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Technical Trading of Equity Factor Premiums

Do technical trend trading/intrinsic momentum strategies work for widely used equity factors such as size (small minus big market capitalizations), value (high minus low book-to-market ratios), profitability (robust minus weak), investment (conservative minus aggressive) and momentum (winners minus losers)? In their January 2018 paper entitled “What Goes up Must Not Come Down – Time Series Momentum in Factor Risk Premiums”, Maximilian Renz investigates time variation and trend-based predictability of these five factors and the market factor. He first constructs price series for the six long-short factor portfolios. He then considers seven rules based on a short simple moving average (SMA) crossing above (bullish) or below (bearish) a long SMA measured in trading days: SMA(1, 20), SMA(1, 40), SMA(1, 120), SMA(1, 180), SMA(1, 240), SMA(20, 180) and SMA(20, 240). He also considers two intrinsic (absolute or time series) momentum rules based on change in price over the past 180 or 240 trading days (positive bullish and negative bearish). Motivated by prior research by others, he focuses on SMA(1, 180), daily price crossing its 180-day SMA. He measures trend-based statistical predictability of factor premiums and investigates economic value via a strategy that levers factor exposures between 0 and 1.5 using trend-based signals. Finally, he examines whether incorporating trend information improves accuracies of 1-factor (market), 3-factor (adding size and value) and 5-factor (further adding profitability and investment) models of stock returns. Using daily returns for the six selected U.S. stock market equity factors and for 30 industries during July 1963 through December 2015, he finds that: Keep Reading

Weekly Summary of Research Findings: 3/5/18 – 3/9/18

Below is a weekly summary of our research findings for 3/5/18 through 3/9/18. 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

EFFR and the Stock Market

Do changes in the Effective Federal Funds Rate (EFFR), the actual cost of short-term liquidity derived from a combination of market demand and Federal Reserve open market operations designed to maintain the Federal Funds Rate (FFR) target, predictably influence the U.S. stock market over the intermediate term? To investigate, we relate smoothed (volume-weighted median) monthly levels of EFFR to monthly U.S. stock market returns (S&P 500 Index or Russell 2000 Index) over available sample periods. Using monthly data as specified since July 1954 for EFFR and the S&P 500 Index (limited by EFFR) and since September 1987 for the Russell 2000 Index, all through January 2018, we find that: Keep Reading

Monthly Rebalanced Shorting of Leveraged ETF Pairs

Is shorting pairs of leveraged exchange-traded funds (ETF) reliably profitable? In their December 2017 paper entitled “Shorting Leveraged ETF Pairs”, Christopher Hessel, Jouahn Nam, Jun Wang, Xing Cunyu and Ge Zhang examine monthly returns from shorting a pair of leveraged and inverse leveraged ETFs for the same index. They first investigate what circumstances make this strategy profitable. They then test the strategy on each of the triple/inverse triple (3X/-3X) pairs associated with the following six base ETFs: Financial Select Sector SPDR (XLF: FAS/FAZ), Powershares QQQ (QQQ: TQQQ/SQQQ), iShares Russell 2000 Index (IWM: TNA/TZA), SPDR S&P 500 (SPY: UPRO/SPXU), VanEck Vectors Junior Gold Miners ETF (GDXJ: JNUG/JDST) and Energy Select Sector SPDR (XLE: ERX/ERY). Their analysis assumes rebalancing pair short positions to equal value at the end of each month and holding them to the end of the next month. Using monthly data for the selected leveraged ETFs from the end of 2007 (except the end of November 2009 for the leveraged versions of GDXJ) through the end of December 2016, they find that: Keep Reading

Industry Rotation Based on Advanced Regression Techniques

Can advanced regression techniques identify monthly cross-industry lead-lag return relationships that usefully indicate an industry rotation strategy? In their January 2018 paper entitled “Dynamic Return Dependencies Across Industries: A Machine Learning Approach”, David Rapach, Jack Strauss, Jun Tu and Guofu Zhou examine dynamic relationships between past and future returns (lead-lag) across 30 U.S. industries. To guard against overfitting the data, they employ a machine learning regression approach that combines a least absolute shrinkage and selection operator (LASSO) and ordinary least squares (OLS). Their approach allows each industry’s return to respond to lagged returns of all 30 industries. They assess economic value of findings via a long-short industry rotation hedge portfolio that is each month long (short) the fifth, or quintile, of industries with the highest (lowest) predicted returns for the next month based on inception-to-date monthly calculations. They consider three benchmark hedge portfolios based on: (1) historical past average returns of the industries; (2) an OLS-only approach; and, (3) a cross-sectional, or relative, momentum approach that is each month long (short) the quintile of industries with the highest (lowest) returns over the past 12 months. Using monthly returns  for 30 value-weighted U.S. industry groups during 1960 through 2016, they find that:

