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True vs. Snooped Sharpe Ratios

Data snooping bias is pervasive in published research and quantitative investment strategies. Should investors resign themselves to the consequence that investment managers/funds offer products picked mostly on past luck? In his May 2018 presentation package entitled “How the Sharpe Ratio Died, and Came Back to Life”, Marcos Lopez de Prado introduces an approach to Sharpe ratio estimation via backtesting that would enable academia, regulators and investors to distinguish between strategies that probably work and those that probably do not. Based on the evolution of Sharpe ratio estimation approaches, he concludes that: Keep Reading

Stock Returns Around Memorial Day

Does the Memorial Day holiday signal any unusual U.S. stock market return effects? By its definition, this holiday brings with it any effects from three-day weekends and sometimes the turn of the month. Prior to 1971, the U.S. celebrated Memorial Day on May 30. Effective in 1971, Memorial Day became the last Monday in May. To investigate the possibility of short-term effects on stock market returns around Memorial Day, we analyze the historical behavior of the stock market during the three trading days before and the three trading days after the holiday. Using daily closing levels of the S&P 500 Index for 1950 through 2017 (68 observations), we find that: Keep Reading

Weekly Summary of Research Findings: 5/14/18 – 5/18/18

Below is a weekly summary of our research findings for 5/14/18 through 5/18/18. 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

Ziemba Party Holding Presidency Strategy Update

“Exploiting the Presidential Cycle and Party in Power” summarizes strategies that hold small stocks (large stock or bonds) when Democrats (Republicans) hold the U.S. presidency. How has this strategy performed in recent years? To investigate, we consider three strategy alternatives using exchange-traded funds (ETF):

  1. D-IWM:R-SPY: hold iShares Russell 2000 (IWM) when Democrats hold the presidency and SPDR S&P 500 (SPY) when Republicans hold it.
  2. D-IWM:R-LQD: hold IWM when Democrats hold the presidency and iShares iBoxx Investment Grade Corporate Bond (LQD) when Republicans hold it.
  3. D-IWM:R-IEF: hold IWM when Democrats hold the presidency and iShares 7-10 Year Treasury Bond (IEF) when Republicans hold it.

We use calendar years to determine party holding the presidency. As benchmarks, we consider buying and holding each of SPY, IWM, LQD or IEF and annually rebalanced portfolios of 60% SPY and 40% LQD (60 SPY-40 LQD) or 60% SPY and 40% IEF (60 SPY-40 IEF). We consider as performance metrics: average annual excess return (relative to the yield on 1-year U.S. Treasury notes at the beginning of each year); standard deviation of annual excess returns; annual Sharpe ratio; compound annual growth rate (CAGR); and, maximum annual drawdown (annual MaxDD). We assume portfolio switching/rebalancing frictions are negligible. Except for CAGR, computations are for full calendar years only. Using monthly dividend-adjusted closing prices for the specified ETFs during July 2002 (limited by LQD and IEF) through April 2018, we find that:

Keep Reading

Diversify with Crypto-assets?

Should investors consider adding crypto-assets to portfolios of traditional assets? In their April 2018 paper entitled “Cryptocurrencies as an Asset Class?”, Sinan Krueckeberg and Peter Scholz investigate whether cryptocurrencies (crypto-assets) qualify as a distinct asset class, attractively diversifying portfolios of traditional asset classes. They distinguish between cryptographic coins (with their own blockchains) and tokens (using third party blockchains). They focus on the 10 coins and tokens with the largest market capitalizations as of December 8, 2017 that have at least three months of prices. Their investigation involves:

  • Computing three types of pairwise correlations between crypto-assets and between crypto-assets and traditional assets to measure crypto-asset uniqueness.
  • Comparing trading volumes and ratios of trading volume to market capitalization to those for several large stocks to assess liquidity.
  • Measuring frequencies of crypto-asset market circuit breaker trips and limit up/down triggers to assess stability.
  • Adding crypto-assets to traditional portfolios with quarterly rebalancing to test impact on Sharpe ratio with ex-post (perfect foresight) optimization and three approaches to ex-ante allocation (the simplest a fixed 1% allocation to crypto-assets). Traditional portfolios consist of stocks and bonds, plus (progressively) real estate, gold and oil.

Using daily price data for the top 10 coins/tokens and for traditional asset class proxies, and tick-by-tick crypto-asset price data to assess stability, during late April 2013 through early November 2017, they find that:

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Interplay of the Dollar, Gold and Oil

What is the interplay among investable proxies for the U.S. dollar, gold and crude oil? Do changes in the value of the dollar lead those in hard assets? To investigate, we relate the return series of three exchange-traded funds: (1) the futures-based PowerShares DB US Dollar Index Bullish (UUP); (2) the spot-based SPDR Gold Shares (GLD); and, (3) the spot-based United States Oil (USO). Using monthly, weekly and daily prices for these funds during March 2007 (limited by inception of UUP) through April 2018 (134 months), we find that: Keep Reading

Estimating the Level of, and Correcting for, Snooping Bias

Is there a tractable way of estimating the level of data snooping bias in investment strategy studies and thereby correcting for it? In their April 2018 paper entitled “Detection of False Investment Strategies Using Unsupervised Learning Methods”, Marcos Lopez de Prado and Michael Lewis summarize and validate an approach for estimating snooping bias derived from backtesting multiple strategies on the same data and using that estimate to correct for the bias. The approach involves estimating the overall scope and dispersion of multiple backtests based on correlation clusters within known backtests. Focusing on Sharpe ratio as the key performance metric, they validate their approach via Monte Carlo simulations. Based on derivations and simulations, they conclude that: Keep Reading

Unique U.S Equity ETF Seasonalities?

Do exchange-traded funds (ETF) exhibit unique calendar-based anomalies? In their April 2018 paper entitled “Evidence of Idiosyncratic Seasonality in ETFs Performance”, flagged by a subscriber, Carlos Francisco Alves and Duarte André de Castro Reis investigate calendar-based patterns of risk-adjusted returns and tracking errors for U.S. equity ETFs and compare findings to those of underlying indexes. They aggregate returns of their ETF sample via equal weighting. They consider returns calculated based on either market price or Net Asset Value (NAV). For risk adjustment, they consider alpha from either 1-factor (market) or 4-factor (market, size, book-to-market, momentum) risk models of stock returns. They look for raw return or alpha patterns in calendar months, calendar quarters, months of calendar quarters, calendar half-years, days before holidays (New Year’s Day, Martin Luther King Jr. Day, George Washington’s Birthday, Good Friday, Memorial Day, Independence Day, Labor Day, Thanksgiving and Christmas), days of the week and turn-of-the-month (last trading day of a month through three trading days of the next month). Using daily prices and NAVs for 148 index-tracking U.S. equity ETFs and associated indexes, and contemporaneous equity factor model returns, during December 2004 through December 2015 (11 years), they find that:

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Weekly Summary of Research Findings: 5/7/18 – 5/11/18

Below is a weekly summary of our research findings for 5/7/18 through 5/11/18. 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

Party in Power and Stock Returns

Past research relating U.S. stock market returns to the party holding the Presidency mostly concludes that Democratic presidents are better for the stock market than Republican presidents. However, the President shares power conferred by the electorate with Congress. Does historical data confirm that Democratic control of Congress is also better for stock market returns than Republican control of Congress? Is control of the smaller Senate more decisive than control of the House of Representatives? To check, we relate annual U.S. stock market (S&P 500 Index) returns to various combinations of party control of the Presidency, the Senate and the House of Representatives. Using party in power data and annual levels of the S&P 500 Index for 1950 through 2017 (68 years), we find that: Keep Reading

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