency. Moderate adoption reduces behavioral biases by improving information processing and decision quality, whereas excessive reliance on algorithmic recommendations generates automation bias and weakens investors’ independent judgment. At the market level, machine learning adoption exhibits an inverted U-shaped relationship with asset pricing efficiency. While moderate adoption enhances information incorporation into prices and reduces pricing deviations, excessive market penetration may induce algorithmic homogeneity and diminish efficiency gains. Furthermore, investor behavioral bias serves as an important transmission mechanism linking machine learning adoption to asset pricing outcomes. Heterogeneity analyses indicate that institutional investors benefit more from machine learning tools than individual investors, and the effects are stronger during periods of high market uncertainty. These findings provide new evidence on the optimal adoption of machine learning in financial markets and offer practical implications for intelligent investment platforms, investor education, and financial regulation. Full article

This study examines the relationship between ESG performance and financial performance and resilience in the banking sector, using a sample of systemically important banking institutions in Romania and Poland for the period 2020–2024. The study aims to assess the extent to which ESG performance contributes to improving financial performance and strengthening the financial resilience of banking institutions operating in these two emerging economies in Central and Eastern Europe. The research employs an empirical framework based on correlation analysis, panel regression models (Fixed Effects and Random Effects), selected on the basis of the Hausman test, with robust standard errors, as well as a robustness analysis using ESG variables lagged by one year. Financial performance is assessed using the Return on Assets (ROA) and Return on Equity (ROE) indicators, whilst financial resilience is analysed using the Capital Adequacy Ratio (CAR), Liquidity Coverage Ratio (LCR), Non-Performing Loans (NPLs) and Cost of Risk (CoR). ESG performance is examined both through the aggregate ESG score and through its individual environmental, social and governance components. The results highlight that ESG performance does not show statistically significant associations with traditional indicators of financial performance. Instead, the analysis reveals differentiated associations between the ESG components and indicators of financial resilience, with the social dimension being associated with credit risk indicators (NPL and CoR), whilst the environmental and governance components do not show significant effects in the estimated models. The study’s contribution lies in the simultaneous analysis of financial performance and financial resilience using a panel framework applied to banks in Romania and Poland, as well as in highlighting the heterogeneous nature of the relationship between ESG components and the various dimensions of financial resilience. The results complement the literature on the banking sector in Central and Eastern Europe and offer relevant implications for banking institutions, investors and regulators, without implying causal relationships between the variables analysed. Full article

Background: This study examines how four canonical behavioral biases (overconfidence, herding, anchoring, and loss aversion) influence investor decision-making in the Saudi stock market (Tadawul), and whether overconfidence and loss aversion operate as moderating forces on herding and anchoring, respectively. Methods: Employing a quantitative, cross-sectional design with a stratified sample of 384 retail investors, the study applies Partial Least Squares Structural Equation Modelling (PLS-SEM) to test six hypotheses derived from Modern Portfolio Theory and Behavioral Finance frameworks. The measurement model satisfies established thresholds for reliability, convergent validity, and discriminant validity. Results: Results confirm that loss aversion is the dominant predictor of behaviorally influenced decision-making (β = 0.402, p < 0.001, f² = 0.188), followed by herding (β = 0.234, p < 0.001) and overconfidence (β = 0.164, p = 0.001), while anchoring does not exert a statistically significant independent effect (β = 0.102, p = 0.084 one-tailed, p = 0.168 two-tailed). Neither the overconfidence × herding (β = 0.005, p = 0.920, two-tailed) nor the loss aversion × anchoring (β = −0.039, p = 0.330, two-tailed) interaction terms reach significance, indicating that these bias pairs operate as independent additive forces rather than compounding systems. The model explains 55.7% of the variance in investor decision-making (R² = 0.557). Conclusion: The findings advance behavioral finance theory in GCC and Islamic equity markets by (1) demonstrating non-equivalence of anchoring effects relative to Western-market benchmarks, (2) resolving competing theoretical predictions about bias interaction effects, and (3) providing context-specific evidence that loss aversion subsumes anchoring cognition in the Saudi market. Practical implications for the Capital Market Authority, financial educators, and individual investors are discussed and contextualized within the Saudi market setting. Full article

