This article analyzes optimal nonlinear portfolio management contracts. We consider a setting in which the investor faces moral hazard with respect to the effort and risk choices of the portfolio manager. The employment contract promises the manager: (i) a fixed payment, (ii) a proportional asset-based fee, (iii) a benchmark-linked fulcrum fee, and (iv) a benchmark-linked option-type “bonus” incentive fee. We show that the option-type incentive helps overcome the effort-underinvestment problem that undermines linear contracts. More generally, we find that for the set of contracts we consider, with the appropriate choice of benchmark it is always optimal to include a bonus incentive fee in the contract. We derive the conditions that such a benchmark must satisfy. Our results suggest that current regulatory restrictions on asymmetric performance-based fees in mutual fund advisory contracts may be costly.

We study the impact of the tournament-like competition in the mutual fund industry by examining the Active Share choices of funds. Funds with relatively poor performance by the end of the third quarter in a calendar year tend to increase their Active Share during the last quarter. The increase in the trailing funds’ Active Share is accompanied by an increase in the funds’ downside risk exposure. The evidence suggests that the strategic shifts in Active Share we document are not information/skill motivated.

Using a unique dataset we provide new evidence on the significant penalty on client fund performance due to conflicts of interest related to the cross trading (TCT) activities of mutual fund advisers: funds managed by advisers in the top TCT quintile significantly underperform funds managed by advisers in the bottom TCT quintile by 1% per year. Adviser incentives to engage in cross trading are directly related to their opportunities for generating revenues from affiliated trading operations. Additional tests suggest that the significantly higher trading commissions paid by client funds of high-TCT advisers are a major source of their under-performance.

Recent years have seen increased demand from institutional investors for passive replication products that track the performance of hedge fund strategies using liquid investable assets such as futures contracts. In practice, linear replication methods suffer from poor tracking performance and high turnover. We propose a model combination approach to index replication that pools information from a diverse set of pre-specified factor models. Compared with existing methods, the pooled clone strategies yield consistently lower tracking errors, generate less severe portfolio drawdowns, and require substantially smaller trading volume. The pooled hedge fund clones also provide economic benefits in a portfolio allocation context.

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

Model selection (i.e., the choice of an asset pricing model to the exclusion of competing models) is an inherently misguided strategy when the true model is unavailable to the researcher. This paper illustrates the advantages of a model pooling approach in characterizing the cross section of stock returns. The optimal pool combines models using the log predictive score criterion, a measure of the out-of-sample performance of each model, and consistently outperforms the best individual model. The benefits to model pooling are most pronounced during periods of economic stress, and it is a valuable tool for asset allocation decisions.

Does the “smart money” effect documented by Gruber (1996) and Zheng (1999) reflect fund selection ability of mutual fund investors? We examine the finding that investors are able to predict mutual fund performance and invest accordingly. We show that the smart money effect is explained by the stock return momentum phenomenon documented by Jegadeesh and Titman (1993) . Further evidence suggests investors do not select funds based on a momentum investing style, but rather simply chase funds that were recent winners. Our finding that a common factor in stock returns explains the smart money effect offers no affirmation of investor fund selection ability.