Knowledge that Transforms

We find that in the leveraged loan sector, firms borrowing from non-banks have lower profitability following loan originations, compared to firms borrowing from banks, after controlling for observable factors. As non-bank borrowers experience less intense monitoring than bank borrowers, they engage in more risk-taking, which could explain their lower profitability following loan issuance. Using the leveraged lending guidance as a plausibly exogenous shock, which resulted in the migration of borrowers from banks to non-banks, we provide causal evidence corroborating our main results. Overall, our findings suggest that macroprudential policies which exclusively target the traditional banking sector may have negative consequences.

We propose a novel framework for modeling large dynamic covariance matrices via heterogeneous autoregressive volatility and correlation components. Our model provides direct forecasts of monthly covariance matrices and is flexible, parsimonious and simple to estimate using standard least squares methods. We address the problem of parameter estimation risks by employing nonlinear shrinkage methods, making our framework applicable in high dimensions. We perform a comprehensive empirical out-of-sample analysis and find significant statistical and economic improvements over common benchmark models. For minimum variance portfolios with over a thousand stocks, the annualized portfolio standard deviation improves to 8.92% compared to 9.75–10.43% for DCC-type models.

High-income individuals direct a higher share of their savings toward the stock market and a lower share toward credit markets and bank deposits. An increase in income inequality thus translates into an expected decrease in the relative supply of debt capital. Nonfinancial corporations are predicted to cater to these shifts by adjusting their capital structure. Results from instrumental variable models estimated on a panel sample of either US or non-US corporations and several robustness checks empirically support this prediction. Consistent with the theorized household portfolio channel, the negative relation between leverage and local income inequality is driven by corporations less likely to have access to non-local capital markets. This result is confirmed when exploiting the introduction of the euro as an exogenous shock to European firms’ relative exposure to non-domestic investors.

We examine the liquidity provision premium in cryptocurrency markets using the returns from the short reversal strategy. We show that returns from liquidity provision can be predicted using the volatility index, realized variance, risk aversion, crash risk, tail risk, and innovations of Tether liquidity. We also find that<br/>an increase in the liquidity provision premium is associated with a decline in liquidity, trading volume, and transaction count, as well as more withdrawals, higher fees, and greater impermanent loss on Uniswap.<br/>This suggests potential competition between centralized and decentralized exchanges. Further, the liquidity provision premium of stock markets in China and Japan positively predicts the premium of cryptocurrency markets (effect of a common shock), meanwhile that of stock markets in the US and Canada negatively predicts the premium of cryptocurrency markets (substitution effect).

Using a survey in the Netherlands, we find that high inflation perceptions are associated with low trust in Dutch politics and the Dutch central bank, also if we control for a broad array of potential confounding factors. The higher individuals’ perceived inflation is and the harder it is for them to make ends meet, the lower their trust in Dutch politics, the Dutch central bank, and the European Central Bank. We also find that trust in an authority is lower when it is considered responsible for bringing inflation down. Quite remarkably, most people think the government is responsible for maintaining price stability.

Firms with similar credit ratings, particularly high-yield ones, exhibit strong comovement in stock returns . After a firm is downgraded to high-yield status, it comoves more with other high-yield firms and less with investment-grade ones, a pattern not fully explained by changes in fundamentals or other firm characteristics. We find evidence that suggests the investor clientele explanation for rating-related comovement, potentially arising from heterogeneous lottery preferences. High-yield-averse funds reduce their holdings of firms being downgraded to high-yield status, particularly those that are more lottery-like, much more significantly than high-yield-prone funds. Furthermore, a firm’s stock returns become sensitive to flows into high-yield-prone funds after being downgraded to high-yield status, consistent with the price impact of rating-based category investing.

Predicting IPO first-day returns is inherently challenging due to the wide range of contributing factors, each with distinct statistical properties. We assess the performance of several machine learning (ML) techniques and identify XGBoost as the most statistically effective model for forecasting first-day returns. Using a comprehensive set of 863 pre-IPO variables, our high-performing predictive model accurately estimates both the direction and magnitude of IPO first-day returns. The most influential predictors include underwriter agency measures, price revision, and the free-float fraction. Using a rolling-window predictive approach, the model demonstrates substantial practical value, generating approximately $300 billion in gains from IPOs with positive first-day returns and avoiding more than $22 billion in losses from those with negative returns over the 2000–2016 period.

We test the split signaling hypothesis by examining the reaction of sophisticated investors to stock split announcements. Return-based tests of signaling used in earlier studies produce conflicting results and have been criticized as unreliable. We overcome this issue by focusing on the long-term post-split behavior of short sellers, who are widely regarded as sophisticated investors. Upon controlling for alternative hypotheses and conventional short selling determinants, we find a substantial reduction in short interest in reaction to split announcements. Furthermore, consistent with signaling, the degree of the reduction is positively related to signal strength and to the splitter's level of information asymmetry. Overall, our results are consistent with the view that firms use stock splits to relay positive value-relevant signals.

Opportunities for stochastic arbitrage in an options market arise when it is possible to construct a portfolio of options which provides a positive option premium and which, when combined with a direct investment in the underlying asset, generates a payoff which stochastically dominates the payoff from the direct investment in the underlying asset. We provide linear and mixed-integer linear programs for computing the stochastic arbitrage opportunity providing the maximum option premium to an investor. We apply our programs to 18 years of data on monthly put and call options on the Standard & Poors 500 index, finding no evidence that stochastic arbitrage opportunities are systematically present. A skewed specification of the underlying market return distribution with a constant market risk premium and constant multiplicative variance risk premium is broadly consistent with the pricing of market index options at moderate strikes.

This study investigates the impact of public corruption on lending outcomes in the context of Marketplace Lending (MPL) platforms such as LendingClub. Utilizing data on over one million loans and state-level corruption metrics from the US Department of Justice, this research uniquely examines within-country variations in corruption. Our findings reveal a significant correlation between public corruption and loan defaults, with a 3 % increase in default rates per unit increase in corruption. Interest rates also rise by 9 basis points under similar conditions. These effects persist across various model specifications and robustness checks. We demonstrate that trust mediates the relationship between corruption and loan defaults, and that neither governance nor enforcement explains the observed impacts. This study contributes to the literature by linking corruption to individual financial behavior in fintech lending, highlighting the crucial role of trust in financial transactions.