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Weekly Summary of Research Findings: 7/1/19 – 7/5/19

Below is a weekly summary of our research findings for 7/1/19 through 7/5/19. 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

Sentiment Indexes and Next-Month Stock Market Return

Do sentiment indexes usefully predict U.S. stock market returns? In his May 2018 doctoral thesis entitled “Forecasting Market Direction with Sentiment Indices”, flagged by a subscriber, David Mascio tests whether the following five sentiment indexes predict next-month S&P 500 Index performance:

  1. Investor Sentiment – the Baker-Wurgler Index, which combines six sentiment proxies.
  2. Improved Investor Sentiment – a modification of the Baker-Wurgler Index that suppresses noise among input sentiment proxies.
  3. Current Business Conditions – the ADS Index of the Philadelphia Federal Reserve Bank, which combines six economic variables measured quarterly, monthly and weekly to develop an outlook for the overall economy.
  4. Credit Spread – an index based on the difference in price between between U.S. corporate bonds and U.S. Treasury instruments with matched cash flows. (See “Credit Spread as an Asset Return Predictor” for a simplified approach.)
  5. Financial Uncertainty – an index that combines forecasting errors for large sets of economic and financial variables to assess overall economic/financial uncertainty.

He also tests two combinations of these indexes, a multivariate regression including all sentiment indexes and a LASSO approach. He each month for each index/combination predicts next-month S&P 500 Index return based on a rolling historical regression of 120 months. He tests predictive power by holding (shorting) the S&P 500 Index when the prediction is for the market to go up (down). In his assessment, he considers: frequency of correctly predicting up and down movements; effectiveness in predicting market crashes; and, significance of predictions. Using monthly data for the five sentiment indexes and S&P 500 Index returns during January 1973 through April 2014, he finds that: Keep Reading

Online, Real-time Test of AI Stock Picking?

Will equity funds “managed” by artificial intelligence (AI) outperform human investors? To investigate, we consider the performance of AI Powered Equity ETF (AIEQ), which “seeks to provide investment results that exceed broad U.S. Equity benchmark indices at equivalent levels of volatility.” Per the offeror, the EquBot model supporting AIEQ: “…leverages IBM’s Watson AI to conduct an objective, fundamental analysis of U.S.-listed common stocks and real estate investment trusts…based on up to ten years of historical data and apply that analysis to recent economic and news data. Each day, the EquBot Model…identifies approximately 30 to 125 companies with the greatest potential over the next twelve months for appreciation and their corresponding weights… The EquBot model limits the weight of any individual company to 10%. At times, a significant portion of the Fund’s assets may consist of cash and cash equivalents.” We use SPDR S&P 500 (SPY) as a simple benchmark for AIEQ performance. Using daily dividend-adjusted closes of AIEQ and SPY from AIEQ inception (October 18, 2017) through June 2019, we find that: Keep Reading

Cash Flow Duration as Overarching Stock Return Predictor

Does duration (relative arrival sequence) of firm cash flows explain many widely accepted equity factor returns? In their April 2019 paper entitled “Duration-Driven Returns”, Niels Gormsen and Eben Lazarus investigate whether firm cash flow duration explains value, profitability, investment, low risk, idiosyncratic volatility and payout factor returns. They measure cash flow duration monthly via multiple regressions that relate analyst long-term growth estimates for each firm to its profitability, investment, low risk (market beta), idiosyncratic volatility and payout. They then each month for U.S. and global stocks separately reform four value-weighted sub-portfolios:

  1. Above-median NYSE market capitalization and top 30% of duration.
  2. Above-median NYSE market capitalization and bottom 30% of duration.
  3. Below-median NYSE market capitalization and top 30% of duration.
  4. Below-median NYSE market capitalization and bottom 30% of duration.

They specify the duration factor as return to a portfolio that is each month long (short) the two equal-weighted long-duration (short-duration) sub-portfolios. As a robustness test, they separately analyze a sample of single-stock dividend futures (dividend strips, claims to dividends to be paid out during a given calendar year), which allow varying duration characteristics while keeping maturity of cash flows fixed. Using monthly data for a broad sample of U.S. stocks starting August 1963, monthly data for global stocks starting July 1990, and annual data for 150 single-stock dividend futures with up to 5-year maturity starting January 2010, all through December 2018, they find that: Keep Reading

Productivity and the Stock Market

Financial media often cite Bureau of Labor Statistics (BLS) productivity growth news releases as relevant to investment outlook. Does the quarter-to-quarter change in U.S. labor force productivity predict U.S. stock market behavior? Specifically, does a relatively weak (strong) change in productivity portend strong (weak) earnings and therefore an advance (decline) for stocks? Using annualized quarterly changes in non-farm labor productivity from BLS and end-of quarter S&P 500 Index levels during January 1950 through March 2019, we find that: Keep Reading

