Equity Premium

What are current estimates of equity risk premiums (ERP) and risk-free rates around the world? In their March 2024 paper entitled “Survey: Market Risk Premium and Risk-Free Rate used for 96 countries in 2024”, Pablo Fernandez, Diego García de la Garza and Javier Acin summarize results of a February 2024 email survey of international finance and economic professors, analysts and company managers “about the Risk-Free Rate and the Market Risk Premium (MRP) used to calculate the required return to equity in different countries.” Results are in local currencies. Based on 4,064 specific and credible premium estimates spanning 96 countries for which there are at least six estimates, they find that: Keep Reading

Are sentiments conveyed in Seeking Alpha articles useful for stock picking? In their January 2023 paper entitled “Seeking Alpha: More Sophisticated Than Meets the Eye”, Duo Selina Pei, Abhinav Anand and Xing Huan apply two-pass natural language processing to test the informativeness of articles from Seeking Alpha incremental to publicly available earnings data. Specifically, they each month:

• Associate articles with one or more specific stocks.
• Extract positive and negative sentiment at both phrase and aggregate levels for each article/stock.
• Calculate a standardized net sentiment for each article/stock based on the difference between positive and negative mentions, emphasizing event sentiment over general sentiment.
• Rank articles/stocks based on standardized net sentiment over the last month. Reform equal-weighted portfolios of articles/stocks by ranked tenths (deciles). Calculate both immediate [-1,+1] and 90-day future [+2,+90] average gross raw returns and average gross abnormal returns adjusted for size, book-to-market and momentum.
• Sort stocks into 20 groups based on monthly standardized net sentiments up to two days before portfolio selection, excluding stocks with few articles or neutral sentiment. Reform an equal-weighted hedge portfolio that is long stocks with the highest sentiments and short stocks with the lowest (on average, 105 long and 86 short positions).

Using 350,095 articles published on Seeking Alpha since its inception in 2004 through the beginning of October 2018, daily returns of matched stocks and their options and associated earnings surprise data as available, they find that: Keep Reading

How do returns of different asset classes recently interact with the Effective Federal Funds Rate (EFFR)? We focus on monthly changes (simple differences) in EFFR  and look at lead-lag relationships between change in EFFR and returns for each of the following 10 exchange-traded fund (ETF) asset class proxies:

• Equities:
  • SPDR S&P 500 (SPY)
  • iShares Russell 2000 Index (IWM)
  • iShares MSCI EAFE Index (EFA)
  • iShares MSCI Emerging Markets Index (EEM)
• Bonds:
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • iShares iBoxx $ Investment Grade Corporate Bond (LQD)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
• Real assets:
  • Vanguard REIT ETF (VNQ)
  • SPDR Gold Shares (GLD)
  • Invesco DB Commodity Index Tracking (DBC)

Using end-of-month EFFR and dividend-adjusted prices for the 10 ETFs during December 2007 (limited by EMB) through March 2024, we find that: Keep Reading

Does pricing of options on leveraged exchange-traded funds (ETF) predict future returns of the underlying 1X ETFs? In the March 2024 version of their paper entitled “Lever Up! An Analysis of Options Trading in Leveraged ETFs”, Collin Gilstrap, Alex Petkevich, Pavel Teterin and Kainan Wang examine options trading in leveraged equity ETFs and its implications for future performance of underlying funds. They hypothesize that the compounded leverage of such options attracts especially sophisticated investors. Specifically, they test a risk-on/risk-off strategy that, at the end of each month:

1. Calculates the difference in changes in implied volatilities between at-the-money (ATM) call options and ATM put options on a leveraged ETF (and separately for comparison, on its underlying 1X ETF).
2. If this difference is greater (smaller) than its median value over the prior 12 months, specifies the next month as bullish (bearish) for the 1X ETF, and invests in a synthetic 3X ETF (the risk-free asset) next month. The synthetic 3X ETF earns three times the monthly returns of the underlying 1X ETF.

They also consider a more realistic test using SPDR S&P 500 ETF (SPY) as the underlying 1X ETF and Direxion Daily S&P 500 Bull 3X Shares (SPXL) as the associated leveraged ETF. They assume 0.2% trading frictions for portfolio turnover. Using daily returns for 76 leveraged equity ETFs matched to 30 underlying 1X ETFs and daily implied volatilities for associated ATM call and put options during January 2007 through December 2021, they find that: Keep Reading

Can holders of popular large-capitalization stocks improve portfolio performance by systematically buying or selling options on these stocks? In their February 2024 paper entitled “The Performance of Options-Based Investment Strategies: Evidence for Individual Stocks from 2004 to 2019”, Zhuo Li and Thomas Miller, Jr. compare to buy-and-hold the performances of four strategies that augment a long stock position with options, as follows:

1. Buy and hold the stock.
2. Covered call  – long stock plus short call.
3. Protective put – long stock plus long put.
4. Collar – long stock plus short call plus long put.
5. Covered combination – long stock plus short call plus short put.

