Vol. 20, No. 1, 2022 by Seoyoung Kim View PDF… Read more
First Quarter (2022)
Book Review: In Pursuit of the Perfect Portfolio
Vol. 20, No. 1, 2022 by Andrew Lo and Stephen Foerster (Reviewed by Megan Czasonis) View PDF… Read more
Vol. 20, No. 1, 2022 View PDF… Read more
Exponential Glide Paths
Vol. 20, No. 1, 2022 by Moshe Levy and Haim Levy In the absence of market-timing ability, investors are better-off keeping their asset allocation constant through time. Target-date funds help reduce variation in the asset allocation, by taking into account that human capital, which is a part of the investor’s total portfolio and is typically… Read more
How Well Do Factor ETFS Capture the FAMA–French Factors?
Vol. 20, No. 1, 2022 by Nicholas Apergis, Thomas Poufinas, Alexandros Panagakis and Ioannis Ritsios Institutional investors are investigating systematic, rule-based investment directions other than purely passive investing, such as factor-based investing. This study examines how well the factor-ETFs capture the Fama–French factors and attempts to explain their difference from the smart beta indexes applied… Read more
Measuring the Economic and Academic Impact of Philanthropic Funding: The Breast Cancer Research Foundation
Vol. 20, No. 1, 2022 by Detelina Vasileva, Larry Norton, Marc Hurlbert and Andrew W. Lo Using survey data gathered from grantees of the nonprofit Breast Cancer Research Foundation (BCRF), we investigated the commercial and non-commercial impacts of their research funding. We found significant impact in both domains. Commercially, 19.5% of BCRF grantees filed patents… Read more
Characteristic-Based Returns: Alpha or Smart Beta?
Vol. 20, No. 1, 2022 by Soohun Kim, Robert A. Korajczyk and Andreas Neuhierl We propose new methodology to construct arbitrage portfolios by utilizing information contained in firm characteristics for both abnormal returns and betas (and, therefore, smart-beta risk premiums). Our methodology gives maximal weight to risk-based interpretations of characteristics’predictive power before any attribution to… Read more
Vol. 20, No. 1, 2022 by Megan Czasonis, Mark Kritzman and David Turkington The authors describe a new statistical method for improving forecasting called relevance. They describe their new method from both a conceptual and mathematical perspective, and they show how relevance links regressions to event studies and machine learning algorithms… Read more