Journal of Empirical Finance最新文献

We investigate how firm value may be related to emotional and mental states of innovative workers via the innovation channel. By analyzing a comprehensive inventor-level dataset, we discover that the economic value inventors produce for their firms through their patenting activity is positively associated with their sunshine-induced mood. This augmentation in patent value is not due to a surge in the quantity of patents, but rather to the heightened significance and influence of the created inventions. These inventor-level observations aggregate to the firm level. The observed sunshine-enhanced effect is of a magnitude comparable to other primary behavioral drivers of innovation outcomes, such as CEO overconfidence and CEO thrill-seeking tendencies. Our research underscores the significance of the emotional and psychological states of innovative personnel that has implications for managers and shaerholders.

Existing research on return predictability traditionally employs aggregate, market-level information. To investigate the applicability of return predictability at a finer level, we examine out-of-sample time-series return predictability at the characteristic-based portfolio level, using predictive regressions with portfolio-level predictors and a sum-of-the-parts approach. In addition to rejecting the null of no predictability at the market level, we detect statistically and economically significant out-of-sample predictability amongst particular portfolios. Notably, we show that large growth portfolios exhibit return predictability, consistent with predictions drawn from prior literature, while we fail to consistently detect predictability for all remaining size and book-to-market portfolios. Our results reveal a significant (relative) forecast error R-squared of 0.65 % for large-growth stocks, translating into an annualised certainty equivalent gain of 1.37 %.

We evaluate the performance of several linear and nonlinear machine learning (ML) models in forecasting the realized volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a dataset that includes past values of the RV and additional predictors, including lagged returns, implied volatility, macroeconomic and sentiment variables. We compare these models to widely used heterogeneous autoregressive (HAR) models. Our main conclusions are that (i) the additional predictors improve the out-of-sample forecasts at the daily and weekly forecast horizons; (ii) we find no evidence that nonlinear ML models can statistically outperform linear models in general; and (iii) in terms of the economic value that an investor would derive from monthly RV forecasts to build volatility-timing portfolios, simpler models without additional predictors work better.

This study explores how local gambling culture, as measured by state-level religious composition, influences equity crowdfunding success. Our research uncovers a positive relationship between local gambling culture and equity crowdfunding success, driven by the state's innovation propensity. We also shed light on the positive effect of local gambling culture on firms’ document amendment behaviors, influenced by state regulation restrictions. Further analyses indicate that firms’ ownership structure moderates the positive relationships between local gambling culture and both equity crowdfunding success and document amendment behaviors. Our study centers on equity crowdfunding, demonstrating the important role of local gambling culture in determining entrepreneurial fundraising outcomes.

Individual investors tend to trade in the same direction as other individual investors in the same broker branch. The more pronounced an individual investor's herding behavior, the worse his/her investment performance. We find that the limit orders of herding investors have a lower execution ratio, a longer time-to-execution, and a higher probability of being picked up by institutional investors, indicating that their orders are subject to the pick-off risk as they face fierce execution competition and tend to become stale after submissions. Finally, we find that individual investors learn from experience and herd less in the future.

How carbon risk exposure affects fund vulnerability is an important and unexplored topic. On this basis, I explore the impact of the carbon risk exposure of funds on their vulnerability. I find that funds with higher carbon risk exposure are more vulnerable. I further investigate the mechanism by which the carbon risk exposure of funds affects their vulnerability and verify the fund flow correlation and fund portfolio liquidity channels. I find that the increase in the carbon risk exposure of funds increases flow correlation and reduces portfolio liquidity, thereby increasing their vulnerability. The heterogeneity analysis results also highlight the greater impact of carbon risk exposure on the vulnerability of funds with poor social responsibility performance and higher portfolio concentration.

G11; G12; G23

This paper investigates the dynamics of the United States oil (USO) exchange traded fund (ETF). Daily USO returns are modelled using stochastic volatility (SV) frameworks derived from three different model classes: SV models with contemporaneous jumps in returns and volatility (SVCJ); SV model with jumps in returns only (SVJ); and a pure SV model class without jumps. Six affine and non-affine models are considered within each model class that depend on specification of the drift and the diffusion terms in the variance process, resulting in a total of 18 models that are estimated using particle Markov Chain Monte Carlo (PMCMC) approach. Model evaluation is conducted using the Deviance Information Criterion (DIC), Bayes factors, probability plots, and deviation measures to assess the discrepancy between the estimated volatility and key benchmarks, the crude oil ETF volatility index (OVX) and the realised volatility (RV). Our analysis indicates that models incorporating jumps, particularly the SVCJ-PLY-0.5 and SVCJ-PLY-1.0, more accurately capture USO dynamics than standard SV models. The SVCJ-PLY-0.5 model ranks highest based on DIC statistics and Bayes factors, and both models excel in aligning their estimated volatility with the OVX and RV benchmarks. Overall, the statistical criteria employed in our comparison favour models with jumps over the standard SV model class, suggesting that models incorporating jumps in both return and variance processes (SVCJ) are superior to those with jumps solely in the return process (SVJ). The affine models SVJ-LIN-0.5 and SVCJ-LIN-0.5 with linear variance drift and square root diffusion that are particularly interesting for theoretical finance applications are highly ranked among considered frameworks, outperforming several non-affine alternatives. Our analysis of the regression model for volatility forecasting reveals a significant predictive accuracy in the evaluated models, demonstrating their effectiveness in anticipating future volatility trends.

This paper explores the influence of increasing ownership in fixed-income ETFs and mutual funds on liquidity commonality among corporate bonds. The unpredictable nature of liquidity demands in these funds may lead to correlated trading in underlying illiquid bonds. The study finds a positive and significant relationship between ETF ownership and liquidity commonality in investment-grade corporate bonds. In contrast, mutual fund or index fund ownership does not exhibit a similar effect, a result that differentiates corporate bonds from equities. This distinction from equities is attributed to different liquidity management strategies employed by equity and corporate bond mutual funds. The paper also highlights factors contributing to the varying impacts of ETFs and mutual funds on corporate bonds, including correlated trading due to fund flows, differences in investor clienteles, and the role of ETF arbitrage activities.

Non-standard errors capture variation due to differences in research design choices. We document large variation in design choices in the context of asset pricing factor models and find that the average ratio of the non-standard error to the standard error across factors exceeds one. Using NAN breakpoints instead of NYSE breakpoints improves the average Sharpe ratios the most, from 0.46 to 0.63. Other important design choices relate to excluding microcaps, industry-adjusting, and the rebalancing frequency, which highlights the need for researchers to clearly describe and motivate these choices.

We examine firms’ investment decisions in research and development during political uncertainty using US firm quarterly data from 2005 through 2021. By using quarterly data, we minimize stickiness and overly generalized assumptions. The findings show that firms invest more in research and development in times of high political risk. Monetarily, the significance translates to about $10.6 million injection into research and development. The results are more pronounced for firms in competitive industries, politically sensitive firms, firms with higher growth opportunities, and firms with more liquid assets. The results are robust to the test for correlation, addressing endogeneity, and alternative proxies adopted for the variables of interest. Overall, the findings of this study support the strategic growth option theory, which suggests that firms follow a preemptive strategy in periods of high uncertainty.