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Value Investing Strategy (Strategy Overview)

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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.

Are Target Retirement Date Funds Attractive?

Do target retirement date funds, offering glidepaths that shift asset allocations away from equities and toward bonds as target dates approach, safely generate attractive returns? To investigate, we consider seven such mutual funds offered by Vanguard, as follows:

We consider as benchmarks SPDR S&P 500 ETF Trust (SPY), iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD) and both 80-20 and 60-40 monthly rebalanced SPY-LQD combinations. We look at monthly and annual return statistics, including compound annual growth rate (CAGR) and maximum drawdown (MaxDD). Using monthly total returns for SPY, LQD, three target retirement date funds since October 2003 and four target retirement date funds since June 2006 (limited by Vanguard inception dates), all through September 2023, we find that:

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SACEVS Input Risk Premiums and EFFR

The “Simple Asset Class ETF Value Strategy” (SACEVS) seeks diversification across a small set of asset class exchanged-traded funds (ETF), plus a monthly tactical edge from potential undervaluation of three risk premiums:

  1. Term – monthly difference between the 10-year Constant Maturity U.S. Treasury note (T-note) yield and the 3-month Constant Maturity U.S. Treasury bill (T-bill) yield.
  2. Credit – monthly difference between the Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield.
  3. Equity – monthly difference between S&P 500 operating earnings yield and the T-note yield.

Premium valuations are relative to historical averages. How might this strategy react to changes in the Effective Federal Funds Rate (EFFR)? Using end-of-month values of the three risk premiums, EFFRtotal 12-month U.S. inflation and core 12-month U.S. inflation during March 1989 (limited by availability of operating earnings data) through September 2023, we find that: Keep Reading

How Are Renewable Energy ETFs Doing?

How do exchange-traded-funds (ETF) focused on supplying renewable energy perform? To investigate, we consider nine of the largest renewable energy ETFs, all currently available, as follows:

We use SPDR S&P 500 (SPY) as a benchmark, assuming investors look at renewable energy stocks to beat the market and not to beat the energy sector. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the nine renewable energy ETFs and SPY as available through September 2023, we find that: Keep Reading

Firm Carbon Dioxide Emissions and Future Earnings/Stock Returns

Prior research indicates that stocks of firms with high direct and indirect carbon dioxide emissions tend to beat the market (offer a carbon premium). Does high-emissions stock outperformance derive from surprisingly high earnings? In their September 2023 paper entitled “Does the Carbon Premium Reflect Risk or Mispricing?”, Yigit Atilgan, Ozgur Demirtas, Alex Edmans and Doruk Gunaydin examine relationships between firm carbon dioxide emissions and future earnings surprises. They consider three levels of emissions from S&P Global Trucost: Scope 1 directly from firm operations; Scope 2 from firm consumption of purchased heat/electricity/steam; and, Scope 3 from upstream supply chain operations. They consider level of emissions (natural logarithm of emissions measured in tons) and annual change in level of emissions, with the latter winsorized at the 2.5% level. They consider several measures of earnings surprises, all comparing analyst forecasts to actual earnings. They calculate market reactions to earnings announcements as 3-day cumulative abnormal returns (CAR) relative to a 3-factor (market, size, book-to-market) model the day before through the day after earnings announcements. Using carbon dioxide emissions data, stock returns, market valuations, book values and analyst earnings forecasts for a broad sample of U.S. stocks during 2002 through 2021, they find that: Keep Reading

Are Preferred Stock ETFs Working?

Are preferred stock strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider seven of the largest preferred stock ETFs, all currently available, in order of longest to shortest available histories:

We use a monthly rebalanced portfolio of 60% SPDR S&P 500 (SPY) and 40% iShares iBoxx $ Investment Grade Corporate Bond (LQD) (60-40) as a simple hybrid benchmark for all these funds except PGF, for which we use Financial Select Sector SPDR (XLF). We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the preferred stock ETFs and benchmarks as available through August 2023, we find that: Keep Reading

Are Equity Multifactor ETFs Working?

