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Cambridge Endowment for Research in Finance (CERF)

 
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Thu 24 Nov 13:00: Title to be confirmed

Wed, 21/09/2022 - 16:24
Title to be confirmed

Abstract to be confirmed

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Thu 13 Oct 13:00: Comparing factor models with conditioning information

Wed, 21/09/2022 - 15:40
Comparing factor models with conditioning information

We develop a framework to conduct asymptotically valid tests for comparing factor models with conditioning information. The tests are based on a metric analogous to the squared Sharpe ratio improvement measure that is used to gauge the extent of model mispricing in an unconditional setting. We propose an estimator for the metric and study its limiting properties in detail, establishing the asymptotic normality. An advantage of our framework is that it can be applied without an a priori knowledge of the persistence nature of the conditioning variables. We accommodate a range of dependence classes, including stationary, near stationarity, integrated, and local-to-unity. An application of our methodology to major models shows that the conditional versions of the Stambaugh and Yuan (2017) four-factor model and the Daniel, Hirshleifer, and Sun (2020) three-factor model are the best performers.

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Thu 27 Oct 13:00: Momentum and Short-Term Reversals: Theory and Evidence

Wed, 21/09/2022 - 15:40
Momentum and Short-Term Reversals: Theory and Evidence

How might short-term reversals and longer-term momentum coexist within markets? To address this question, we develop a dynamic model with liquidity demands and information*processing constraints. Specifically, we consider noise traders, and investors who under*estimate the quality of information they do not themselves produce. Markets transition from reversals to momentum as lag horizons lengthen. Reversals weaken following earn*ings announcements. Skipping a month between formation and holding periods increases momentum profits. Larger order flows from retail traders imply stronger reversals. These predictions are supported empirically. If noise demands are positively autocorrelated, price buildups and collapses occur as in recent “meme” stock episodes.

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Thu 13 Oct 13:00: Comparing factor models with conditioning information

Thu, 18/08/2022 - 12:00
Comparing factor models with conditioning information

We develop a framework to conduct asymptotically valid tests for comparing factor models with conditioning information. The tests are based on a metric analogous to the squared Sharpe ratio improvement measure that is used to gauge the extent of model mispricing in an unconditional setting. We propose an estimator for the metric and study its limiting properties in detail, establishing the asymptotic normality. An advantage of our framework is that it can be applied without an a priori knowledge of the persistence nature of the conditioning variables. We accommodate a range of dependence classes, including stationary, near stationarity, integrated, and local-to-unity. An application of our methodology to major models shows that the conditional versions of the Stambaugh and Yuan (2017) four-factor model and the Daniel, Hirshleifer, and Sun (2020) three-factor model are the best performers.

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Thu 04 May 13:00: Title to be confirmed

Wed, 17/08/2022 - 12:01
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Abstract not available

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Thu 09 Feb 13:00: Title to be confirmed

Wed, 17/08/2022 - 12:00
Title to be confirmed

Abstract not available

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