They focus on 10 stocks widely held in 401(k) plans: ExxonMobil, Comcast, Berkshire Hathaway (Class B), Oracle, Microsoft, Coca-Cola, Amazon, Wells Fargo, Google (Class A) and Apple. They roll at the end of each calendar month from the standard monthly option that expires during the next month to the one that expires during the subsequent month. They choose option strike prices that are at least 5% out-of-the-money but as close to 5% as possible, with exceptions when no such options are available. They assume option buys and sells are at the daily closing bid-ask midpoint. They ignore the possibility of early option exercise. Using monthly data for the selected 10 stocks and specified options as available during January 2004 through November 2019, they find that: Keep Reading

Does finance professor David Kass, who presents annual lists of stock picks on Seeking Alpha, make good selections? To investigate, we consider his picks of:

• “10 Stocks for 2020”: APPL, BAC, BRK-A, WBD, JPM, MSFT, OXY, PH, SWK, STNE
• “16 Stocks For 2021”: GOOG, ABBV, AMZN, APPL, BAC, BRK-A, CABO, CHTR, LSXMK, META, MU, MSFT, PCG, PSTH, RBAC, RH
• “12 Stocks For 2022”: GOOG, AMZN, APPL, BAC, BRK-A, CVS, GPN, META, MSFT, NU, NVDA, TECK
• “10 Stocks For 2023”: ATVI, APPL, BRK-A, CVX, DHR, HSY, KHC, OXY, PEP, PGR

We compare the average return for stocks picks each year with that for SPDR S&P 500 ETF Trust (SPY) for the same year as a benchmark. Using dividend-adjusted returns from Yahoo!Finance for SPY and most stock picks and returns from Barchart.com and Investing.com for three picks during their selection years, we find that: Keep Reading

How different are stock market performance metrics for:

• Capital gains only, capital gains plus dividends accrued as cash (spent or saved), and capital gains plus dividends reinvested in the stock market?
• Nominal versus real returns?
• Simple return-to-risk calculations versus Sharpe ratio?

Using quarterly S&P 500 Index levels and dividends, quarterly U.S. Consumer Price Index (CPI) data (all items) and monthly 3-month U.S. Treasury bill (T-bill) yield as the risk-free rate/return on cash during the first quarter of 1988 through the fourth quarter of 2023, we find that: Keep Reading

Prior to 2015, we tracked performance of an equity market timing model based on real earnings yield (REY). The Simple Asset Class ETF Value Strategy (SACEVS) subsumed that model in 2015. Earnings yield is aggregate corporate earnings divided by corresponding stock index level. The REY model adjusts this earnings yield by subtracting the inflation rate for the same period. Does the REY concept still hold value for equity market timing? Using quarterly S&P 500 operating and as-reported earnings, S&P 500 Index (SP500) level, quarterly inflation as calculated from the U.S. Consumer Price Index, dividend-adjusted SPDR S&P 500 ETF Trust (SPY) and 3-month U.S. Treasury bill (T-bill) yield as available during March 1988 through December 2023, we find that: Keep Reading

Referring to “Substitute QQQ for SPY in SACEVS and SACEMS?” and “Conditionally Substitute SSO for SPY in SACEVS and SACEMS?”, a subscriber requested a horse race for boosting the performance of the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS), and thereby the Combined Value-Momentum Strategy (SACEVS-SACEMS), based on substituting:

1. ProShares Ultra S&P500 (SSO) for SPDR S&P 500 ETF Trust (SPY) in portfolio holdings, but not in SACEMS asset ranking calculations.
2. Invesco QQQ Trust (QQQ) for SPY in both portfolio holdings and SACEMS asset ranking calculations.

In conducting the horse race, we focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and gross annual Sharpe ratio as key performance metrics. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using monthly total (dividend-adjusted) returns for SACEVS assets, SACEMS assets, SSO and QQQ as available through February 2024, we find that: Keep Reading

How do exchange-traded-funds (ETF) that employ artificial intelligence (AI) to pick assets perform? To investigate, we consider six such ETFs, all currently available, as follows:

• • Amplify AI Powered Equity ETF (AIEQ) – picks U.S. stocks based on a quantitative model that runs on the IBM Watson platform.
  • VanEck Social Sentiment ETF (BUZZ) – picks U.S. stocks with the most positive sentiment as determined from online sources via a rules-based quantitative methodology.
  • WisdomTree International Al Enhanced Value Fund (AIVI) – picks developed market value stocks outside the U.S. and Canada via a quantitative AI model. [We use data only since retention of an AI-oriented advisor at the beginning of 2022.]
  • QRAFT AI-Enhanced U.S. Large Cap Momentum ETF (AMOM) – picks momentum stocks via an investment strategy enhanced by the use of artificial intelligence.
  • Teucrium Aila Long-Short Agriculture Strategy ETF (OAIA) – takes long and short positions in agricultural securities via a strategy employing machine learning.
  • Teucrium Aila Long-Short Base Metals Strategy ETF (OAIB) – takes long and short positions in base metals securities via a strategy employing machine learning.

We use SPDR S&P 500 ETF Trust (SPY) for comparison, though it is not conceptually matched to some of the ETFs. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for the six AI-powered ETFs and SPY as available through January 2024, we find that: Keep Reading