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Financial institutions manage myriad sources of interest rate risk. We propose a new method to measure financial institutions’ residual interest rate risk using high-frequency financial market data. We provide theoretical justification for the method using a model of life insurance companies with endogenous interest rate risk management. Applying the method to U.S. insurers, we find that their interest rate risk management strategies are generally effective. Analyzing a panel of insurers confirms a key theoretical prediction: The effectiveness of interest rate risk management depends, in part, on the ability to create net worth.

Returns on conventional momentum portfolios exhibit time-varying skewness that deepens during momentum crashes. We exploit this feature and propose a crash indicator—based on the interaction between conditional volatility and skewness—that provides a measure of downside risk directly from the return distribution. This indicator significantly predicts left-tail realizations of momentum returns at daily frequency, capturing information about crash risk beyond volatility alone. Building on this predictability, a skewness-based dynamic allocation improves daily downside risk management and earns significant alphas over existing momentum-timing approaches. We also show that momentum skewness cannot be fully reconciled with asymmetric market exposure.

In this paper, we analyze the key drivers of bond covenant prices by employing a novel measurement approach based on secondary market data. We find that covenant prices vary significantly over time and are associated with market-wide credit risk, volatility, and macroeconomic variables. Apart from the time-series dynamics, there is also significant variation across bond and firm characteristics. In particular, covenant prices increase with the riskiness of bonds and are higher for firms that have more growth options, more tangible assets, and are smaller. Furthermore, we document a positive correlation between the prices of covenants and their subsequent inclusion rates

This study examines the relationship between corporate asset growth rates and bond performance, uncovering a strong inverse relationship between the two. Higher asset growth increases asset value, potentially offering greater protection to bondholders and leading to lower bond returns. By decomposing bond returns into initial yields and subsequent yield changes, our analysis supports this expectation and suggests that investors may overreact to asset growth, as investor sentiment significantly influences bond yields in response to it. Finally, drawing on insights from leverage-based Q-theory, we examine how stock returns respond to asset growth, accounting for its effect on bond performance.

This paper studies how fund-family advisors use cross-fund subsidization to manipulate fund performances and maximize fund-family values, and how this activity shapes market equilibrium. The trade-off between subsidization efficiency and funds’ endogenous profit-performance convexities determines the subsidization. When the effect of profit-performance convexities dominates, advisors optimally use low-value funds to subsidize high-value funds. When the effect of subsidization efficiency dominates, advisors use liquid funds to subsidize temporarily distressed funds. The subsidization induces negative asymmetric cross-fund flow-performance sensitivities: high-value (liquid) funds’ performances strongly decrease low-value (temporarily distressed) funds’ flows, whereas low-value (temporarily distressed) funds’ performances weakly reduce high-value (liquid) funds’ flows

We document how mechanical buying by CRSP-index-tracking funds 5 days post-IPO affects stock returns and IPO deal structure. Using a difference-in-differences design, we show that expected indexer demand causes Fast-Track IPOs to outperform their non-Fast-Track counterparts by over five percentage points, peaking at the index inclusion date and reverting significantly within 3 weeks. Anticipated CRSP index inclusion also affects IPO terms, with Fast-Track IPOs raising 6% more capital than their non-Fast-Track counterparts. Our findings support a proposed index rule change to eliminate a $5.8 billion “shadow tax” paid to intermediaries by index fund investors and firms raising capital through IPOs.

This paper shows that trends typically used for monetary policy guidance are also effective in predicting market excess returns. Using a linear combination method across 14 economic and financial predictor variables, we find that moving-average trends outperform the variables’ current values in forecasting market returns. Incorporating neural networks further improves these predictions. Our findings underscore the importance of trends, supporting the Federal Reserve’s emphasis on integrating trends with lagged variables. When accounting for nonlinearity, we find that market return predictability is significantly greater than commonly believed. Our results are robust across both U.S. and global equity markets. JEL C52, C53, C55, C58, G17

We study fee competition between an incumbent and entrant central counterparty (CCP) under two regimes: interoperability (trades clear at each party’s own CCP) and preferred clearing (trades clear at the incumbent unless both counterparties choose otherwise). Preferred clearing creates network effects that force the entrant to undercut aggressively, while the incumbent sustains higher fees. Fee spreads, average trading costs, and industry profits increase under preferred clearing. However, interoperability is costly because linked CCPs must post collateral against cross-CCP exposure. Interoperability improves welfare when link costs are either low or high enough that the incumbent drops fees to reduce clearing fragmentation.

We propose a new private information measure based on a model of strategic trade optimization in the cross section of securities. Investors receive liquidity and private information shocks and optimize trading across securities, accounting for price impact (Kyle’s λ). The model yields a simple private information measure: λ×OIB (order imbalance). Intuitively, order imbalance is more likely to be information-driven when trading is expensive. We validate our measure by showing that it is greater for smaller firms with higher analyst dispersion, peaks with insider trades, helps explain return reversals, predicts return volatility, and increases before M&A announcements and after analyst coverage terminations.