This study innovatively integrates signaling theory with information cue theory to examine the diagnosticity of various information cues in predicting equity crowdfunding success. Analyzing data from 144 campaigns on China's Dreammove platform (2014-2019) through five machine learning models, we find that quantitative information cues are most predictive of funding success. Narrative cues also hold diagnostic value, albeit to a lesser extent, while visual cues are limited in their predictive capacity. Employing Shapley Additive exPlanations (SHAP) interpretability, we identify larger entrepreneurial teams, ambitious funding targets, concise, accessible narratives, and modest visual elements like youthfulness and clarity enhance campaign appeal. This research advances the understanding of how investors process information in equity crowdfunding, offering new insights by moving beyond single-dimensional analyses and providing practical guidance for entrepreneurs and investors in emerging crowdfunding markets.

Environmental, Social, and Governance (ESG) practices are becoming central to firm evaluation, though their financial impact is debated. Investors use positive, negative, or combined screening to assess ESG criteria. Negative screening, rooted in religious practices, excludes sectors like tobacco, alcohol, and gambling but may limit options and underperform ‘sin’ stocks. Positive screening favors companies excelling in ESG, offering diverse strategies and potentially better risk-adjusted returns. This study analyzes investor preferences for ESG screening in U.S. mutual funds from 2002 to 2020. Funds with positive screening attract higher net flows overall, with returns similar across all screening types. Retail investors are less (more) sensitive to positive (negative) screening than institutional investors. Flows to funds with positive screening rise during bear markets. During the bear market, positive screening also outperforms combined screening in net flows, while negative and combined screenings show no significant differences.

This study utilizes account-level data from mutual fund investors to test the hedonic editing hypothesis through direct and indirect approaches. First, an empirical analysis of redemption behavior among Chinese mutual fund investors confirms the hedonic editing hypothesis's applicability in the Chinese market. Second, the study indirectly examines the impact of hedonic editing on investment decisions within a portfolio context, focusing on the disposition effect. Analysis reveals that while individual mutual fund investors exhibit a strong disposition effect, this tendency weakens when the overall portfolio is profitable or when other mutual funds are redeemed on the same day. These findings support the hypothesis that mutual fund investors adopt a broad decision-making framework and incorporate hedonic editing into their decision processes.

Our study, set against the backdrop of the ongoing Russia-Ukraine war, Hamas-Israel conflict and fintech advancements, investigates the implications of Bitcoin (BTC), Ethereum (ETH), and Tether (USDT)'s active addresses on BTC prices. In doing this, we employ a combination of traditional time series VAR model and machine learning BPNN model during the geopolitical conflict period. Our findings indicate that BTC and ETH prices often move in tandem ahead of geopolitical conflicts. Investors aiming to boost their income choose to buy lower-priced ETH, leading to a rise in the number of active ETH addresses, positively correlated with BTC's price. However, during geopolitical conflicts, this relationship shifts. The number of active USDT addresses is a significant factor influencing BTC price. Consequently, when confronted with a steep decline in BTC prices, investors tend to convert BTC into the USDT stablecoin to avert losses.

This paper examines the role of cognitive ability in protecting individuals from financial fraud. We analyze fraud that targets retirement savings in the Swedish public pension system, where several fund companies were expelled for misconduct. Some of these firms then faced criminal investigations that led to prison sentences. Investors in these fraudulent funds experienced both direct losses from the failure to recover misappropriated funds and indirect losses due to poor fund performance and high fees. Using administrative data on fund selections in the Swedish pension system, linked with cognitive ability test scores from military enlistment, we find a nearly linear, strongly negative relationship between cognitive ability and the likelihood of investing in these fraudulent firms. Verbal skills, in particular, play a critical role in protecting savers from fraud, and we ensure that our results are not confounded by a general propensity of low-ability individuals to end up in poorer-performing funds.

