Investing Research Articles

A subscriber requested review of a finding that deviation of 10-year constant maturity U.S. Treasury note (T-note) yield from an intermediate-term linear trend predicts U.S. stock market return. Specifically, when weekly yield is more than one standard deviation of weekly trend divergences below (above) a weekly 70-week linear extrapolation, next-week S&P 500 Index return is… Keep Reading

Do technical trend trading/intrinsic momentum strategies work for widely used equity factors such as size (small minus big market capitalizations), value (high minus low book-to-market ratios), profitability (robust minus weak), investment (conservative minus aggressive) and momentum (winners minus losers)? In their January 2018 paper entitled “What Goes up Must Not Come Down – Time Series… Keep Reading

Is shorting pairs of leveraged exchange-traded funds (ETF) reliably profitable? In their December 2017 paper entitled “Shorting Leveraged ETF Pairs”, Christopher Hessel, Jouahn Nam, Jun Wang, Xing Cunyu and Ge Zhang examine monthly returns from shorting a pair of leveraged and inverse leveraged ETFs for the same index. They first investigate what circumstances make this strategy profitable. They then test… Keep Reading

Can advanced regression techniques identify monthly cross-industry lead-lag return relationships that usefully indicate an industry rotation strategy? In their January 2018 paper entitled “Dynamic Return Dependencies Across Industries: A Machine Learning Approach”, David Rapach, Jack Strauss, Jun Tu and Guofu Zhou examine dynamic relationships between past and future returns (lead-lag) across 30 U.S. industries. To guard against overfitting the data, they… Keep Reading

How do cryptocurrencies work, and how can investors acquire and hold them? In their January 2018 paper entitled “Crypto-Assets Unencrypted”, Seoyoung Kim, Atulya Sarin and Daljeet Virdi survey cryptocurrency history and technology. They summarize cryptocurrency market sizes, trading volumes and volatilities, with comparisons to major fiat currencies and commodities. They further discuss crypto-asset valuation, regulation and the mechanics of… Keep Reading

Are simple moving averages (SMA) useful for timing difficult-to-value Bitcoin? In their January 2018 paper entitled “Bitcoin: Predictability and Profitability Via Technical Analysis”, Andrew Detzel, Hong Liu, Jack Strauss, Guofu Zhou and Yingzi Zhu investigate the use of 5-day, 10-day, 20-day, 50-day or 100-day SMAs to predict Bitcoin returns. Specifically, they test a trading strategy that holds Bitcoins (cash) when… Keep Reading

In his January 2018 retrospective “Richard Thaler and the Rise of Behavioral Economics”, Nicholas Barberis reviews the development of behavioral (less than fully rational) models of economics and finance, with focus on Richard Thaler’s contributions. This retrospective summarizes key models that make psychology-based assumptions about: individual preferences; individual beliefs; and, the process by which individuals make decisions. He further segments… Keep Reading

Does appearance-based masculinity predict hedge fund manager performance? In their January 2018 paper entitled “Do Alpha Males Deliver Alpha? Testosterone and Hedge Funds”, Yan Lu and Melvyn Teo use facial width-to-height ratio (fWHR) as a positively related proxy for testosterone level to investigate the relationship between male hedge fund manager testosterone level and hedge fund performance. They each… Keep Reading

Are financial advisors expert guides for their client investors? In their December 2017 paper entitled “The Misguided Beliefs of Financial Advisors”, Juhani Linnainmaa, Brian Melzer and Alessandro Previtero compare investing practices/results of Canadian financial advisors to those of their clients, including trading patterns, fees and returns. They estimate account alphas via multi-factor models. Using detailed data from two large… Keep Reading

How can machine investors beat humans? In the introductory chapter of his January 2018 book entitled “Financial Machine Learning as a Distinct Subject”, Marcos Lopez de Prado prescribes success factors for machine learning as applied to finance. He intends that the book: (1) bridge the divide between academia and industry by sharing experience-based knowledge in a… Keep Reading