Knowledge that Transforms

Prior studies on cryptocurrency momentum overlook liquidation risk and other real-world frictions and inadequately assess its profitability. We evaluate time-series and cross-sectional momentum addressing these issues. When portfolios are marked to market daily, many cross-sectional momentum portfolios are liquidated, and those with statistically significant mean returns often earn negative profits. The mean return is insufficient for testing profitability in the cryptocurrency market, where returns are heavily skewed and fat-tailed. Evidence of time-series momentum is strong, whereas evidence of cross-sectional momentum is almost nonexistent. Investor overconfidence and the disposition effect appear to be important drivers of cryptocurrencies’ price dynamics.

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.

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.

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.

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.

This paper provides the first systematic evidence on a recent industry innovation: money market funds offering multiple intraday NAV strikes and redemption windows. Emerging after the 2016 floating-NAV reforms, these multistrike funds hold safer, more liquid assets than traditional single-strike funds offering end-of-day redemptions, yet face substantially larger outflows during periods of market stress. Our findings point to a structural concentration of liquidity-sensitive investors in multistrike funds, revealing how fund microstructure influences run dynamics among sophisticated institutions. Despite evolving liquidity requirements, the core behavioral and structural differences we identify remain highly relevant for evaluating ongoing and future regulatory reforms

Underestimating discount rate volatility leads to asset pricing anomalies. Using analysts’ return forecasts as proxies for subjective discount rates, I show that these forecasts exhibit systematically lower volatility than CAPM-based benchmarks, whose objective fluctuations negatively predict future returns, especially for high beta-volatility stocks. A misvaluation measure based on this underestimation significantly predicts cross-sectional CAPM alphas, while a tradable factor explains 12 prominent anomalies. These findings underscore discount rate volatility underestimation as a unifying explanation for analysts’ forecast errors and cross-sectional return predictability, linking recent evidence on aggregate subjective belief dynamics with firm-level mispricing.

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 show that demand pressure from retail investors makes options on low-price stocks relatively expensive—delta-hedged options on low-price stocks underperform those on high-price stocks by 0.63% per week for calls and 0.36% for puts. Natural experiments corroborate this finding: options become more expensive following stock splits, options on mini indices are more expensive than those on main indices, and mini contract options are more expensive than standard options. We attribute our findings to retail investors’ preference for skewness and divergence of opinion. Limits to arbitrage and strategic quote setting by market makers contribute to, but do not fully explain, this effect.

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.