学术论文

We analyze a single-factor setting in which there is private information regarding cash flows as well as their betas. Private information about betas, together with market makers’ risk aversion and mean betas’ nonnegativity, implies a nonlinear price schedule whose stochastic slope covaries positively with order flow when the expected factor payoff is positive and vice versa. We predict a negative relation between the covariance and expected returns and an attenuation of the beta anomaly in asset returns after accounting for this relation. Empirical tests confirm these predictions.

We uncover the major drivers of macro aggregates and the real exchange rate at business cycle frequencies in Group of Seven countries. The estimated drivers of key macro variables resemble each other and account for a modest fraction of the real exchange rate variances. Dominant drivers of the real exchange rate are orthogonal to main drivers of business cycles, generate a significant deviation of the uncovered interest parity condition, and lead to small movements in net exports. We use these facts to evaluate international business cycle models accounting for the dynamics of both macro aggregates and the real exchange rate.

We re-examine monetary policy spillovers to Emerging Market Economies (EME) in the form of capital flow reversals, using sectoral-level securities holdings data for Euro Area investors. In response to a surprise monetary tightening, active investors such as investment funds re-balance their portfolios away from EME, while more passive, long term investors such as insurance funds and banks exhibit no significant reaction on average. For active investors, the reallocation out of EME appears stronger under synchronized monetary tightening between the Fed and the ECB. However, these investors may even inject more capital to EME securities when the monetary tightening surprises contain positive news about the Euro Area economy. Issuers’ monetary–fiscal stability may explain the heterogeneous impact of these spillovers.

In contemporary data analysis, it is increasingly common to work with non-stationary complex data sets. These data sets typically extend beyond the classical low-dimensional Euclidean space, making it challenging to detect shifts in their distribution without relying on strong structural assumptions. This paper proposes a novel o ine change-point detection method that leverages classiers developed in the statistics and machine learning community. With suitable data splitting, the test statistic is constructed through sequential computation of the Area Under the Curve (AUC) of a classier, which is trained on data segments on both ends of the sequence. It is shown that the resulting AUC process attains its maxima at the true change-point location, which facilitates the change-point estimation. The proposed method is characterized by its complete nonparametric nature, high versatility, considerable exibility, and absence of stringent assumptions on the underlying data or any distributional shifts. Theoretically, we derive the limiting pivotal distribution of the proposed test statistic under null, as well as the asymptotic behaviors under both local and xed alternatives. The localization rate of the change-point estimator is also provided. Extensive simulation studies and the analysis of two real-world data sets illustrate the superior performance of our approach compared to existing model-free change-point detection methods.

Lenders are reluctant to finance firms' innovation activities because such activities tend to be opaque, with a high likelihood of negative outcomes that could hamper loan repayment. We posit that public credit registries (PCRs), which play an important role in credit information sharing in many countries, can facilitate financing by reducing adverse selection and moral hazard and increasing bank competition. Using the staggered establishment of PCRs in different countries and an international firm–patent data set, we find that credit information sharing positively affects firm innovation, especially in firms that experience a larger increase in bank debt financing after the establishment of a PCR. This finding is consistent with the notion that credit information sharing promotes firm innovation by easing bank debt financing frictions. We also find a stronger effect in countries that experience a large increase in bank competition after the establishment of a PCR—consistent with increased bank competition serving as a channel through which credit information sharing facilitates bank debt financing, thereby generating a positive effect on firm innovation. The positive effect is more pronounced when the established PCR has features that promote credit information sharing. It is also more pronounced for opaque firms and firms in innovation-intensive industries, indicating that credit information sharing helps to reduce financing frictions. Finally, we posit and find evidence that firm efficiency in transforming innovation inputs into outputs improves after the establishment of a PCR. Overall, our paper offers novel insights into how credit information sharing facilitates firm innovation.

We analyze whether and how the perceived federal-level legal liability linked to federal judge ideology is associated with the likelihood of firms receiving going-concern modified audit opinions and analyze the differential effects on Big 4 and non–Big 4 auditors. We find that Big 4 and non–Big 4 auditors converge in their going-concern reporting decisions in circuits with more liberal judges. This convergence is caused by the greater effect of judge ideology on non–Big 4 auditors. Furthermore, we empirically examine the association between federal judge ideology and actual lawsuits against auditors and find that judge ideology has a greater impact on lawsuit likelihood for non–Big 4 auditors for the restating companies. When auditors are sued, both the payout likelihood and amount are greater in circuits with more liberal judges, with the effect being more pronounced for non–Big 4 auditors. This study provides evidence on how the perceived exposure to a gross negligence legal standard shapes auditors' going-concern reporting incentives for the two tiers of auditors in the market. It also adds to the literature on auditor litigation.

Insurers can boost their earnings by accruing interest income from their corporate bond investments. We document that insurers have higher corporate bond investments as well as less equity and cash holdings, when their parents meet or just beat analysts’ quarterly earnings forecasts, compared to when their parents miss or comfortably beat the forecasts. The investment in corporate bonds to boost earnings is more pronounced when bond offerings provide more opportunities for accruing interest income, when the parent’s corporate governance is weaker, when the parent’s managers have more equity incentives, when insurers face more competition, when other earnings management techniques are used, or when the insurance segment is more important to the parent. Finally, insurers suspected of helping their parents meet or beat earnings benchmarks experience worse investment performance in subsequent years, presumably because, by investing more in corporate bonds, the insurers forgo investment opportunities with higher longer-term returns.

We propose a fiscal policy expectations mechanism. When bad macro news arrives (in our study, when initial jobless claims (IJC) are higher than expected), investors may expect more generous government spending and drive up aggregate stock prices through the expected cash flow channel. Using a time-series sample from January 2013 to March 2021, we find that this phenomenon emerges when newspapers mention fiscal policy more. In the cross section, firms expected to receive more government spending – through stimulus supports during COVID-19 or procurement contracts before 2020 – exhibit higher individual stock returns when bad IJC shocks arrive.

Firms with political connections to a regime with an authoritarian history face a dilemma when the regime undergoes a democratic transition. Such connections provide an essential competitive advantage when the regime is in power but become a liability when an institutional transition brings democratic change. This study theorizes that when expose a regime’s distorted policies favoring elites over others and signal a high probability of regime turnover, firms may hedge against the risks associated with their political connections by engaging in philanthropy. We further contend that this effect is stronger for firms located in regions characterized by the rise of an opposing political party or a strong civil society. We find support for our theory in Taiwan’s 2014 Sunflower Movement. Our article reveals a strategy that firms adopt to survive democratic transitions and thus contributes to research on how firms use non-market strategies to adapt to institutional changes. Our research also shows that strategic corporate social responsibility (CSR) can substitute for corporate political activity or compensate for its limitations, and it expands research on the signaling function of social movements from public to private politics.