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Big Ideas

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

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:

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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:

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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

Roll of Social Transmission Bias in Investing

Is the concept of emergent social behaviors useful in investing and trading? In his January 2020 address to the American Finance Association entitled “Social Transmission Bias in Economics and Finance”, David Hirshleifer discusses social economics and finance, a new field that examines how social processes shape economic and financial behaviors. This field is distinct from: (1) information economics (some people know more than others); and, (2) behavioral finance (people make systematic mistakes). He focuses on social transmission bias, systematic modification of signals or ideas between sender and receiver, as the key element of the new field. He employs five “fables” (models) to illustrate the novelty and importance of such bias. Based on his long experience in behavioral finance and recent/current studies, he concludes that:

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Tools for Defeating Data Snooping

Suppose ten stock market timing strategies out of 10,000 beat the market for ten years running. Are they true outperformers, or just lucky? Multiple hypothesis testing methods address that question by controlling for luck. What are these methods, and how should researchers use them? In their November 2019 paper entitled “An Evaluation of Alternative Multiple Testing Methods for Finance Applications”, Campbell Harvey, Yan Liu and Alessio Saretto:

  1. Address the scope of the multiple testing problem in empirical financial economics.
  2. Summarize multiple testing methods based on conventional (frequentist) hypothesis testing.
  3. Simulate performance of different methods across a variety of testing environments.

Their goal is to provide a menu of choices to help researchers improve inference in financial economics. Based on theory and simulations, they conclude that: Keep Reading

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