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Big Ideas

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

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

Testing Tactical Investment Rules

How can investment strategy researchers best address the randomness inherent in market data and the ability of investors/markets to adapt to changing conditions? In his September 2019 paper entitled “Tactical Investment Algorithms”, Marcos Lopez de Prado reviews three methods for testing the performance of an investment rule:

  1. Walk-forward (WF) tests a rule against an actual historical data series, implicitly assuming that market behaviors are neither largely random nor changing (that the rule being tested is “all-weather”).
  2. Resampling (RS) addresses randomness in market behaviors by assuming that resampling of past observations can usefully generate possible future price paths. 
  3. Monte Carlo (MC) addresses both randomness and adaptation in market behaviors by simulating possible future price paths based on models of price generation derived from theory and statistical analysis of actual data.

Based on his knowledge of financial markets and testing methods, he concludes that: Keep Reading

Investment Strategy Development Tournaments?

Is there a way that asset managers can share knowledge/data across proprietary boundaries with many researchers to advance development of investment strategies? In their September 2019 paper entitled “Crowdsourced Investment Research through Tournaments”, Marcos Lopez de Prado and Frank Fabozzi describe highly structured tournaments as a crowdsourcing paradigm for investment research. In each such tournament, the organizer poses one investment challenge as a forecasting problem and provides abstracted and obfuscated data. Contestants pay an entry fee, develop models and provide forecasts, retaining model ownership by running calculations on their own hardware/software. Based on this hypothetical tournament setup and their experience, they conclude that:

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Compendium of Recent “Long Run” Research

The following list links to summaries of recent (since 2010) investment research using long data samples. These summaries may be helpful in developing strategic allocations and tactical wariness for long-horizon investments.

Another long run source is the annual update of the work summarized in Triumph of the Optimists (Chapter-by-Chapter Review).

Some general cautions regarding such studies are:

  • Reconstruction of price series from, for example, old newspapers involves missing data and potentially inconsistent reporting approaches. In other words, quality of old data is suspect.
  • The number of asset class price series available may be small in early parts of sample periods.
  • Some studies may impound survivorship bias via omission of assets that were important in the past but are no longer tracked in source databases.
  • For studies using Shiller data, monthly levels are averages of monthly values, blurring monthly statistics and modestly blurring annual statistics. Results based on end-of-month values may differ.
  • Reported returns are gross, not net. Accounting for costs of maintaining a tracking fund for a portfolio/index of commodities would reduce returns. Also:
    • Studies involving shorting (such as factor premium analyses) typically do not address the cost/feasibility of shorting.
    • Costs of maintaining tracking funds may vary by asset class, by country and over time, confounding comparisons. For example, commodity futures indexes generally assume monthly rolling of many contract series.
    • Investment capacities of some assets may be especially limited early in sample periods.
    • Tax consequences of trading vary considerably across countries and over time.
  • Historical timeliness of data collection/processing for periodic trading (for example, for portfolio rebalancing) may be especially problematic in early parts of sample periods.
  • Economies and markets change over time, making it difficult to assess the relative importance of older versus newer data.
  • Distant past availability of retrospectively constructed indexes may have altered contemporaneous market behaviors (induced market adaptations).

Systemic Risk Impacts of Growth in Passive Investing

How does a shift in emphasis from active to passive investing affect the financial market risk landscape? In their September 2019 paper entitled “The Shift From Active to Passive Investing: Potential Risks to Financial Stability?”, Kenechukwu Anadu, Mathias Kruttli, Patrick McCabe, Emilio Osambela and Chaehee Shin analyze how a shift from active to passive investing affects:

  1. Investment fund redemption liquidity risks.
  2. Market volatility.
  3. Asset management industry concentration.
  4. Co-movement of asset returns and liquidity.

They also assess how effects are likely to evolve if the active-to-passive shift continues. Based on their framework/analysis, they conclude that: Keep Reading

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