Equity Premium

How do returns of different asset classes recently interact with inflation as measured by monthly change in the not seasonally adjusted, all-items consumer price index (CPI) from the U.S. Bureau of Labor Statistics? To investigate, we look at lead-lag relationships between change in CPI 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 monthly total CPI values and monthly dividend-adjusted prices for the 10 specified ETFs during December 2007 (limited by EMB) through June 2023, we find that: Keep Reading

How should global investors assess country sovereign bond and equity risks? In his July 2023 paper entitled “Country Risk: Determinants, Measures and Implications – The 2023 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 changes in the Effective Federal Funds Rate (EFFR), the actual cost of short-term liquidity derived from a combination of market demand and Federal Reserve open market operations designed to maintain the Federal Funds Rate (FFR) target, predictably influence the U.S. stock market over horizons up to a few months? To investigate, we relate smoothed (volume-weighted median) monthly levels of EFFR to monthly U.S. stock market returns (S&P 500 Index or Russell 2000 Index) over available sample periods. Using monthly data as specified since July 1954 for EFFR and the S&P 500 Index (limited by EFFR) and since September 1987 for the Russell 2000 Index, all through June 2023, we find that: Keep Reading

During 1989 through 2019, the S&P 500 Index generated 5.5% real annual return, compared to just 2.5% annual real growth in U.S. gross domestic product (GDP). How can this disconnect happen? Can it continue? In the June 2023 version of his paper entitled “End of an Era: The Coming Long-Run Slowdown in Corporate Profit Growth and Stock Returns”, Michael Smolyansky examines interactions between U.S. stock market performance and declines in interest rates and corporate tax rates over the last three decades. He focuses on S&P 500 non-financial stocks adjusted for index additions/deletions and for changes in firm shares outstanding, allowing computation of per share metrics. He decomposes stock returns into: (1) change in price-earnings ratio (P/E);  (2) change in earnings before interest and taxes (EBIT); (3) change in interest expenses; and, (4) change in effective corporate tax rate. Using the specified annual data during 1962 through 2019, he finds that: Keep Reading

Which previously researched variables or combinations of such variables best predict long-term U.S. stock market returns? In their June 2023 paper entitled “Estimating Long-Term Expected Returns”, Rui Ma, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti compare abilities of several yield, yield/growth, valuation variables and combinations across these categories of variables to predict 10-year and 20-year U.S. stock market returns out-of-sample. Specifically, they test 25 predictors from the following individual variables and combinations thereof:

• Yield category: dividend yield, total yield, net total yield and cyclically adjusted total yield.
• Yield/growth category: current values of these yields plus historical earnings growth, dividend growth, total yield growth and cyclically adjusted total yield growth, respectively.
• Valuation category: total return cyclically adjusted price-earnings ratio, total wealth portfolio composition, equity market value-to-gross domestic product ratio (the Buffett indicator) and cyclical consumption.
• Combining categories based on: simple prediction average, inverse variance-weighted prediction average, constrained regression and Bayesian model averaging.

Their benchmark predictor is the historical average return. They use annualized log returns for all predictors, focusing on mean absolute errors and mean squared errors relative to actual future returns as accuracy metrics. They also consider also a mean-variance asset allocation perspective, allocating to the S&P 500 Index and 10-year U.S. Treasury notes to maximize gross Sharpe ratio based on predicted equity returns. Using monthly data as described above during 1871 through 2020, they find that:

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With monthly market state specified as positive (negative) when prior-month market excess return relative to U.S. Treasury bill (T-bill) yield is positive (negative), “Equity Factor Performance Following Positive and Negative Market Returns” reports that average monthly market excess return is 0.83% (10.0% annualized) positive market states and 0.05% (0.6% annualized) for negative states during August 1965 through January 2017. Is this finding reliable and easily exploitable? To check, we look at SPDR S&P 500 ETF Trust (SPY) monthly total returns after prior-month total returns are positive or negative out-of-sample with respect to the cited study. We also consider SPY excess returns according to whether its prior-month excess total returns are positive or negative. Using end-of-month SPY dividend-adjusted prices and monthly 3-month T-bill yield during January 2017 through June 2023, we find that:

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Do stock return anomalies perform differently after positive and negative monthly market returns? In their July 2023 paper entitled “The Market State, Mispricing and Asset Pricing Anomalies”, Michael Di Carlo and Ilias Tsiakas examine the role of the overall market state in estimating returns for stock return anomalies, specifying the market state as positive (negative) for a month when the market excess return relative to U.S. Treasury bill yield is positive (negative) the prior month. They then measure returns during each of the two states for 14 stock return anomalies, including: market beta, size, book-to-market, operating profitability, asset growth, momentum, short-term reversal, volatility, idiosyncratic volatility, correlation with the market, maximum return over the last month, maximum return over the past year, illiquidity and 1% value-at-risk. For each anomaly, they measure returns via a hedge portfolio that is each month long (short) the fifth, or quintile, of stocks with the highest (lowest) expected returns based on the relevant anomaly characteristic. Using the required monthly data for U.S. common stocks priced over $5 during August 1965 through January 2017, they find that: Keep Reading

Do exchange-traded funds selecting stocks based on environmental, social, and governance characteristics (ESG ETF) typically offer attractive performance? To investigate, we compare performance statistics of eight ESG ETFs, all currently available, to those of simple and liquid benchmark ETFs, as follows:

1. iShares MSCI USA ESG Select ETF (SUSA), with SPDR S&P 500 ETF Trust (SPY) as a benchmark.
2. iShares MSCI KLD 400 Social ETF (DSI), with SPY as a benchmark.
3. iShares ESG MSCI EM ETF (ESGE), with iShares MSCI Emerging Markets ETF (EEM) as a benchmark.
4. iShares ESG Aware MSCI EAFE ETF (ESGD), with iShares MSCI EAFE ETF (EFA) as a benchmark
5. iShares ESG MSCI USA ETF (ESGU), with SPY as a benchmark.
6. Nuveen ESG Small-Cap ETF (NUSC), with iShares Russell 2000 ETF (IWM) as a benchmark.
7. Vanguard ESG U.S. Stock ETF (ESGV), with SPY as a benchmark.
8. Vanguard ESG International Stock ETF (VSGX), with Vanguard FTSE All-World ex-US Index Fund ETF (VEU) as a benchmark.

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 all benchmarks over matched sample periods through June 2023, we find that: Keep Reading

Is the ability of the VIX percentile threshold rule described in “Using VIX and Investor Sentiment to Explain Stock Market Returns” to explain future stock market excess return in-sample readily exploitable out-of-sample? To investigate, we test a strategy (VIX Percentile Strategy) that each month holds SPDR S&P 500 ETF Trust (SPY) or 3-month U.S. Treasury bills (T-bills) according to whether a recent end-of-month level of the CBOE Volatility Index (VIX) is above or below a specified inception-to-date (not full sample) percentage threshold. To test sensitivities of the strategy to settings for its two main features, we consider:

• Each of 70th, 75th, 80th, 85th or 90th percentiles as the VIX threshold for switching between T-bills and SPY.
• Each of 0, 1, 2 or 3 skip months between VIX measurement and strategy response.

We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as essential performance metrics and use buy-and-hold SPY as a benchmark. We do not quantify frictions due to switching between SPY and T-bills for the VIX Percentile Strategy. Using end-of-month VIX levels since January 1990 and dividend-adjusted SPY prices and T-bill yields since January 1993 (SPY inception), all through May 2023, 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 May 2023, we find that: Keep Reading