Working papers

The earnings announcement return cycle

with Conson Zhang
May 2018

 
Stocks earn negative abnormal returns before earnings announcements and positive after them. An “earnings announcement return cycle" (EARC) strategy earns a four-factor alpha of 8.5% per year (t-value = 6.12). The EARC is unrelated to the earnings announcement premium, and it is a feature of stocks widely covered by analysts. Analysts' forecasts follow the same pattern as returns: analysts’ forecasts become more optimistic after an earnings announcement and more pessimistic as the next one draws near. We attribute one-half of the earnings announcement return cycle to this “optimism cycle.” The EARC may stem from mispricing. Both the average return and optimism patterns are stronger among high-uncertainty and difficult-to-arbitrage stocks, and the EARC strategy is more profitable on days when it would accommodate larger amounts of arbitrage capital.



Factor momentum

with Rob Arnott, Mark Clements, and Vitali Kalesnik
January 2018

 
Past industry returns predict the cross section of industry returns, and this predictability is at its strongest at the one-month horizon (Moskowitz and Grinblatt 1999). We show that the cross section of factor returns shares this property, and that industry momentum stems from factor momentum. Factor momentum is transmitted into the cross section of industry returns via variation in industries' factor loadings. Momentum in industry-neutral factors spans industry momentum; factor momentum is therefore not a by-product of industry momentum. Factor momentum is a pervasive property of all factors; we show that factor momentum can be captured by trading almost any set of factors. Factor momentum does not resolve the puzzle of momentum in individual stock returns; it significantly deepens this puzzle.


Asset managers: Institutional performance and smart betas

with Joseph Gerakos and Adair Morse
Revise and resubmit at Journal of Finance, November 2016

 
Using a dataset of $17 trillion of assets under management, we document that actively- managed institutional accounts outperformed strategy benchmarks by 86 (42) basis points gross (net) during 2000–2012. In return, asset managers collected $162 billion in fees per year for managing 29% of worldwide capital. Estimates from a Sharpe (1992) model imply that their outperformance comes from factor exposures (“smart beta”). If institutions had instead implemented mean-variance portfolios of institutional mutual funds, they would not have earned higher Sharpe ratios. Recent growth of the ETF market implies that asset managers are losing advantages held during our sample period.


Long-term discount rates do not vary across firms

with Matti Keloharju and Peter Nyberg
February 2018

 
Long-term expected returns appear to vary little, if at all, in the cross section of stocks. We devise a bootstrapping procedure that injects small amounts of variation into expected returns and show that even negligible differences in expected returns, if they existed, would be easy to detect. Markers of such differences, however, are absent from actual stock returns. Our estimates are consistent with production-based asset pricing models such as Berk, Green, and Naik (1999) and Zhang (2005) in which firms' risks change over time. Our results imply that stock market anomalies have only a limited effect on firm valuations.



Informed traders, long-dated options, and the cross section of stock returns

with Mark Clements and Vitali Kalesnik
September 2017

 
Option prices predict the cross section of equity returns. We show that, unconditionally, the prices of long-dated options contain all the information relevant for predicting returns. Information, however, shifts towards short-dated options when an earnings announcement is imminent and when options are cheap to trade. The difference between short- and long-dated options also predicts the timing of merger announcements. Our results are consistent with option prices reflecting the actions of informed traders, and with these traders optimally choosing option maturities to maximize the value of their information.





Old working papers

Learning and stock market participation

November 2005

 
I examine the impact of trading constraints on market participation when agents learn about their investment opportunities. The possibility of facing binding constraints in the future creates a feedback that can keep agents out of the market even if the risk premium is high. This effect arises with learning because the changes in investment opportunities are correlated with future realized outcomes: an agent will have a poor investment opportunity set precisely in those future states where her marginal utility is high. Non-participation arises also in an equilibrium model where agents resolve uncertainty about the cash flow covariance between tradable and non-tradable assets. These results suggest that learning and short-sale constraints can simultaneously generate limited participation, higher risk premium, and insignificant contemporaneous correlation between the stock return and the income of those who do not participate in the stock market. We conclude that a standard intertemporal hedging motive, generated by (i) learning about the parameters of the economy or by (ii) changes in the labor income dynamics, may account for agents' seemingly puzzling nonparticipation decisions without relying on non-standard preferences.

The individual day trader

November 2005

 
This paper shows that individual day traders are reluctant to close losing day trades. They even sell other stocks from their portfolios to finance the unintended purchases. This disposition to ride losers has significant long-term welfare consequences. Day traders hurt their portfolios’ performance up to −6% in three months after a holdings change. The changes in individuals’ exposure to market-wide shocks cause this underperformance: individuals systematically migrate towards small technology stocks with low B/M ratios. We find a negative relation between day trading profits and long-term performance: active day traders have the highest day trading profits but they hurt their long-term performance the most. Our results suggest that behavioral biases can push investors towards portfolios they might feel uncomfortable holding under other circumstances.