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Value Investing Strategy (Strategy Overview)

Allocations for March 2024 (Final)
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Momentum Investing Strategy (Strategy Overview)

Allocations for March 2024 (Final)
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Equity Premium

Governments are largely insulated from market forces. Companies are not. Investments in stocks therefore carry substantial risk in comparison with holdings of government bonds, notes or bills. The marketplace presumably rewards risk with extra return. How much of a return premium should investors in equities expect? These blog entries examine the equity risk premium as a return benchmark for equity investors.

Use Analyst Target Price Forecasts to Rank Stocks?

While prior research indicates that analyst forecasts of future stock returns are substantially biased upward, might the relative rankings of return forecasts be informative? In their June 2023 paper entitled “Analysts Are Good at Ranking Stocks”, Adam Farago, Erik Hjalmarsson and Ming Zeng apply within-analyst 12-month stock price targets to rank stocks in two ways:

  1. Average Demeaned Return – each month, demean the returns implied by target prices from an analyst by subtracting from each return the average forecasted return for that analyst. Then, average the demeaned returns for a given stock across all analysts.
  2. Average Ranking – each month, rank stocks by forecasted return for each analyst. Then, average the rankings for a given stock across all analysts covering that stock.

Both approaches remove the upward biases observed in raw target prices. To test analyst forecast informativeness, they then form hedge portfolios that are each month long (short) the equal-weighted or value-weighted fifths, or quintiles, of stocks with the highest (lowest) demeaned returns or rankings that month. Using 12-month target prices for each analyst who issues targets for at least three stocks during a month and associated monthly firm characteristics and stock prices during March 1999 through December 2021, they find that:

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SACEVS-SACEMS for Value-Momentum Diversification

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 2023, we find that: Keep Reading

Do Convertible Bond ETFs Attractively Meld Stocks and Bonds?

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, all through July 2023, we find that: Keep Reading

Recent Interactions of Asset Classes with Inflation (CPI)

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

Evaluating Country Investment Risk

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

EFFR and the Stock Market

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

Long-run Slowdown in U.S. Equity Market Ahead?

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

Best Long-term U.S. Stock Market Return Predictors?

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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Exploit Difference Between Positive and Negative Market States?

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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Equity Factor Performance Following Positive and Negative Market Returns

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

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