Objective research to aid investing decisions
Menu
Value Allocations for December 2019 (Final)
Cash TLT LQD SPY
Momentum Allocations for December 2019 (Final)
1st ETF 2nd ETF 3rd ETF

Investing Research Articles

Weekly Summary of Research Findings: 10/7/19 – 10/11/19

Below is a weekly summary of our research findings for 10/7/19 through 10/11/19. 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

Commodity Futures Risk Premium Over the Long Run

What are long run returns for commodity futures? In their September 2019 paper entitled “The Commodity Futures Risk Premium: 1871-2018”, Geetesh Bhardwaj, Rajkumar Janardanan and Geert Rouwenhorst estimate the historical risk premium of commodity futures from a long and broad sample free of survivorship bias covering 230 contract series traded since 1871 mostly in the U.S. and the UK. They calculate the premium as average excess return for rolling front-month contracts in three ways: (1) simple equal weighting of all monthly observations; (2) equal-weighted separately calculated premiums for each contract series; and, (3) average excess return for an equal‐weighted index series. They explore the link between survival of a contract series and its risk premium. They also estimate returns to basis or momentum factor strategies that are each month long (short) the equal-weighted half of available commodities with the higher (lower) futures basis or prior-year spot return. Using monthly prices for 230 commodity futures traded on 28 exchanges during 1871 through 2018, they find that: Keep Reading

Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for September 2019. The actual total (core) inflation rate for September is lower (a little lower than) forecasted.

Bond Returns Over the Very Long Run

Do bonds have a bad rap based on an unfavorable subsample? In the September 2019 revisions of his papers entitled “The US Bond Market Before 1926: Investor Total Return from 1793, Comparing Federal, Municipal, and Corporate Bonds Part I: 1793 to 1857” and “Part II: 1857 to 1926”, Edward McQuarrie revisits analysis of returns to bonds in the U.S. prior to 1926. He focuses on investor holding period returns rather than yields, considering U.S. Treasury, state, city and corporate debt. Specifically, he estimates returns to a 19th century diversified bond portfolio comprised of all long-term investment grade bonds trading in any year (free of contaminating factors such as circulation privileges and tax exemptions). Returns assume:

  1. Weights are proportional to amounts outstanding.
  2. Bonds are far from before maturity.
  3. Calculations use actual bond prices.

In other words, he calculates performance of a diversified index fund tracking actual long-term, investment-grade 19th century U.S. bonds. He also calculates returns to sub-indexes as feasible. He further constructs a new stock index for the period January 1793 to January 1871 and revisits conclusions in Stocks for the Long Run about relative performances of stocks and bonds. Using newly and previously compiled U.S. bond and stock prices extending back to January 1793, he finds that:

Keep Reading

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

FactSet S&P 500 Earnings Growth Estimate Evolutions

A subscriber, citing the weekly record of S&P 500 earnings growth estimates in the “FactSet Earnings Insight” historical series, wondered whether estimate trends/revisions are exploitable. To investigate, we collect S&P 500 quarterly year-over-year earnings growth estimates as recorded in this series. These data are bottom-up (firm by firm) aggregates, whether purely from analyst estimates (before any actual earnings releases), or a blend of actual earnings and estimates (during the relevant earnings season). Using these data and contemporaneous weekly levels of the S&P 500 Index during April 2011 through mid-September 2019, we find that: Keep Reading

Trading Strategy Catalog

Where can investors find a large catalog of trading strategies spanning asset classes? In the September 2019 update of their paper entitled “151 Trading Strategies”, Zura Kakushadze and Juan Andres Serur make freely available their book describing over 150 trading strategies, including over 550 formulas. These strategies encompass many asset classes and trading styles, including: stocks, options, fixed income, futures, exchange-traded funds, commodities, foreign exchange, convertibles, structured assets (collateralized debt and mortgage-backed), volatility, real estate, distressed, crypto, weather, energy, inflation, global macro, infrastructure and tax arbitrage. Some strategies incorporate machine learning. The paper does not include empirical studies, numerical simulations or backtests, but does contain annotated source code for out-of-sample testing, references and a glossary of acronyms and mathematical terms. Based on experience, they observe that: Keep Reading

Weekly Summary of Research Findings: 9/30/19 – 10/4/19

Below is a weekly summary of our research findings for 9/30/19 through 10/4/19. 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

In Search of the Bear?

Is intensity of public interest in a “bear market” useful for predicting stock market return? To investigate, we download monthly U.S. Google Trends search intensity data for “bear market” and relate this series to monthly S&P 500 Index returns. For comparison with the “investor fear gauge,” we also relate search data to monthly CBOE option-implied S&P 500 Index volatility (VIX) levels. Google Trends analyzes a percentage of Google web searches to estimate the number of searches done over a certain period. “Each data point is divided by the total searches of the geography and time range it represents to compare relative popularity… The resulting numbers are then scaled on a range of 0 to 100 based on a topic’s proportion to all searches on all topics.” Using the specified data during January 2004 (earliest available on Google Trends) through August 2019, we find that: Keep Reading

Are Managed Futures ETFs Working?

Are managed futures, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider three managed futures ETFs, all currently available:

  1. WisdomTree Managed Futures Strategy (WTMF) – seeks positive total returns in rising or falling markets that are uncorrelated with broad market equity and fixed income returns via diversified combination of commodities, currencies and interest rates futures.
  2. First Trust Morningstar Managed Futures Strategy (FMF) – seeks positive returns that are uncorrelated to broad market equity and fixed income returns via a portfolio of exchange-listed futures.
  3. ProShares Managed Futures Strategy (FUT) – seeks to profit in rising and falling markets by long and short positions in futures across asset classes such as commodities, currencies and fixed income such that each contributes equally to portfolio risk.

We focus on compound annual growth rate (CAGR), maximum drawdown (MaxDD) and correlations of returns with those of SPDR S&P 500 (SPY) and iShares Barclays 20+ Year Treasury Bond (TLT) as key performance statistics. We use Eurekahedge CTA/Managed Futures Hedge Fund Index (the index) as a benchmark. Using monthly returns for the three funds as available through August 2019, and contemporaneous monthly returns for the benchmark index, SPY and TLT, we find that: Keep Reading

Daily Email Updates
Login
Research Categories
Recent Research
Popular Posts