Big Ideas

Does failure to replicate dampen interest in previously published research? In their May 2021 paper entitled “Non-replicable Publications Are Cited More Than Replicable Ones”, Marta Serra-Garcia and Uri Gneezy use results of three recent replication studies to compare citation rates for papers published in top psychology, economics and general science journals that fail to replicate versus those that do replicate. Replication rates in those past studies are 39% in psychology (replication study published 2015), 61% in economics (replication study published 2016) and 62% in general science (replication study published 2018). Strengths of replicated findings compared to original study findings are 75% for those that do replicate and 0% for those that do not replicate. The authors look at citation rates before and after publication of associated replication studies and also assess the nature/potential impact of citations. Using citations of the studied papers through 2019, they find that: Keep Reading

Is the known weakening of stock anomalies after publication due more to in-sample overfitting by researchers or post-publication exploitation by arbitrageurs (market adaptation)? In their May 2021 paper entitled “Why and How Systematic Strategies Decay”, Antoine Falck, Adam Rej and David Thesmar examine the typical post-publication risk-adjusted performance (Sharpe ratio) of U.S. stock anomalies. They include only anomalies published through 2010 to allow significant out-of-sample testing in their dataset that ends in 2014. In general, their anomaly return calculations: (1) are based on long-short portfolios of top minus bottom tenth (decile) of anomaly variable sorts; (2) assume that annual variables are available four months after fiscal year end; and, (3) are market beta-hedged based on 36-month rolling betas. They consider date of publication, six proxies for in-sample overfitting and four proxies for ease of anomaly exploitation (arbitrage) to explain weaker post-publication performance. Using a sample of 72 published investment strategies as applied to U.S. stocks during January 1963 through April 2014 and as applied to international stocks as available during January 1995 through December 2018, they find that:

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How has systematic investment, with trades generated by rules or algorithms, evolved? What are its strengths and weaknesses? In his February 2021 paper entitled “Why Is Systematic Investing Important?”, Campbell Harvey summarizes the history, advantages and disadvantage of systematic (algorithmic) investing. Based on the body of research and personal experience, he concludes that: Keep Reading

Several recent papers find that most studies identifying factors that predict stock returns are not replicable or derive from snooping of many factors. Is there a good counter-argument? In their January 2021 paper entitled “Is There a Replication Crisis in Finance?”, Theis Ingerslev Jensen, Bryan Kelly and Lasse Pedersen apply a Bayesian model of factor replication to a set of 153 factors applied to stocks across 93 countries. For each factor in each country, they each month:

1. Sort stocks into thirds (top/middle/bottom) with breakpoints based on non-micro stocks in that country.
2. For each third, compute a “capped value weight” gross return (winsorizing market equity at the NYSE 80th percentile to ensure that tiny stocks have tiny weights no mega-stock dominates).
3. Calculate the gross return for a hedge portfolio that is long (short) the third with the highest (lowest) expected return.
4. Calculate the corresponding 1-factor gross alpha via simple regression versus the country portfolio.

They further propose a taxonomy that systematically assigns each of the 153 factors to one of 13 themes based on high within-theme return correlations and conceptual similarities. Using firm and stock data required to calculate the specified factors starting 1926 for U.S. stocks and 1986 for most developed countries (in U.S. dollars), and 1-month U.S. Treasury bill yields to compute excess returns, all through 2019, they find that: Keep Reading

How might crypto-assets transform finance? In their December 2020 paper entitled “DeFi and the Future of Finance”, Campbell Harvey, Ashwin Ramachandran and Joey Santoro examine the potential for decentralized finance (DeFi) to disrupt traditional financial infrastructure. They summarize origins and essential features of DeFi, its potential to improve traditional finance and its risks. They also speculate on future development of DeFi. Based on a review of relevant research and events, they conclude that:

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Many institutional investors are attempting to exploit alternative data (less structured and more obscure than traditional data) to boost portfolio performance, supporting a complex system of data collectors, aggregators and organizers. How do they approach this potential edge? In their October 2020 paper entitled “Alternative Data in Investment Management: Usage, Challenges and Valuation”, Gene Ekster and Petter Kolm describe the alternative data ecosystem. They identify and discuss obstacles and emerging best practices in applying alternative data for investing purposes. They illustrate potential effectiveness of alternative data methods via a healthcare industry example. Based on review of current alternative data examples/obstacles/practices and samples of daily medical purchasing activity by 778 U.S. healthcare facilities (2.6% of all such facilities) during 2015 through 2017, they conclude that:

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How connected are principal measures of U.S. economic activity and U.S. stock market performance? In their October 2020 paper entitled “Has the Stock Market Become Less Representative of the Economy?”, Frederik Schlingemann and René Stulz model and measure relationships between market capitalizations of U.S. publicly listed firms and their contributions to U.S. employment and Gross Domestic Product (GDP). They estimate employment contribution directly based on firm reports, with modeled adjustments. They measure contribution to GDP based on firm value-add, approximated as operating income before depreciation plus labor costs (with labor costs often modeled). They also try other ways of measuring value-add. Using annual non-farm employment and GDP data for the U.S., annual employment and value-add data for U.S. publicly listed firms and annual stock prices for those firms during 1973 (limited by firm employment data) through 2019, they find that:

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Is aggregate U.S. stock market value sensitive to flows of new funds (inelastic)? In their October 2020 paper entitled “In Search of the Origins of Financial Fluctuations: The Inelastic Markets Hypothesis”, Xavier Gabaix and Ralph Koijen analyze aggregate stock market fluctuations in relation to flows of money into and out of stocks by different investor categories. They key on difficulties in satisfying demand for stocks/cash when money enters/exits the market. For example, institutions have reasonably rigid equity allocations, many individuals exhibit strong buy-and-hold inertia and hedge funds are not large enough to accommodate large inflows. In other words, households and their institutional proxies require considerable incentive to deviate from established equity allocations. As a result, relative modest flows have large impacts on prices (are inelastic). They further analyze how key tenets of macro-finance change if, in contrast to conventional belief, markets are inelastic. Using data on money flows to/from U.S. stocks across different investor categories as available during 1993 through 2018, and contemporaneous U.S. stock market level, they find that:

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Should investors buy yield enhancement products (YEP), which typically offer higher-than-market yields from a package comprised of an underlying stock or equity index and a series of short put options? In the August 2020 version of her paper entitled “Engineering Lemons”, Petra Vokata examines gross and net performances of YEPs, which embed fees as a front-end discount (load) allocated partly to issuers and partly to distributing brokers as a commission. Using descriptions of underlying assets and cash flows before and at maturity for 28,383 YEPs linked to U.S. equity indexes or stocks and issued between January 2006 and September 2015, and contemporaneous Cboe S&P 500 PutWrite Index (PUT) returns as a benchmark, she finds that:

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Do returns for “smart beta” indexes, constructed to exploit research on one or more factors that predict individual stock returns, reliably predict returns for exchange-traded funds (ETF) introduced to track them? In the June 2020 version of their preliminary paper entitled “The Smart Beta Mirage”, Shiyang Huang, Yang Song and Hong Xiang compare returns of smart beta indexes before and after listings of corresponding smart beta ETFs (see the illustration below). They then explore four potential explanations of differences: (1) offeror timing of ETF introduction based on underlying factor performance, (2) offeror timing of ETF introduction based on underlying index performance, (3) long-term trends in factor premiums and (4) data snooping bias. Using introduction dates for 238 U.S. single-factor and multi-factor equity smart beta ETFs listed between 2000 to 2018 and price data for matched smart beta indexes as available through December 2019, they find that: Keep Reading