SSRN Author: Sina EhsaniSina Ehsani SSRN Content
https://privwww.ssrn.com/author=1904069
https://privwww.ssrn.com/rss/en-usTue, 12 May 2020 18:54:03 GMTeditor@ssrn.com (Editor)Tue, 12 May 2020 18:54:03 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: Factor Momentum and the Momentum FactorMomentum 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.
https://privwww.ssrn.com/abstract=3014521
https://privwww.ssrn.com/1896110.htmlTue, 12 May 2020 09:07:50 GMTNew: Time-Series Efficient FactorsFactors 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 ...
https://privwww.ssrn.com/abstract=3555473
https://privwww.ssrn.com/1881034.htmlWed, 01 Apr 2020 10:07:59 GMTREVISION: The Risk in Low-Variance AnomalyThe low variance (LV) strategy always bets against the volatile leg of common factor-portfolios. Factor loadings of the strategy are thus perfectly predictable based on the status of factor portfolio variances during the formation period. I find that the strategy earns alpha only when traders have to bear major factor risk to arbitrage it away: LV is an anomaly only when it is expected to bet on factor risk. In other times—when low variance means low factor risk—alpha is exactly zero. My results are consistent with models that rationalize anomalies by arbitrageurs reluctance to eliminate mispricing due to factor risk aversion. I use the findings to develop a trading strategy that uses factor data to time LV.
https://privwww.ssrn.com/abstract=3480257
https://privwww.ssrn.com/1857685.htmlTue, 14 Jan 2020 15:57:03 GMTREVISION: Decomposing the Volatility EffectA low variance (LV) strategy is a cross-sectional bet against variance plus a collection of time-series bets against common risk factor variances. I characterize and measure both components and show that the former produces profits while the latter generates volatility. The undesirable factor risk bets, however, secure the anomaly’s existence by acting as limits to arbitrage. LV earns alpha only when traders have to bear major factor risk to arbitrage it away. In other times—when low variance means low factor risk—the cross-section does not exhibit mispricing. My results are consistent with models that rationalize anomalies by arbitrageurs reluctance to eliminate mispricing due to factor risk aversion. I use the findings to develop a dynamic strategy that instruments variance return trade-off of common factors in time-series to trade variance in the cross-section.
https://privwww.ssrn.com/abstract=3480257
https://privwww.ssrn.com/1841428.htmlWed, 13 Nov 2019 15:23:19 GMTNew: Calculating Returns in the Secondary Corporate Loan MarketThis note describes in detail the methodology to calculate returns in the secondary corporate loan market. It is provided as a supplementary note to "The cross-section of expected returns in the secondary corporate loan market."
https://privwww.ssrn.com/abstract=3420435
https://privwww.ssrn.com/1807522.htmlTue, 16 Jul 2019 16:29:33 GMT