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Cryptocurrency Primer

How do cryptocurrencies work, and how can investors acquire and hold them? In their January 2018 paper entitled “Crypto-Assets Unencrypted”, Seoyoung Kim, Atulya Sarin and Daljeet Virdi survey cryptocurrency history and technology. They summarize cryptocurrency market sizes, trading volumes and volatilities, with comparisons to major fiat currencies and commodities. They further discuss crypto-asset valuation, regulation and the mechanics of investing in them. Based on cryptocurrency data for January 2013 through early December 2017, with focus on the 20 largest cryptocurrencies, they find that: Keep Reading

Distance Between Fast and Slow Price SMAs and Equity Returns

Does the distance between fast and slow simple moving averages (SMA) of an equity price series expose the degree of surprising/informative news about the asset? In their February 2018 paper entitled “The Predictability of Equity Returns from Past Returns: A New Moving Average-Based Perspective”, Doron Avramov, Guy Kaplanski and Avanidhar Subrahmanyam investigate distance between fast and slow price series SMAs as predictors of equity (individual stocks, industry and country market) returns. They choose the 21-day SMA as fast and the 200-day SMA as slow and define the distance between them (Moving Average Distance, or MAD) as the ratio of the former to the latter. They hypothesize that future returns are a continuous function of MAD. They test their hypothesis by measuring future returns: (1) for U.S. stocks sorted into tenths (deciles) based on MAD; and, (2) for U.S. stocks, industries and country markets above and below several MAD thresholds. To assess uniqueness of MAD indications, they control for 18 firm characteristics and several past return variables across different lookback intervals. Using daily prices adjusted for splits and dividends for a broad sample of U.S. stocks priced at least $5, U.S. industry stock groups and country stock markets, and values of U.S. Treasury bill (T-bill) yields and control variables, during June 1977 through October 2015, they find that: Keep Reading

Weekly Summary of Research Findings: 2/26/18 – 3/2/18

Below is a weekly summary of our research findings for 2/26/18 through 3/2/18. 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

Timing Bitcoin with SMAs

Are simple moving averages (SMA) useful for timing difficult-to-value Bitcoin? In their January 2018 paper entitled “Bitcoin: Predictability and Profitability Via Technical Analysis”, Andrew Detzel, Hong Liu, Jack Strauss, Guofu Zhou and Yingzi Zhu investigate the use of 5-day, 10-day, 20-day, 50-day or 100-day SMAs to predict Bitcoin returns. Specifically, they test a trading strategy that holds Bitcoins (cash) when current Bitcoin price is above (below) a selected SMA. They assume cash earns the U.S. Treasury bill (T-bill) yield. Using daily Bitcoin prices and T-bill yield, along with data for other variables/assets for comparison, during July 18, 2010 through December 12, 2017, they find that: Keep Reading

The BGSV Portfolio

How might an investor construct a portfolio of very risky assets? To investigate, we consider:

  • Diversifying (based on pairwise correlations) by combining: (1) Bitcoin Investment Trust (GBTC), representing a very long-term option on Bitcoins; (2) VanEck Vectors Junior Gold Miners ETF (GDXJ), representing a very long-term option on gold; and, (3) ProShares Short VIX Short-Term Futures (SVXY), to capture the U.S. stock market volatility risk premium by shorting short-term S&P 500 Index implied volatility (VIX) futures.
  • Capturing upside volatility and managing portfolio drawdown via monthly rebalancing and gain-skimming to a cash position.

We assume equal initial allocations of $10,000 to each of the three risky assets and $0 to cash. If the risky assets have a month-end combined value less than the combined initial allocations, we rebalance them to equal weights for next month. If the risky assets have a combined month-end value greater than the combined initial allocations, we rebalance to the initial allocations and move the excess permanently (skim) to cash. We assume monthly portfolio reformation frictions of 2% of month-end combined values of risky assets. We assume accrued, skimmed cash earns the 3-month U.S. Treasury bill (T-bill) yield. Using monthly adjusted values of GBTC, GDXJ and SVXY and contemporaneous T-bill yield during May 2015 (limited by GBTC) through mid-February 2018, we find that:

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