Working papers

Time-series efficient factors

with Sina Ehsani
March 2020

 
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

with Robert Arnott, Mark Clements, and Vitali Kalesnik
March 2021

 
Past industry returns predict future industry returns, and this predictability is at its strongest at the one-month horizon. We show that the cross section of factor returns shares this property and that industry momentum stems from factor momentum. Factor momentum transmits into the cross section of industry returns through variation in industries’ factor loadings. We show that momentum in “systematic industries,” mimicking portfolios built from factors, subsumes industry momentum as does momentum in industry-neutral factors. Industry momentum is therefore a byproduct of factor momentum, not vice versa. Momentum concentrates in its entirety in the first few highest-eigenvalue factors.





The earnings announcement return cycle

with Conson Zhang
January 2019

 
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.

Award: 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
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.