Vol. 17, No. 3, 2019 Arun Muralidhar Finance theory is based on a very simple, yet critical assumption that “individuals maximize the expected utility of wealth”. However, there are three crucial elements of this simple six-word phrase that does not really stand the test of what investors actually do and one could argue that the… Read more
Third Quarter (2019)
Book Review – The Son also Rises: Surnames and the History of Social Mobility
Volume 17, No. 3, 2019 By Gregory Clark (Reviewed by Savannah Smith) View PDF… Read more
Case Study – Do You Know the Provenance of Your Alternative Data
Vol. 17, No. 3, 2019 Seoyoung Kim View PDF… Read more
Practitioner’s Digest
Vol. 17, No. 3, 2019 Practitioner’s Digest View PDF… Read more
Bill Gross’Alpha: The King Versus the Oracle
Vol. 17, No. 3, 2019 Aaron Brown and Richard Dewey We set out to investigate whether “Bond King” Bill Gross demonstrated alpha (excess average return after adjusting for market exposures) over his career, in the spirit of earlier papers asking the same question of “Oracle of Omaha,” Warren Buffett. The journey turned out to be… Read more
Return Predictability and Market-Timing: A One-Month Model
Vol. 17, No. 3, 2019 Blair Hull, Xiao Qiao and Petra Bakosova We use weighted least squares to combine 15 diverse variables to build a predictive model for the one- month-ahead market excess returns.We transform our forecasts into investable positions to form a market-timing strategy. From 2003 to 2017, our strategy had 16.6% annual returns… Read more
Embedded Betas and Better Bets: Factor Investing in Emerging Market Bonds
Vol. 17, No. 3, 2019 Johnny Kang, Kevin So and Thomas Tziortziotis We document novel empirical insights driving the prices of sovereign external emerging market bonds. In the time series, we examine the market portfolio’s time-varying exposures to a broad set of macro factors (rates, credit, currency, and equity) and identify these embedded betas as… Read more
How to Beat the Machines Before They Beat You
Vol.17, No.3, 2019 Vineer Bhansali The use of “big” data, algorithms and machine learning is disrupting investment management. By carefully selecting domains where data is sparse and there is possibility of regime changes, a human investor can not only survive, but also thrive in a world of investment machines… Read more
Does Trading by ETF and Mutual Fund Investors Hurt Performance? Evidence from Time- and Dollar-Weighted Returns
Vol.17, No. 3, 2019 Ananth Madhavan and Aleksander Sobczyk This paper analyzes the “return gap” between internal rate of returns that account for intermediate investor flows (“dollar-weighted returns”) and more familiar buy-and-hold returns that funds typically must report. Our sample constitutes all US-domiciled open- end mutual funds and exchange-traded funds (ETFs), and covers both fixed… Read more