Published and Forthcoming Papers

The Good or The Bad? Which Mutual Fund managers Join Hedge Funds? (with Joshua Pollet, Z. Jay Wang, and Lu Zheng)), 2011, Review of Financial Studies 24 (9), pp 3008-3024. doi: 10.1093/rfs/hhr057

Does the mutual fund industry lose its best managers to hedge funds? We find that mutual funds are able to retain managers with good performance in the face of competition from a growing hedge fund industry. On the other hand, poor performers are more likely to leave the mutual fund industry. A small fraction of these poor performers find jobs with smaller and younger hedge fund companies, especially when the hedge fund industry is growing rapidly. Analogously, a small fraction of the better performing mutual fund managers are retained by allowing them to manage a hedge fund side-by-side.

Market Liquidity and Flow-Driven Risk (with Timothy Johnson), 2011, Review of Financial Studies 24 (3), pp 721-753. doi: 10.1093/rfs/hhq132

Using a unique data set of trades and limit orders for S&P 500 futures, we decompose the aggregate risk into a component driven by the impact of net market orders and a component unrelated to net orders. The first component -- flow-driven risk -- is large, accounting for approximately 50 percent of market variance, and it is not transient. This risk represents the joint effect of net trade demand and the price impact of that demand i.e. illiquidity. We find that flows are largely unpredictable, and lagged flows have no price impact. Flow-driven risk is time varying because price impact is highly variable. Illiquidity rises with market volatility, but not with flow uncertainty. Net selling increases illiquidity, which amplifies downside flow-driven risk. The findings are consistent with flow-driven shocks resulting from fluctuations in aggregate risk-bearing capacity. Under this interpretation, investors with constant risk tolerance should trade against such shocks (i.e. "supply liquidity") to achieve substantial utility gains. Quantitatively accounting for the scale of flow-driven risk poses a major challenge for asset pricing theory.

Liquidity Effects in OTC Options Markets: Premium or Discount? (with Anurag Gupta and Marti G. Subrahmanyam), 2011, Journal of Financial Markets 14, pp 127-160. doi:10.1016/j.finmar.2010.08.003

Can the liquidity premium in asset prices, as documented in the exchange-traded equity and bond markets, be generalized to the over-thecounter (OTC) derivative markets? Using OTC euro () interest rate cap and floor data, we find that illiquid options trade at higher prices relative to liquid options. This liquidity discount, though opposite to that found in equities and bonds, is consistent with the structure of this OTC market and the nature of its demand and supply forces. Our results suggest that the effect of liquidity on asset prices cannot be generalized without regard to the characteristics of the market.

The Economic Determinants of Interest Rate Option Smiles (with Anurag Gupta and Marti G Subrahmanyam), 2008, Journal of Banking and Finance 32(5), pp 714-728. doi: 10.1016/j.jbankfin.2007.05.012

We address three questions relating to the interest rate options market: What is the shape of the smile? What are the economic determinants of the shape of the smile? Do these determinants have predictive power for the futures shape of the smile and vice versa? We investigate these issues using daily bid and ask prices of euro () interest rate caps/floors. We find a clear smile pattern in interest rate options. The shape of the smile varies over time and is affected in a dynamic manner by yield curve variables and the future uncertainty in the interest rate markets; it also has information about future aggregate default risk. Our findings are useful for the pricing, hedging and risk management of these derivatives.

Working Papers

The Dynamics of Hedge Fund Fees (with Z. Jay Wang, Youchang Wu, and Quoc H. Nguyen))

In contrast to the perception of a common 2/20 fee structure, we find considerable cross-sectional and time series variations in hedge fund fees using a large panel data set. Fund family characteristics and prior performance play an important role in fee determination. New fund families are likely to charge at- or above-median fees. Initial fees of funds introduced by an existing family are positively related to the prior performance of the family as well as of the investment strategy they follow. Furthermore, management fees are dynamically adjusted in response to past fund performance. Funds that increase management fee more aggressively experience a bigger drop in subsequent money inflows, and are more likely to maintain their good performance. This suggests that fee increases, which typically apply only to new investors, may benefit existing investors by mitigating diseconomies of scale.

Experience of Regret and Subsequent Trading Behavior (with Pan Deng and Fei Wu))

Regret is a proposed explanation for many puzzles in economics and finance. Yet very few studies analyze the effect of experienced regret on subsequent decisions in a real-world-setting. We find that after experiencing regret, individuals are more likely to change their decision to place a market or limit order. Confirming the predictions of regret theory, the effect of regret is stronger following an action rather than inaction, loss on the prior order, and an unusual order strategy for the individual. Moreover, decisions influenced by regret yield poor returns. The totality of these results rules out rational learning as an explanation.

Extrapolative Expectations: Implications for Volatility and Liquidity

This paper presents a model of liquidity and volatility in which investors extrapolate recent price movements to forecast the volatility of a risky asset. High perceived volatility leads to high risk premium, low current return, low risk-free rate and illiquid markets. Illiquidity amplifies supply shocks, increasing realized volatility of prices, which feeds into subsequent volatility forecasts. As a result, clustering of volatility and liquidity arises endogenously. The model helps to unify several known facts about liquidity and volatility, and I find support for its new prediction which links misperception of volatility to liquidity.

Projects in Progress

Mutual fund manager turnover and flow performance sensitivity

Value of Ambiguity