Equity Premium

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

Has ease of access to, and processing of, firm accounting data suppressed stock anomalies by leveling the information playing field? In their July 2024 paper entitled “The Effect of New Information Technologies on Asset Pricing Anomalies”, David Hirshleifer and Liang Ma test the effects of mandating Electronic Data Gathering, Analysis and Retrieval (EDGAR) during April 1993 to May 1996 and eXtensible Business Reporting Language (XBRL) during 2009 to 2011 on well-known stock return anomalies attributed to mispricing. EDGAR makes firm accounting data available electronically, and XBRL reduces the cost of processing such data by making it machine readable. They focus on eight anomalies, five of which rely on accounting data (accruals, net operating assets, investment-to-assets ratio, asset growth and gross profitability) and three of which rely on market data (momentum, net stock issuance and composite equity issuance). They examine effects of EDGAR/XBRL implementations on each anomaly individually, on the five accounting anomalies in aggregate and on the three non-accounting anomalies in aggregate. They carefully consider EDGAR/XBRL implementation dates and fiscal years by firm to compare anomalies for implemented and non-implemented sets of stocks. Using firm characteristics and monthly returns for a broad sample of U.S. common stocks during July 1992 through June 1997 (July 2009 through June 2012) for the EDGAR (XBRL) sample, they find that: Keep Reading

What is the distribution of U.S. common stock outcomes over the past century? In the July 2024 draft of his paper entitled “Which U.S. Stocks Generated the Highest Long-Term Returns?”, Hendrik Bessembinder presents cumulative returns and compound annual growth rates (CAGR) for all 29,078 publicly listed U.S. common stocks in the Center for Research in Security Prices (CRSP) databases through 2023, from initial appearance in CRSP until delisting or the end of the sample period. He assumes immediate reinvestment of all dividends. Using daily price/dividend data for all U.S. common stocks during December 1925 through December 2023, he finds that:

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