This study examines the determinants of investment decision quality among individual investors in the United Arab Emirates (UAE) financial market. As digital investment platforms continue to expand access to financial information and investment opportunities, understanding the drivers of effective investment decisions has become increasingly important for investors. Integrating insights from financial literacy, behavioral finance, trust and disclosure, and technology acceptance perspectives, the study develops a unified framework to examine how financial literacy and awareness, trust, transparency and disclosure, as well as digital platform support jointly shape the quality of investment decisions. Drawing on survey data from 208 individual investors with varying levels of investment experience, the study employs correlation and multiple regression analyses to examine the proposed relationships. The results reveal positive bivariate associations between investment decision quality and all three explanatory factors. In the multivariate model, however, only financial literacy and awareness and digital platform support remain statistically significant, with digital platform support emerging as exerting the largest statistically significant effect on decision quality. The independent effect of trust, transparency, and disclosure attenuates to non-significance, suggesting that its effect largely overlaps with financial literacy and digital platform support rather than representing a distinct independent contribution. The findings indicate that investor outcomes depend not only on access to information but also on investors’ ability to understand, interpret, and apply that information through effective digital support mechanisms. The study contributes to the investment decision-making literature by providing an integrated empirical framework and new evidence from the UAE as an underexplored market context. The findings provide indicative implications for investor education initiatives, financial institutions, and digital platform providers within the sampled context of an increasingly digital financial environment. Full article

Inflation expectations are central to monetary policy transmission, yet relatively little is known about whether individuals’ own investment experiences are associated with how they form such expectations across different forecast horizons. This study examines the association between self-reported past investment performance and horizon-specific expected cumulative consumer price changes at the one-, three-, and five-year horizons using a large-scale online survey of 157,523 active Japanese investors. Because the survey asks respondents how consumer prices will change over each horizon, the three- and five-year responses are interpreted as expected cumulative price changes rather than annualized inflation rates. Ordered probit models are estimated while controlling for demographic, socioeconomic, and behavioral characteristics. The results show a horizon-dependent conditional association: self-reported investment performance is not significantly associated with one-year expectations in the full specification, whereas it is positively and significantly associated with three- and five-year expectations. Formal stacked OLS interaction tests indicate that the association differs significantly across horizons, and additional threshold-specific probit models show that the pattern is most evident for moderate inflation-expectation thresholds. The economic magnitudes are statistically precise but modest. Heterogeneity analyses further suggest that the association is weaker among respondents with higher financial literacy and higher assets, and stronger among respondents with a more myopic view of the future. Because the analysis relies on cross-sectional observational data and subjective performance measures, the findings should be interpreted as conditional associations rather than causal effects. Full article

This study investigates the dynamic interactions between Bitcoin, global financial indicators, and the Kazakhstan Stock Exchange (KASE) index within a VAR-based econometric framework, addressing a notable gap in the literature on emerging and shallow financial markets. While prior research predominantly focuses on developed economies, evidence suggests that cryptocurrency–stock market linkages are time-varying, crisis-sensitive, and often asymmetric. In this context, the present study examines both short-term causality structures and shock transmission mechanisms among KASE, Bitcoin (BTC), oil prices, the U.S. dollar index (DXY), and the VIX using monthly data for the period 2017M01–2026M04. Empirical findings indicate that, despite the absence of statistically significant Granger causality from individual global variables to KASE, the joint dynamics suggest a non-negligible, albeit indirect, interaction structure. Variance decomposition and impulse-response analyses further reveal that KASE dynamics are predominantly driven by its own shocks, reflecting the relatively segmented and internally driven nature of the market. Diagnostic tests confirm the robustness of the model, with no evidence of serial correlation or heteroskedasticity in residuals. These findings are consistent with the structural characteristics of the Kazakh financial system, including limited market depth, lower investor participation, and high sensitivity to domestic macroeconomic conditions. Unlike developed markets where stronger integration is observed, KASE appears only weakly connected to global financial and cryptocurrency markets. The study contributes to the literature by providing empirical evidence from a frontier market and highlights the importance of considering country-specific structural factors when evaluating financial integration. Policy implications emphasize the need to enhance market depth, transparency, and investor confidence to strengthen the responsiveness of KASE to global financial developments. Full article

