Time-series efficient factors
with Sina Ehsani
Factors in prominent asset pricing models are positively serially correlated. We derive the optimal allocation that transforms an auto-correlated factor to a "time-series efficient" factor. The key determinant of the value of factor timing is the ratio of a factor's auto-correlation to its Sharpe ratio. Time-series efficient factors earn significantly higher Sharpe ratios than the original factors and contain all the information found in the original factors. Momentum strategies profit by timing auto-correlated factors; they pick up factor "inefficiencies." We show that, rather than augmenting models with the momentum factor, each factor can instead be made time-series efficient. An asset pricing model with time-series efficient factors, such as an efficient Fama-French five-factor model, prices momentum. Time-series efficient factors also explain more of the co-variance structure of returns; they describe the cross section better than the standard factors and align more closely with the true SDF.
Factor momentum and the momentum factor
with Sina Ehsani
Revise and resubmit at Journal of Finance, May 2020
Momentum in individual stock returns emanates from momentum in factor returns. Most factors are positively autocorrelated: the average factor earns a monthly return of 6 basis points following a year of losses and 51 basis points following a positive year. We find that factor momentum concentrates in factors that explain more of the cross section of returns and that it is not incidental to individual stock momentum: momentum-neutral factors display more momentum and momentum in firm-specific residuals appears to capture momentum in omitted factors. Our key result is that momentum is not a distinct risk factor; it times other factors.
Featured in Wall Street Journal ("A New Way to Think About Momentum Investing
”, May 5, 2019)
Award: Q-Group’s Jack Treynor 2019 Prize Winner
with Rob Arnott, Mark Clements, and Vitali Kalesnik
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; industry momentum is therefore a by-product of factor momentum, not vice versa. Factor momentum is a pervasive property of all factors; we show that factor momentum can be captured by trading almost any set of factors.
The earnings announcement return cycle
with Conson Zhang
Stocks earn significantly negative abnormal returns before earnings announcements and positive after them. This “earnings announcement return cycle” (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 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.
Alpha Letters / CQA Prize Winner at CQA Spring 2019
Informed traders, long-dated options, and the cross section of stock returns
with Mark Clements and Vitali Kalesnik
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.