This paper models ambiguity as a second dimension of investor preferences, extending the CAPM framework to incorporate ambiguity aversion alongside risk aversion. We derive an asset pricing model under ambiguity that leads to the emergence of a size factor. Market simulations demonstrate that the size factor's impact contradicts its empirical characterization in the literature. This is because extreme ambiguity is associated with the equally weighted portfolio, such that larger deviations from this reference point command a greater premium. Additionally, ambiguity introduces heterogeneity in equity portfolio holdings, influencing the determination of the risk-free rate. The risk-free rate in our model can be endogenously determined through market equilibrium or remain exogenous if set by an external agency balancing aggregate demand to meet macroeconomic objectives beyond the model's scope.

Using a large proprietary mortgages dataset, we investigate how the effects of flood risk and energy efficiency on property value vary across sociodemographic segments and regions in England. We find that while flood risk tends to negatively impact property value, policy interventions aimed at making flood risk insurance affordable essentially eliminate the negative effects of flood risk on property value. However, this effect largely favours the wealthiest, older, and rural sociodemographic segments of the population. Furthermore, for these groups, the effect of flood risk on property value is consistently positive in both statistical and economic terms. This appears linked to the challenge fully disentangling the characteristics that make the properties acquired by the wealthier segments of the population desirable, such as proximity to water bodies and access to flood plains, from flood risk presents. We also show that although properties with higher energy efficiency certification are valued higher than those with lower energy efficiency rating across all English regions, this is not the case for properties located in the wealthiest sociodemographic areas. This could be linked to many of these properties being stately, large, and/or old, characteristics suggesting energy efficiency may not be a crucial purchase factor for wealthier owners.

This paper investigates the impact of geopolitical risk (GPR) on bank credit growth by utilizing quarterly data from banks between 2008Q1 and 2023Q4 in 18 European countries. The main variable of interest is the GPR index developed by Caldara and Iacoviello (2022), which assesses risk at a country-specific level. We find that GPR leads to a decline in total lending, which is attributed to foreign activities, while no significant impact on domestic lending is documented. Our results also show a more resilient lending behavior in response to geopolitical shocks from state-owned banks compared with private banks, implying that the response of bank total lending to GPR is contingent on bank ownership structure. Moreover, as expected, the recent Russian invasion of Ukraine has amplified the negative effect of GPR on total lending, especially for those banks operating in countries located closer to Ukraine and Russia, and those that are more dependent on Russian gas. Finally, we find that the credit behavior of large and listed banks is unaffected by GPR, as are those countries with higher values of gross domestic product, those belonging to the eurozone, and those with low economic policy uncertainty, thus lending activity in these countries continues.

This paper investigates how major customers impact suppliers’ internal information quality. Using data on U. S. manufacturing firms from 2004 to 2022, we find that suppliers with a more concentrated customer base exhibit better internal information quality. The results are robust to instrumental variable approaches and controls for self-selection bias. Additionally, the positive relationship between customer concentration and suppliers’ internal information quality is more pronounced when customers possess greater bargaining power and stronger monitoring incentives. Furthermore, we find that low-quality internal information from suppliers can negatively affect customers’ future performance.

This study pioneers the application of regularized vector autoregressive (VAR) models in return forecasting research and explores their effectiveness in predicting the spot and futures returns of crude oil and heating oil. Our findings demonstrate the efficacy of regularized VAR models, especially the superior predictive performance of the VAR-elastic model with a small lag order and the VARX-elastic model (the model that incorporates predictors as exogenous variables into the VAR-elastic framework) with a large lag order, in predicting the futures and spot returns of crude oil and heating oil. Furthermore, the predictive power of the regularized VAR models for crude oil and heating oil returns is primarily concentrated during periods of economic recessions. Finally, mean-variance investors operating within crude oil and heating oil markets can achieve considerable utility gains by employing regularized VAR models, particularly the VAR-elastic model with a small lag order and the VARX-elastic model with a large lag order.