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Big Ideas

These blog entries offer some big ideas of lasting value relevant for investing and trading.

The State of Systematic (Algorithmic) Investing

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

Re-examining Equity Factor Research Replicability

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

Crypto Transformation of Finance?

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:

Keep Reading

Using Alternative Data for Investing

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:

Keep Reading

U.S. Economy and Equity Market Linkage Weakening?

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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Inelastic Markets Hypothesis

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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Performance of Yield Enhancement Products

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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Endemic Data Snooping in Smart Beta Offerings?

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

Open Source Stock Predictor Data and Code

Are published studies that predict higher returns for some U.S. stocks and lower for others based on firm accounting, stock trading and other data reproducible? In their May 2020 paper entitled “Open Source Cross-Sectional Asset Pricing”, Andrew Chen and Tom Zimmermann make available data and code that reproduce many published cross-sectional stock return predictors, allowing other researchers to modify and extend past studies. They commit to annual updates of their study. Defining statistical significance as achieving at least 95% confidence in predictive power, they include:

  • 180 clear predictors that exhibit statistical significance in original studies and are easily reproducible.
  • 30 likely predictors that exhibit statistical significance in original studies but are not precisely reproducible.
  • 315 additional predictors covered in past studies that were not clearly tested or failed, or are variations of these predictors. They further extend this group by separately testing 1-month, 3-month, 6-month and 12-month portfolio reformation frequencies (1,260 total tests).

They compute all predictors on a monthly basis and create for each a long-short portfolio based on the specifications and the sample period of its original study. They check predictive power of each using data available at the end of each month to evaluate long-short portfolio returns the next month. They assume a 6-month lag for availability of annual accounting data and a 1-quarter lag for quarterly accounting data. They make no attempt to account for portfolio reformation frictions or to winnow predictors based on similarity. Using data and sample periods for U.S. firms/stocks as specified in original published studies as described above, they find that: Keep Reading

Maximum Drawdown as Portfolio/Strategy Performance Metric

How should investors think about maximum drawdown (MaxDD) as a portfolio/strategy performance metric? In their April 2020 paper entitled “Drawdowns”, Otto Van Hemert, Mark Ganz, Campbell Harvey, Sandy Rattray, Eva Martin and Darrel Yawitch examine usefulness of MaxDD for portfolio/strategy performance evaluation. They first quantify how MaxDD relates to key return statistics based on 100,000 simulations of monthly returns for each variation. They then investigate use of MaxDD for detecting portfolio/strategy failure due to strategy crowding or other market changes. Finally, they assess MaxDD-based rules for portfolio risk reduction. Using pure simulations and simulations based on actual U.S. stock market monthly returns since 1926, they find that: Keep Reading

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