This paper explores implications of weak identification in common ‘long memory’ and recent ‘rough’ approaches to modeling volatility dynamics of financial assets. We unveil an asymptotic near-observational equivalence between a long memory model with weak autoregressive dynamics and a rough model with a near-unit autoregressive root. Standard methods struggle to distinguish them, and conventional asymptotics are invalid. We propose an identification-robust approach to construct confidence sets that reveal the uncertainty and aid inference. Empirical studies based on realized volatility and trading volume often fail to statistically reject either model, thereby providing evidence of their potential coexistence.

Social media has become a popular venue for individuals to share the results of their own analysis on financial securities. This paper investigates the extent to which investor opinions transmitted through social media predict future stock returns and earnings surprises. We conduct textual analysis of articles published on one of the most popular social media platforms for investors in the United States. We also consider the readers' perspective as inferred via commentaries written in response to these articles. We find that the views expressed in both articles and commentaries predict future stock returns and earnings surprises.

Social ties between mutual funds and the companies in which they invest (investees) can both facilitate information transfers and encourage favoritism. Using the investment choices of mutual funds in China, we compare investment performance of holdings in companies that are socially connected to mutual funds versus those that are not. We find that funds allocate more investment to connected investees' stocks, especially when a fund is weakly monitored. This overweighting is greater in times of poor investee performance, when the benefits of additional investment to the connected investees are high. Weakly monitored funds' preference for connected stocks hurts the returns of these funds, yielding a 6.6 percent lower annualized risk-adjusted return, relative to closely monitored funds. These results suggest that, absent sufficient monitoring, agency conflicts generated by social networks can dominate the information advantages of these networks.

We examine the effects of city-level auditor industry specialization and scale economies on audit pricing in the United States. Using a sample of Big N clients for the 2000–2007 period, and a scale measure based on percentile rankings of the number of audit clients at the city-industry level, we document significant specialization premiums and scale discounts in both the pre- and post-Sarbanes-Oxley Act (SOX) periods. However, the effects of industry specialization and scale economies on audit pricing are highly interactive. The negative effect of city-industry scale on audit fees obtains only for clients of specialist auditors. By contrast, clients of non-specialist auditors obtain scale discounts only when they enjoy strong bargaining power, suggesting that auditors are “forced” to pass on scale economies to clients with greater bargaining power. Data Availability: Data are available from sources identified in the article.

In recent years, firms have increasingly come under scrutiny from environmental, social, and governance (ESG) rating agencies which systematically assess and publicize ESG-related information to diverse stakeholders. This study aims to investigate whether firms exhibit a heightened incentive to avoid ESG-related regulatory violations once they come under the coverage of ESG rating agencies. Analyzing data spanning from 2000 to 2018 and considering the coverage provided by four prominent ESG rating agencies to U.S. firms, we leverage the staggered initiation and intensity of this coverage. Our findings reveal a negative correlation between ESG violations and the commencement and extent of coverage by ESG rating agencies. This relationship is particularly pronounced for firms characterized by lower levels of corporate monitoring as indicated by fewer analysts providing coverage, limited media attention, weaker ESG commitments, and less disparate ESG ratings. Taken together, our study sheds light on the monitoring role of ESG rating agencies, illustrating their significance in incentivizing managers to mitigate ESG violations.

In this study, we forecast the realized volatility of the S&P 500 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models’ parameters and the volatility of realized volatility. A dynamic model averaging (DMA) approach is used to combine the forecasts of the individual models. Our empirical results suggest that DMA can generate more accurate forecasts than individual model in both statistical and economic senses. Models that use time-varying parameters have greater forecasting accuracy than models that use the constant coefficients. The superiority of time-varying parameter models is also found in volatility density forecasting.

This paper investigates the impact of international political risk on government bond yields in 34 debtor countries using a comprehensive database of 109 international political crises from 1988 through 2007. After employing the total number of international political crises as a proxy for political risk and controlling for country-specific economic conditions, we establish a positive and significant link between international political risk and government bond yields. This is consistent with global bond investors demanding higher returns at times of high political uncertainty. In addition, we show that international political risk has a reduced adverse effect on bond prices when the debtor country has a stable political system and strong investor protection.

We analyze the relationship between greater bank competition and the screening of potential borrowers. Using a large sample of Chinese private firms and China's entry into the WTO as a unique setting leading to greater bank competition, we find the following. First, the sensitivity of bank credit to prior borrowing-firm performance increases after China's WTO entry. This sensitivity increase is greater in more bank-dependent industries and smaller in Chinese regions with greater financial sector development. Second, the increase in the sensitivity of bank credit to firm performance is much greater for state-owned firms compared to private firms. Third, the effect of bank credit on subsequent firm productivity and performance is greater for loans given after China's WTO entry compared to those given prior to WTO entry. Overall, the results of our empirical analysis suggest that the stringency of bank screening of borrowers in China increased with greater banking sector competition.

We develop a state‐of‐the‐art fraud prediction model using a machine learning approach. We demonstrate the value of combining domain knowledge and machine learning methods in model building. We select our model input based on existing accounting theories, but we differ from prior accounting research by using raw accounting numbers rather than financial ratios. We employ one of the most powerful machine learning methods, ensemble learning, rather than the commonly used method of logistic regression. To assess the performance of fraud prediction models, we introduce a new performance evaluation metric commonly used in ranking problems that is more appropriate for the fraud prediction task. Starting with an identical set of theory‐motivated raw accounting numbers, we show that our new fraud prediction model outperforms two benchmark models by a large margin: the Dechow et al. logistic regression model based on financial ratios, and the Cecchini et al. support‐vector‐machine model with a financial kernel that maps raw accounting numbers into a broader set of ratios.

Managers often overreact to revenue fluctuations, leading to unnecessary workforce adjustments and increased training costs. This study examines how board governance influences firms’ employment sensitivity to revenue fluctuations. Analyzing global board reforms, we find that board reforms significantly reduce managerial overreaction to revenue fluctuations. Utilizing recent difference-in-differences estimators that address heterogeneous treatment effects, we ensure the robustness of our results. The reduction in employment sensitivity is more pronounced when board reforms strengthen the independence of boards and audit committees, particularly in jurisdictions with weaker board efficacy, shareholder, and employment protection legislation. Enhanced effects are observed in firms with initially lower board independence and rapid reform compliance, in entities experiencing greater information asymmetry, marked by higher labor intensity, higher pre-reform agency costs and financial constraints, and in firms led by less experienced CEOs or boards with higher male representation.