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Investing Research Articles

Beta Across Return Measurement Intervals

Is there a distinct systematic asset risk, as measured by its market beta, associated with each return measurement interval (frequency, such as daily, monthly or annually)? In other words, is return measurement frequency a risk factor? In their October 2018 paper entitled “Measuring Horizon-Specific Systematic Risk via Spectral Betas”, Federico Bandi, Shomesh Chaudhuri, Andrew Lo and Andrea Tamoni  introduce spectral beta, an asset’s market beta for a given return measurement frequency, as a way to assess this frequency as a source of systematic investment risk. They specify how to combine spectral betas into an overall beta and explore ways to interpret and exploit spectral betas. Using mathematical derivations and samples of monthly and daily returns for broad samples of U.S. stocks and stock portfolios, they find that: Keep Reading

Weekly Summary of Research Findings: 12/3/18 – 12/7/18

Below is a weekly summary of our research findings for 12/3/18 through 12/7/18. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

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It Can’t All Be Data Snooping?

Is it possible that all the 300+ published factors that predict stock returns (such as size, value, profitability, investment, momentum…) derive from data snooping? In his October 2018 paper entitled “The Limits of Data Mining: A Thought Experiment”, Andrew Chen estimates how much data snooping would be required to “discover” all these factors by pure luck. Specifically, he calibrates a pure luck model built on the assumption that the probability of publishing a factor discovery increases with the degree to which the discovery is convincing (t-statistic). Using this model, he estimates the number of unpublished factor studies required for the published set to be attributable to pure luck. He considers two sets of factor t-statistics: 156 from factor replications via equal-weighted long-short extreme fifths (quintiles) of factor stock sorts; and, a hand-collected set of 316 from published factor studies. Using the specified approach and these two sets of t-statistics, he finds that: Keep Reading

Curbing Data Snooping

How should researchers applying machine learning to quantitative finance address the field’s data limitations, which exacerbate data snooping bias? In their October 2018 paper entitled “A Backtesting Protocol in the Era of Machine Learning”, Robert Arnott, Campbell Harvey and Harry Markowitz take a step back and re-examine financial markets research methods, with focus on suppressing backtest overfitting of investment strategies. They introduce a research protocol recognizing that self-deception is easy. Their goal is that the protocol offers the best way to match or beat expectations in live trading. Based on logic and their collective experience, they conclude that: Keep Reading

Personal Trading Performance of Financial Intermediaries

Do employees of financial intermediaries such as brokers, financial analysts and fund managers take advantage of their access to private information? In their March 2018 paper entitled “Personal Trading by Brokers, Analysts, and Fund Managers”, Henk Berkman, Paul Koch and Joakim Westerholm examine the personal trading of employees at Finnish financial intermediaries (experts) who have regular access to material private information. In Finland, regulations require that these experts disclose personal trades in any stock listed on the Nasdaq OMX Helsinki Exchange. Using  personal trading data for 1,249 experts at 40 Finnish financial intermediaries representing 90% of the Finnish fund management industry and 99% of the Finnish brokerage industry, plus aggregated trading data of Finnish retail investors, during August 2006 through August 2011, they find that: Keep Reading

Testing ETF Momentum/Reversal Strategies

Do exchange-traded funds (ETF) exhibit statistically reliable short-term reversal and intermediate-term momentum? In their October 2018 paper entitled “Momentum Strategies for the ETF-Based Portfolios”, Daniel Nadler and Anatoly Schmidt look for reversal and momentum in next-month performance of past winners and past losers for the following 13 universes:

  • U.S. Equity ETFs: 28 US equity ETFs with returns available at the beginning of 2006.
  • Multi-Asset Class ETFs: U.S. Equity ETFs plus one gold ETF, five international equity ETFs and five bond ETFs, also with returns available at the beginning of 2006.
  • 11 U.S. Equity ETF Proxies: formed separately from the stock holdings as of January 2018 of each of SPDR S&P 500 (SPY), PowerShares NASDAQ 100 (QQQ) or one of the nine Select Sector SPDRs.

Every day for each universe, they reform overlapping winner (loser) portfolios consisting of the equally weighted tenth (decile) of assets with the highest (lowest) total returns over the past 21, 63, 126 or 252 trading days and hold for 21 trading days. They consider two test periods: 2007 through 2017, and 2011 through 2007. They use equal-weighted portfolios of all assets in each universe as the benchmark for that universe. They conclude that one portfolio beats another when the difference between average 21-day future returns is statistically significant with p-value less than 0.10. Using daily returns for the specified assets during 2006 through 2017, they find that: Keep Reading

Free Data and the Collapse of Trading Costs

How have costs of U.S. stock trading data evolved in recent years? In his October 2018 paper entitled “Retail Investors Get a Sweet Deal: The Cost of a SIP of Stock Market Data”, James Angel examines costs of U.S. stock market data. He also describes the production of these data and their consolidation/distribution via Securities Information Processors (SIP). Using data for U.S. trading costs as far back as 1987, he finds that:

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Weekly Summary of Research Findings: 11/26/18 – 11/30/18

Below is a weekly summary of our research findings for 11/26/18 through 11/30/18. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

SACEMS-SACEVS Mutual Diversification

Are the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) mutually diversifying. To check, we look at the following three equal-weighted (50-50) combinations of the two strategies, rebalanced monthly:

  1. SACEVS Best Value paired with SACEMS Top 1 (aggressive value and aggressive momentum).
  2. SACEVS Best Value paired with SACEMS Equally Weighted (EW) Top 3 (aggressive value and diversified momentum).
  3. SACEVS Weighted paired with SACEMS EW Top 3 (diversified value and diversified momentum).

We also test sensitivity of results to deviating from equal SACEVS-SACEMS weights. Using monthly gross returns for SACEVS and SACEMS portfolios since January 2003 for the first strategy and since July 2006 for the latter two, all through October 2018, we find that: Keep Reading

How Financial Journalists Work

How do journalists develop the information that appears in the financial media? In their November 2018 paper entitled “Meet the Press: Survey Evidence on Financial Journalists As Information Intermediaries”, Andrew Call, Scott Emett, Eldar Maksymov and Nathan Sharp report results of a survey of and follow-up interviews with financial journalists on inputs, incentives and beliefs that shape their reporting. Using 462 responses to a 14-question survey (emailed to 4,590 financial journalists) received during April 3, 2018 to May 3, 2018 and 18 follow-up interviews, they find that:

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