Equity Premium

Are equity multifactor strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider eight multifactor ETFs, all currently available:

• iShares Edge MSCI Multifactor USA (LRGF) – holds large and mid-cap U.S. stocks with focus on quality, value, size and momentum, while maintaining a level of risk similar to that of the market. The benchmark is iShares Russell 1000 (IWB).
• iShares Edge MSCI Multifactor International (INTF) – holds global developed market ex U.S. large and mid-cap stocks based on quality, value, size and momentum, while maintaining a level of risk similar to that of the market. The benchmark is iShares MSCI ACWI ex US (ACWX).
• Goldman Sachs ActiveBeta U.S. Large Cap Equity (GSLC) – holds large U.S. stocks based on good value, strong momentum, high quality and low volatility. The benchmark is SPDR S&P 500 (SPY).
• John Hancock Multifactor Large Cap (JHML) – holds large U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns. The benchmark is SPY.
• John Hancock Multifactor Mid Cap (JHMM) – holds mid-cap U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns. The benchmark is SPDR S&P MidCap 400 (MDY).
• JPMorgan Diversified Return U.S. Equity (JPUS) – holds U.S. stocks based on value, quality and momentum via a risk-weighting process that lowers exposure to historically volatile sectors and stocks. The benchmark is SPY.
• Xtrackers Russell 1000 Comprehensive Factor (DEUS) – seeks to track, before fees and expenses, the Russell 1000 Comprehensive Factor Index, which seeks exposure to quality, value, momentum, low volatility and size factors. The benchmark is IWB.
• Vanguard U.S. Multifactor (VFMF) – uses a rules-based quantitative model to evaluate U.S. common stocks and construct a U.S. equity portfolio that seeks to achieve exposure to multiple factors across market capitalizations (large, mid and small). The benchmark is iShares Russell 3000 (IWV).

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the seven equity multifactor ETFs and benchmarks as available through August 2024, we find that: Keep Reading

How does the performance of the global multi-class market look when evaluated at a monthly frequency? In their August 2024 paper entitled “The Risk and Reward of Investing”, Ronald Doeswijk and Laurens Swinkels assess global investing rewards and risks via an exhaustive $150 trillion portfolio of investable global assets priced at a monthly frequency, enabling greater granularity of risk estimates than does the annual frequency used in prior research. They consider five asset classes: equities, real estate, non-government bonds, government bonds and commodities. For these classes and the multi-class market, they examine stability of Sharpe ratios and severity, frequency and duration of drawdowns. Their default base currency is the U.S. dollar, but they measure effects of choosing one of nine other currencies on global market portfolio performance. They calculate excess investment returns generally relative to government bill yields as a proxy for return on savings. Using monthly returns for all investable global assets with reinvested dividends during 1970 through 2022, they find that:

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Do exchange-traded funds (ETF) focused on cybersecurity stocks offer attractive performance? To investigate, we compare performance statistics of six cybersecurity ETFs, all currently available, to those of Invesco QQQ Trust (QQQ), as follows:

1. Amplify Cybersecurity ETF (HACK)
2. First Trust NASDAQ Cybersecurity ETF (CIBR)
3. iShares Cybersecurity and Tech ETF (IHAK)
4. Global X Cybersecurity ETF (BUG)
5. ProShares Ultra Nasdaq Cybersecurity (UCYB)
6. WisdomTree Cybersecurity Fund (WCBR)

We focus on average return, standard deviation of returns, reward/risk (average return divided by standard deviation of returns), compound annual growth rate (CAGR) and maximum drawdown (MaxDD), all based on monthly data. Using monthly dividend-adjusted returns for all specified ETFs since inceptions and for QQQ over matched sample periods, all through July 2024, we find that: Keep Reading

“Nixed: The Upside of Getting Dumped”, flagged by a subscriber, finds that “index deletions…could add an abnormal upside to a portfolio when the current growth-dominated bubble starts to deflate.” The authors have quantified findings as the Research Affiliates Deletions Index (NIXT), constructed by:

1. Starting with deletions due to market capitalization changes from the 500 and 1,000 largest U.S. stocks by market capitalization.
2. Removing the bottom 20% of deletions based on firm quality assessments.
3. Holding the equal-weighted remaining deletions up to five years (or until they rejoin a top market capitalization index), rebalancing annually at the end of May.

Do index deletions inherently underperform? To investigate we look at stocks deleted from the S&P 500 Index due to market capitalization changes over the past few years and compare their performances to that of SPDR S&P 500 ETF Trust (SPY) since the close on respective deletion dates. Using dividend-adjusted prices for 43 S&P 500 deletions at closes on deletion dates and corresponding dividend-adjusted prices for SPY since during April 2020 through 8/23/24, we find that: Keep Reading

A subscriber requested measurement of a “premium” associated with stocks of innovative technology firms by looking at the difference in returns between the following two exchange-traded funds (ETF):

• Invesco QQQ Trust (QQQ), which generally tracks the NASDAQ 100 Index
• SPDR S&P 500 ETF Trust (SPY)

Using monthly dividend-adjusted closing prices for these ETFs during March 1999 (limited by QQQ) through July 2024, we find that: Keep Reading

Does the combination of an artificial intelligence (AI)-generated financial news sentiment with a complex financial stress metric generate good stock market timing signals? In their April 2024 paper entitled “Mixing Financial Stress with GPT-4 News Sentiment Analysis for Optimal Risk-On/Risk-Off Decisions”, Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez and Thomas Jacquot devise and test a risk-on/risk-off strategy for stock market timing. The strategy combines:

• Stress Index (SI): based on VIX, TED spread, Credit Default Swap index and realized volatilities across major equity, bond and commodity markets, all normalized and then aggregated by category. Overall SI is the average of category results, rescaled to fall between 0 and 1.
• News sentiment signal: 10-day moving average of ChatGPT 4 assessments of the sentiment (1 for positive or 0 for negative) in Bloomberg daily market summaries.