Weekly Summary of Research Findings: 6/24/19 – 6/28/19

Below is a weekly summary of our research findings for 6/24/19 through 6/28/19. 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 Portfolio-Asset Addition Testing

Does adding an exchange-traded fund (ETF) or note (ETN) to the Simple Asset Class ETF Momentum Strategy (SACEMS) boost performance via consideration of more trending/diversifying options? To investigate, we add the following 23 ETF/ETN asset class proxies one at a time to the base set and measure effects on the Top 1, equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios:

AlphaClone Alternative Alpha (ALFA)
JPMorgan Alerian MLP Index (AMJ)
UBS ETRACS Wells Fargo Business Development Companies (BDCS)
Vanguard Total Bond Market (BND)
SPDR Barclays International Treasury Bond (BWX)
PowerShares DB G10 Currency Harvest (DBV)
iShares JPMorgan Emerging Market Bond Fund (EMB)
First Trust US IPO Index (FPX)
Guggenheim Frontier Markets (FRN)
iShares iBoxx High-Yield Corporate Bond (HYG)
iShares 7-10 Year Treasury Bond (IEF)
iShares Latin America 40 (ILF)
iShares National Muni Bond ETF (MUB)
PowerShares Closed-End Fund Income Composite (PCEF)
PowerShares Global Listed Private Equity (PSP)
IQ Hedge Multi-Strategy Tracker (QAI)
SPDR Dow Jones International Real Estate (RWX)
ProShares UltraShort S&P 500 (SDS)
iShares Short Treasury Bond (SHV)
iShares TIPS Bond (TIP)
United States Oil (USO)
iPath S&P 500 VIX Short-Term Futures (VXX)
iPath S&P 500 VIX Medium-Term Futures (VXZ)

The base set consists of:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

Each month, we rank the base set plus one of the additional ETFs/ETNs based on past return and reform the SACEMS portfolios. The sample starts with the first month all base set ETFs are available (February 2006), but inceptions for most of the additional ETFs/ETNs are after this month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics, ignoring monthly reformation costs. Using end-of-month total (dividend-adjusted) returns for the specified 32 assets as available during February 2006 through May 2019, we find that: Keep Reading

SACEMS Portfolio-Asset Exclusion Testing

Are all of the potentially trending/diversifying asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) necessary? Might one or more of them actually be harmful to performance? To investigate, we each month rank the following assets based on past return with one excluded (nine separate test series) and reform the Top 1, equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

The test starts with the first month all ETFs are available (February 2006). We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics, ignoring monthly portfolio reformation costs. Using end-of-month total (dividend-adjusted) returns for the specified nine assets during February 2006 through May 2019, we find that: Keep Reading

Equal Weighting, Firm Age and Stock Returns

Does stock performance vary with age (since listing), and does any such effect interact with market capitalization (size)? In their April 2019 paper entitled “Age Matters”, Danqiao Guo, Phelim Boyle, Chengguo Weng and Tony Wirjanto examine age and size of U.S. stocks in combination. To disentangle interaction, they generate 20,000 simulations for each of two sets of portfolios separately for holdings of 5, 25, 50 or 100 stocks:

  1. Rebalanced – a randomly selected equal-weighted portfolio rebalanced each month to equal weight after replacing delisted stocks (roughly 10% of stocks each year) with replacements randomly selected from those in the full sample not already in the portfolio. New stocks are representative of the market in terms of age, while residual stocks age by a month, such that the portfolio tends to grow older.
  2. Bootstrapped – a randomly selected equal-weighted (also, for reference, value-weighted) portfolio that is each month liquidated and randomly reformed, such that it remains representative of the full sample in terms of age.

If stock return is related to age, these two sets of portfolios perform differently. They further compare performances of 16 portfolios double-sorted into four age groups (quartiles) and four size quartiles. Using monthly returns and listing dates for a broad sample of U.S. stocks during July 1926 through December 2016, they find that: Keep Reading

Cryptocurrency Factor Model

Do simple factor models help explain future return variations across different cryptocurrencies, as they do for stocks? In their April 2019 paper entitled “Common Risk Factors in Cryptocurrency”, Yukun Liu, Aleh Tsyvinski and Xi Wu examine performances of cryptocurrency (coin) counterparts for 25 price-related and market-related stock market factors, broadly categorized as size, momentum, volume and volatility factors. They first construct a coin market index based on capitalization-weighted returns of all coins in their sample. They then each week sort coins into fifths based on each factor and calculate average excess return for a portfolio that is long (short) coins in the highest (lowest) quintile. Finally, they investigate whether any small group of factors accounts for returns of all significant factors. Using daily prices in U.S. dollars and non-return variables (excluding top and bottom 1% values as potential errors/outliers) for all coins with market capitalizations over $1 million dollars from Coinmarketcap.com during January 2014 through December 2018 (a total of 1,707 coins, growing from 109 in 2014 to 1,583 in 2018), they find that:

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