Academic Research Insight

Momentum Factor Investing: 30 years of Out of Sample Data

By |October 31st, 2022|Research Insights, Basilico and Johnsen, Academic Research Insight, Momentum Investing Research|

In this article, the author examines the research published over the last 30 years on momentum and its theoretical credibility. One of the original momentum articles was published by Jegadeesh and Titman in 1993, and is considered the seminal work on the topic. The research review contained in this publication begins with the 1993 work and confines itself to only the highest quality journals among the plethora of work that has been published on momentum.

The Short-Duration Equity Premium

By |September 15th, 2022|Larry Swedroe, Research Insights, Factor Investing, Academic Research Insight, Low Volatility Investing|

We examine the short-duration premium using pre-scheduled economic, monetary policy, and earnings announcements. We provide high-frequency evidence that duration premia associated with revisions of economic growth and interest rate expectations are consistent with asset pricing models but cannot explain the short-duration premium. Instead, we show that the trading activity of sentiment-driven investors raises prices of long-duration stocks, which lowers their expected returns, and results in the short-duration premium. Long-duration stocks have the lowest institutional ownership, exhibit the largest forecast errors at earnings announcements, and show the highest mispricing scores.

Brand Values and Long-Term Stock Returns

By |September 8th, 2022|Intangibles, Smarter in 10 Minutes, Factor Investing, Larry Swedroe, Academic Research Insight|

An equal-weighted portfolio of Best Brands (BBs) in the U.S. earns an excess return of 25 to 35 bps per month during the period 2000-2020. This result is remarkably robust across various factor models and therefore is not driven by exposure to common (risk) factors. The excess returns of the BB portfolio are not due to firm characteristics, industry composition, or small-cap stocks. We provide evidence suggesting that expensing investments in brands (instead of capitalizing them) and the tendency to underestimate the effect of brand name on generating future earnings are two mechanisms contributing to the excess returns.

ESG Ratings how do they Compare Across Data Providers?

By |September 7th, 2022|ESG, Research Insights, Basilico and Johnsen, Academic Research Insight|

Investments aligned with environmental, social, and governance (ESG) principles are rapidly growing globally. In the exchange traded fund (ETF) industry, this gives rise to the power of ESG rating firms that have the influence to direct capital flows into ETFs tracking the indexes. This article examines the issues of substantial ESG rating divergence across rating firms, the impact on investors’ choices, and the influence on the ETF industry. The divergence appears to be the greatest in social and governance components, and is often qualitative in nature. The author found that certain economic sectors are more prone to ESG rating divergence than others. She presents a case study about two ESG ETFs that are viewed quite differently under various rating lenses, and offers suggestions to investors, advisors, and analysts on how to research ESG ETFs, given the major rating divergence. The article concludes with ways the ETF industry could improve its practices collectively to better serve investors with clarity and to sustain the growth of ESG impact investments.

How You Sort Matters in Sorting Factor Portfolios

By |September 1st, 2022|Research Insights, Factor Investing, Larry Swedroe, Academic Research Insight|

Non-standard errors capture uncertainty due to differences in research design choices. We establish substantial variation in the design choices made by researchers when constructing asset pricing factors. By purposely data mining over two thousand different versions of each factor, we find that Sharpe ratios exhibit substantial variation within a factor due to different construction choices, which results in sizable non-standard errors and allows for p-hacking. We provide simple suggestions that reduce the average non-standard error by 70%. Our study has important implications for model selection exercises.

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