They consider six strategies:

1. Benchmark (or Long Only) – buy and hold the index, with constant volatility scaling to match the final (retrospective) volatility of an active strategy.
2. VIX – weight the stock index according to VIX, with times of stress indicated by VIX above its 80th percentile.
3. SI – weight the stock index according to the value of SI as described above.
4. News – weight the stock index according to the ChatGPT 4 news sentiment signal.
5. SI News – weight the stock index according to the product of SI and News.
6. Dynamic SI News – because SI News either significantly outperforms or underperforms SI alone during subperiods, each month weight the stock index according to either SI alone or SI News, whichever has the better Sharpe ratio over the past 250 trading days at the end of the prior month.

They test the strategy on the S&P 500 Index, the NASDAQ 100 Index and an equal-weighted combination of these two indexes plus the Nikkei 225, Euro Stoxx 50 and Emerging Markets indexes. They assume trading frictions of 0.2% of value traded. Using daily values of all specified inputs during January 2005 through December 2023, they find that: Keep Reading

How should global investors assess country sovereign bond and equity risks? In his July 2024 paper entitled “Country Risk: Determinants, Measures and Implications – The 2024 Edition”, Aswath Damodaran examines country risk from multiple perspectives. To estimate a country risk premium, he considers measurements of both country government bond risk and country equity risk. Based on a variety of sources and methods, he concludes that: Keep Reading

Do exchange-traded funds (ETF) that hold convertible corporate bonds offer attractive performance? To investigate, we compare performance statistics for the following four convertible bond ETFs, all currently available, to those for a monthly rebalanced 60%-40% combination of SPDR S&P 500 ETF Trust (SPY) and iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD):

1. SPDR Bloomberg Convertible Securities ETF (CWB)
2. iShares Convertible Bond ETF (ICVT)
3. First Trust SSI Strategic Convertible Securities ETF (FCVT)
4. American Century Quality Convertible Securities ETF (QCON)

We focus on average return, standard deviation of returns, reward/risk (average return divided by standard deviation of returns), compound annual growth rate (CAGR) and maximum drawdown (MaxDD), all based on monthly data. Using monthly dividend-adjusted returns for all specified ETFs since inceptions and for SPY and LQD over matched sample periods through July 2024, we find that: Keep Reading

Are the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) mutually diversifying. To check, based on feedback from subscribers about combinations of interest, we look at three equal-weighted (50-50) combinations of the two strategies, rebalanced monthly:

1. 50-50 Best Value – EW Top 2: SACEVS Best Value paired with SACEMS Equally Weighted (EW) Top 2 (aggressive value and somewhat aggressive momentum).
2. 50-50 Best Value – EW Top 3: SACEVS Best Value paired with SACEMS EW Top 3 (aggressive value and diversified momentum).
3. 50-50 Weighted – EW Top 3: SACEVS Weighted paired with SACEMS EW Top 3 (diversified value and diversified momentum).

We consider as a benchmark a simple technical strategy (SPY:SMA10) that holds SPDR S&P 500 ETF Trust (SPY) when the S&P 500 Index is above its 10-month simple moving average and 3-month U.S. Treasury bills (Cash, or T-bills) when below. We also test sensitivity of results to deviating from equal SACEVS-SACEMS weights. Using monthly gross returns for SACEVS, SACEMS, SPY and T-bills during July 2006 through July 2024, we find that: Keep Reading

Do analysts systematically ignore the connection between future firm earnings and current economic conditions? In their July 2024 paper entitled “Predicting Analysts’ S&P 500 Earnings Forecast Errors and Stock Market Returns Using Macroeconomic Data and Nowcasts”, Steven Sharpe and Antonio Gil de Rubio Cruz examine the quality of bottom-up forecasts of near-term S&P 500 earnings aggregated from analyst forecasts across individual firms. Specifically, they:

• Model expected aggregate S&P 500 quarterly earnings growth as a function of GDP growth, output and wage inflation and change in dollar exchange rate. They also consider a simplified model based only on real GDP growth and change in the dollar exchange rate.
• Calculate the gap between modeled S&P 500 earnings growth and analyst-forecasted growth.
• Estimate how well this forecast gap predicts analyst forecast errors.
• Test the extent to which the forecast gap predicts S&P 500 Index total returns.

Using quarterly actual and forecasted S&P 500 earnings, S&P 500 Index total return and values for the specified economic variables during 1993 through 2023, they find that: Keep Reading