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.