This study examines the impact of corporate environmental, social, and governance (ESG) practices on corporate stock prices, with a particular focus on the mediating role of board gender diversity (BGD). Using a dataset of 9543 firm-year observations from non-financial companies across 15 countries in the S&P 1200 global index between 2012 and 2020, the analysis evaluates ESG performance through the Refinitiv ESG Combined Score, which incorporates disclosures across ESG pillars and an overlay for ESG controversies. BGD is measured as the proportion of female directors on corporate boards, while stock prices are assessed using annual closing prices. The findings reveal a positive relationship between ESG performance and corporate stock prices, both at the aggregate level and across individual ESG pillars. Additionally, BGD is shown to enhance stock price performance and serves as a mediator in the ESG-stock price relationship. These results highlight the critical role of board diversity in amplifying the financial benefits of ESG practices. Further analysis suggests that the value relevance of ESG performance varies across institutional settings, with stronger effects observed in emerging/offshore markets and in the North American and European regions. The study offers important implications for companies, investors, and policymakers, emphasizing the need to integrate ESG strategies and promote gender diversity at the board level to enhance corporate valuation and long-term sustainability. Full article

The relationship between financial literacy and capital market participation remains a central focus of both theoretical and empirical research in behavioral finance. However, existing research has predominantly relied on direct-effect, mediation, or moderation frameworks, thereby offering only a partial understanding of how individuals make investment decisions under uncertainty. To address this limitation, this study develops a sequential cognitive–affective framework by integrating the Theory of Planned Behavior (TPB) and the Risk-as-Feelings Hypothesis (RFH). Within this framework, investment intention is conceptualized as the outcome of cognitive evaluations and affective responses, with financial literacy influencing these processes by shaping perceived risk and institutional trust. Utilizing a multistage sampling strategy, survey data were collected from 449 individual investors and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that financial literacy is positively associated with investment intention, both directly and indirectly through a sequential mediation pathway. Specifically, higher financial literacy is associated with lower perceived risk, which subsequently strengthens trust in financial institutions and ultimately increases investment intention. These findings suggest that financial literacy functions not only as a cognitive resource but also as a psychological mechanism that influences how individuals interpret and respond to financial uncertainty. By validating a sequential cognition–affect pathway, this study provides a more comprehensive behavioral explanation for the inconsistent findings reported in prior research. The findings further suggest that financial literacy initiatives designed to address risk perceptions and institutional trust may be more effective in promoting capital market participation than programs focused solely on information provision. Full article

This study examines how investors’ perceptions of artificial intelligence (AI) are associated with investor behavior, focusing on short-term and long-term investment strategies and their implications for investor resilience. While prior research has primarily emphasized the technical capabilities of AI in financial decision making, less is known about how investors perceive these technologies in practice. Using survey data from 221 individual investors and partial least squares structural equation modeling (PLS-SEM), the study analyzes how different dimensions of AI perception affect investment strategies. The results show that perceived efficiency and positive expectations regarding the future role of AI positively influence both short-term and long-term investment strategies. In contrast, perceived accuracy is associated with lower engagement in short-term strategies, suggesting greater reliance on AI-generated recommendations. Most notably, perceived forecasting ability is negatively related to long-term investment strategies, indicating that stronger belief in AI’s predictive capabilities may encourage a shift toward shorter investment horizons. The findings demonstrate that different dimensions of AI perception are associated with investment behavior in different ways. While some AI-related perceptions may support more disciplined and potentially resilient investment behavior, others may encourage greater dependence on automated forecasts and reduced long-term orientation. The study contributes to understanding the behavioral implications of AI in financial decision making. Full article

With the global rise in sustainable investment concepts, environmental, social, and governance (ESG) factors have increasingly become important criteria influencing investment decisions. Although institutional investors are paying greater attention to corporate ESG performance, limited evidence exists regarding its impact within the Chinese A-share market. Using panel data from Chinese listed firms during the period 2010–2023, this study employs fixed-effects models with clustered standard errors as the baseline estimation method. To improve the robustness of the findings, Tobit regression, Logit regression, lagged-variable models, heterogeneity analysis, and Hausman tests are further conducted. The empirical findings indicate that the overall ESG score and the individual environmental (E), social (S), and governance (G) dimensions do not exhibit statistically significant effects on institutional ownership in the baseline fixed-effects regressions. The results suggest that ESG performance has not yet become a dominant determinant of institutional investment decisions in China’s capital market. However, the robustness tests based on Tobit and Logit models provide limited evidence that ESG performance may still influence institutional investor behavior under alternative empirical specifications. Furthermore, the heterogeneity analysis reveals that the relationship between ESG dimensions and institutional ownership differs across environmentally related and non-environmentally related firms, although the effects are generally weak and statistically limited. The study contributes to the ESG and institutional investment literature in three important ways. First, it provides updated evidence from the Chinese A-share market over the 2010–2023 period, reflecting the evolving stage of ESG development in emerging economies. Second, it comparatively examines the differentiated roles of environmental, social, and governance dimensions rather than relying solely on aggregated ESG indicators. Third, it highlights the limited and transitional nature of ESG integration among institutional investors in China, where traditional financial indicators continue to play a more important role in investment decisions. The findings provide important implications for policymakers, listed firms, and institutional investors seeking to promote sustainable finance development and improve the effectiveness of ESG disclosure practices in emerging markets. Full article

