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Stock Returns Around Blockchain Investment Announcements

How does the market react when firms announce adoption of blockchain technology? In the May 2019 draft of their paper entitled “Bitcoin Speculation or Value Creation? Corporate Blockchain Investments and Stock Market Reactions”, Don Autore, Nicholas Clarke and Danling Jiang study stock price reactions to initial public announcements of investments in blockchain technology by listed U.S. firms. Their key metric is buy-and-hold abnormal return (BHAR) relative to each of five benchmarks: (1) portfolios of stocks matched on size and book-to-market (BM); (2) portfolios of stocks matched on market beta; 3) a broad value-weighted market index; (4) iShares Global Financials ETF (IXG); and, (5) iShares Global Tech ETF (IXN). Their announcement event windows is five trading days before initial public announcement of an investment in blockchain technology (-5) to 65 trading days after (65). Using dates of initial public announcements of investments in blockchain technology and contemporaneous daily returns for 207 stocks listed on NYSE and NASDAQ during October 2008 through March 2018, they find that:

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SMA10 vs. OFR FSI for Stock Market Timing

In response to “OFR FSI as Stock Market Return Predictor”, a subscriber suggested overlaying a 10-month simple moving average (SMA10) technical indicator on the Office of Financial Research Financial Stress Index (OFR FSI) fundamental indicator for timing SPDR S&P 500 (SPY). The intent of the suggested overlay is to expand risk-on opportunities safely. To test the overlay, we add four strategies (4 through 7) to the prior three, each evaluated since January 2000 and since January 2009:

  1. SPY – buy and hold SPY.
  2. OFR FSI-Cash – hold SPY (cash as proxied by 3-month U.S. Treasury bills) when OFR FSI at the end of the prior month is negative or zero (positive).
  3. OFR-FSI-VFITX – hold SPY (Vanguard Intermediate-Term Treasury Fund Investor Shares, VFITX, as a more aggressive risk-off asset than cash) when OFR FSI at the end of the prior month is negative or zero (positive).
  4. SMA10-Cash – hold SPY (cash) when the S&P 500 Index is above (at or below) its SMA10 at the end of the prior month.
  5. SMA10-VFITX – hold SPY (VFITX) when the S&P 500 Index is above (at or below) its SMA10 at the end of the prior month.
  6. OFR-FSI-SMA10-Cash – hold SPY (cash) when either signal 2 or signal 4 specifies SPY. Otherwise, hold cash.
  7. OFR-FSI-SMA10-VFITX – hold SPY (cash) when either signal 3 or signal 5 specifies SPY. Otherwise, hold VFITX.

Using end-of-month values of OFR FSI, SPY total return and level of the S&P 500 Index during January 2000 (OFR FSI inception) through June 2019, we find that:

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Weekly Summary of Research Findings: 8/19/19 – 8/23/19

Below is a weekly summary of our research findings for 8/19/19 through 8/23/19. 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

SACEVS Applied to Mutual Funds

“Simple Asset Class ETF Value Strategy” (SACEVS) finds that investors may be able to exploit relative valuation of the term risk premium, the credit (default) risk premium and the equity risk premium via exchange-traded funds (ETF). However, the backtesting period is limited by available histories for ETFs and for series used to estimate risk premiums. To construct a longer test, we make the following substitutions for potential holdings (selected for length of available samples):

To enable estimation of risk premiums over a longer history, we also substitute:

As with ETFs, we consider two alternatives for exploiting premium undervaluation: Best Value, which picks the most undervalued premium; and, Weighted, which weights all undervalued premiums according to degree of undervaluation. Based on the assets considered, the principal benchmark is a monthly rebalanced portfolio of 60% VFINX and 40% VFIIX. Using monthly risk premium calculation data during March 1934 through July 2019 (limited by availability of T-bill data), and monthly dividend-adjusted closing prices for the three asset class mutual funds during June 1980 through July 2019 (39 years, limited by VFIIX), we find that:

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Contents of Investment Advisor Portfolios

What should investors expect to see in a typical investment advisor’s model portfolio? In their July 2019 paper entitled “Factors and Advisors Portfolios”, Brian Lawler, Andrew Ang, Brett Mossman and Patrick Nolan examine patterns and factor exposures in detailed holdings for a large number of model portfolios from many types of investment advisors. When holdings are funds, they examine contents of the funds. They assess exposures to economic growth, real interest rates and inflation. Within equity holdings, they assess exposures to size, value, momentum, quality and volatility factors. Using holdings of 9,940 model portfolios provided by investment advisors during October 2017 through September 2018, they find that: Keep Reading

Gold Price Drivers?

What drives the price of gold: inflation, interest rates, stock market behavior, public sentiment? To investigate, we relate monthly and annual spot gold return to changes in:

We start testing in 1975 because: “On March 17, 1968, …the price of gold on the private market was allowed to fluctuate…[, and] in 1975…the price of gold was left to find its free-market level.” We lag CPI measurements by one month to ensure they are known to the market when calculating gold return. Using monthly data from December 1974 (March 1978 for consumer sentiment) through July 2019, we find that: Keep Reading

S&P 500 Volatility Indexes as an Asset Class

Should investors consider allocations to products that track equity volatility indexes? In her July 2019 paper entitled “Challenges of Indexation in S&P 500 Index Volatility Investment Strategies”, Margaret Sundberg examines whether behaviors of S&P 500 Index option-based volatility indexes justify treatment of volatility as an asset class. To assess potential strategies, she employs the following indexes:

Using daily time series for these indexes during April 2008 through March 2019, she finds that: Keep Reading

Day Trading a Bust?

Can individual investors make a living by day trading? In their July 2019 paper entitled “Day Trading for a Living?”, Fernando Chague, Rodrigo De-Losso and Bruno Giovannetti analyze performances of all Brazilian retail investors who begin trading futures on the main Brazilian stock index during 2013 through 2015 and persist in this trading for at least 300 sessions. They use data for 2012 to identify beginners, and they use data for 2016-2017 to extend performance evaluations for at least two years of trading. They consider performance both gross and net of exchange and brokerage fees, but they ignore income taxes and expenses such as courses and trading platforms. They employ subsamples and regressions to measure learning while trading. Using trading records for the specified index futures by all Brazilian investors during 2012 through 2017, they find that: Keep Reading

Weekly Summary of Research Findings: 8/12/19 – 8/16/19

Below is a weekly summary of our research findings for 8/12/19 through 8/16/19. 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

Are Stock Quality ETFs Working?

Are stock quality strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider four ETFs, all currently available (from oldest to youngest):

  • Invesco S&P 500 Quality ETF (SPHQ) – seeks to track performance of S&P 500 stocks with the highest quality scores based on firm return on equity, accruals ratio and financial leverage ratio, reformed semi-annually.
  • iShares Edge MSCI USA Quality Factor ETF (QUAL) – seeks to track performance of U.S. large-capitalization and mid-capitalization stocks selected based return on firm equity, earnings variability and debt-to-equity.
  • Fidelity Quality Factor ETF (FQAL) – seeks to track performance of U.S. large-capitalization and mid-capitalization stocks with a higher firm quality profile than the broader market.
  • Vanguard U.S. Quality Factor ETF (VFQY) – applies a rules-based quantitative model to select U.S. common stocks with strong fundamentals (strong profitability and healthy balance sheets) across market capitalizations, sectors and industry groups.

Because some available sample periods are very short, we focus on daily return statistics, along with cumulative returns and maximum drawdowns. We use three benchmarks according to fund descriptions: SPDR S&P 500 (SPY), Vanguard Russell 1000 Index Fund ETF (VONE) and iShares Russell 3000 ETF (IWV). Using daily returns for the four stock quality ETFs and benchmarks as available through most of July 2019, we find that: Keep Reading

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