Flooding poses a significant threat to commercial real estate investment, disrupting business operations, escalating maintenance costs, and heightening investment uncertainty, particularly in coastal and low-lying urban environments. This study examines the social dimensions of climate vulnerability by investigating how flood risk shapes stakeholders’ decisions to invest in commercial properties within flood-prone urban areas, with a focus on Lekki Phase 1, Lagos, Nigeria. A quantitative survey design was adopted. Data were collected from 87 commercial property investors through a structured questionnaire (FIIFRZQ) measured on a four-point Likert-type scale. The instrument demonstrated acceptable overall internal consistency (Cronbach’s α = 0.72), with subscale α values ranging from 0.62 to 0.81. Multiple regression analysis was used to assess the joint and individual contributions of seven factor categories (environmental, legal, economic, neighbourhood, structural, locational and behavioural) to investors’ willingness to invest in commercial property that is at risk of flooding. The seven predictors collectively explained 61.2% of the variance in investment willingness (R² = 0.612; F(7, 79) = 17.91; p < 0.001). Five factors, namely legal, environmental, structural, economic, and locational, were statistically significant contributors to investment willingness, while neighbourhood and behavioural factors were not. Johnson’s relative weights analysis confirmed legal and environmental considerations as the dominant drivers. The findings illuminate the interplay between climate vulnerability and investor behaviour in urban real estate markets, with actionable implications for policymakers, real estate practitioners, and investors navigating decision-making in flood-exposed urban environments. Full article

Drawing on signaling theory, this study investigates the role of lead founders’ human capital signals in the success outcomes of equity crowdfunding (ECF) campaigns. While prior research emphasizes entrepreneurial teams or broadly defined founder characteristics, the role of dominant entrepreneurial actors remains underexplored. We focus on the lead founder, defined as the individual combining founder status, CEO authority, and ownership concentration, as the primary signal carrier in ECF contexts. Using a multi-platform dataset of 1067 campaigns from Republic Europe, Crowdcube, Mamacrowd, and Invesdor (2012–2024), we examine how lead founders’ education and experience shape investor decisions. Our results indicate that industry-related education is the strongest predictor of the number of investors. Furthermore, while industry experience alone can positively predict investor engagement, its role disappears once education is accounted for, suggesting that education in industry-related fields can outweigh industry experience in shaping investor perceptions. Additionally, our findings suggest that entrepreneurial experience and attendance at a top-ranked university do not contribute meaningfully to explaining investor participation. Accordingly, the study contributes to the human capital signaling literature by showing that investors evaluate the incremental informational value of human capital signals rather than assessing each signal independently, and highlights the centrality of the lead founder in decision-making under highly uncertain crowdfunding environments. 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

This study provides a comprehensive examination of the inflation-hedging potential of commodity indices and futures for U.S. investors using monthly data spanning July 1959 to December 2025 for 27 individual commodities, and January 1947 to November 2025 for 13 commodity indices. We employ multiple complementary methodologies, including optimal hedge ratios with Newey–West standard errors, asymmetric hedging analysis, long-horizon regressions, rolling window stability tests, Granger causality analysis, out-of-sample validation, and Markov-switching vector error correction models (MS-VECM). Our results reveal substantial heterogeneity in hedging effectiveness across commodity sectors. Energy commodities, particularly gasoline and crude oil, demonstrate the strongest inflation-hedging properties with higher hedge ratios and hedging effectiveness. Industrial metals, represented by copper, also provide reliable hedging with stable performance across market conditions. In contrast, precious metals, including gold and silver, show weak contemporaneous hedging ability despite their traditional safe-haven reputation, though they may offer protection during specific market regimes. Agricultural commodities and livestock exhibit minimal or negative hedging effectiveness. The MS-VECM analysis confirms that hedging relationships are time-varying, with effectiveness differing significantly between stable and turbulent market regimes. These findings have important implications for portfolio construction and risk management strategies. Full article