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

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 2023, we find that: Keep Reading

Median Long-term Returns of U.S. Stocks and Portfolio Concentration

Are concentrated stock portfolios inherently disadvantaged by lack of diversification? In his June 2023 paper entitled “Underperformance of Concentrated Stock Positions”, Antti Petajisto analyzes rolling future returns for individual U.S. stocks relative to the broad U.S. stock market (market-adjusted) as a way to assess implications of concentrated stock portfolios. He focuses on median return as most representative of investor experience. He considers monthly rolling investment horizons of five, 10 and 20 years because concentrated stock positions are typically long-term holdings. He looks also at the relationship between 5-year past returns and future returns for individual stocks. Using monthly returns for individual U.S. common stocks from an evolving sample similar to the Russell 3000 (no microcaps) and for the overall capitalization-weighted U.S. stock market during January 1926 through December 2022, he finds that:

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Robustness of Machine Learning Return Forecasting

Are new machine learning portfolio strategies practically better than old stock factor ways? In their August 2023 paper entitled “Predicting Returns with Machine Learning Across Horizons, Firms Size, and Time”, Nusret Cakici, Christian Fieberg, Daniel Metko and Adam Zaremba examine the ability of various machine learning models to predict stock returns for: (1) monthly and annual return forecast horizons; (2) three ranges of firm size; and, (3) two subperiods. They apply eight machine learning models (including simple and penalized linear regressions, dimension reduction techniques, regression trees and neural networks) to 153 firm/stock characteristics following approaches typical in the finance literature. For each model, they employ rolling 11-year intervals, with:

  • Model training using the first seven years.
  • Model validation using the next three years.
  • Out-of-sample testing the last year using hedge portfolios that are long (short) the value-weighted fifth, or quintile, of stocks with the highest (lowest) predicted returns, reformed either monthly or annually depending forecast horizon.

They focus on gross 6-factor (market, size, book-to-market, profitability, investment, momentum) alpha to assess machine learning effectiveness. Using data for the selected 153 firm/stock characteristics and associated stock returns, measured monthly, for all listed U.S. stocks during January 1972 through December 2020, they find that: Keep Reading

Alternative Equity Factor Portfolio Formation Method

The conventional approach to measuring equity factor returns is via hedge portfolios that are long (short) the equal-weighted or value-weighted extreme highest (lowest) fifth or tenth of stocks sorted by some firm/stock characteristic. Is there a better way? In their August 2023 paper entitled “Power Sorting”, Anastasios Kagkadis, Harald Lohre, Ingmar Nolte, Sandra Nolte and Nikolaos Vasilas construct equity factor portfolios based on power sorting, which: (1) models the firm characteristic-future stock return relationship using a power series; and, (2) uses the power series to set factor portfolio weights. This approach requires no arbitrary quantile break points, instead allocating some weight to all stocks and tilting toward/away stocks with extreme characteristics as a compromise between conventional sorts and machine learning methods. Power sorting employs separate parameters for the long and short sides of the factor portfolio. Higher parameter values generate portfolios that concentrate more in stocks with characteristic extremes, while lower values spread weights more evenly across stocks. Differences between the two parameters allow differently weighted long and short sides of a factor portfolio. Additionally, they set an upper limit on the allocation to any one stock (2% in their main analysis) to ensure factor portfolio diversification. Using monthly factor data and associated stock returns for 85 widely accepted factor characteristics during March 1980 through December 2021, they find that:

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Blending AI Stock Picking and Conventional Portfolio Optimization

Should investors trust artificial intelligence (AI) models such as ChatGPT to pick stocks? In their August 2023 paper entitled “ChatGPT-based Investment Portfolio Selection”, Oleksandr Romanko, Akhilesh Narayan and Roy Kwon explore use of ChatGPT to recommend 15, 30 or 45 S&P 500 stocks, with portfolio weights, based on textual sentiment as available to Chat GPT via web content up to September 2021. For robustness, they ask ChatGPT to repeat recommendations for each portfolios 30 times and select the 15, 30 or 45 most frequently recommended stocks for respective portfolios. They then test out-of-sample performance of the following five implementations of each portfolio during September 2021 to July 2023, mid-March 2023 to July 2023, and May 2023 to July 2023:

  1. ChatGPT picks and ChatGPT weights.
  2. ChatGPT picks weighted equally.
  3. ChatGPT picks weighted based on minimum variance (Min Var) weights from a 5-year rolling weekly history.
  4. ChatGPT picks weighted based on maximum return (Max Ret) weights from a 5-year rolling weekly history.
  5. ChatGPT picks weighted based on maximum Sharpe ratio (Max Sharpe) weights from a 5-year rolling weekly history.

For benchmarking, they consider:

  • Long-only portfolios that incorporate all possible combinations of 15, 30 or 45 S&P 500 stocks weighted as above for Min Var, Vax Ret or Max Sharpe.
  • The S&P 500 Index, Dow Jones Industrial Average and the NASDAQ Index.
  • Average performance of 13 popular equity funds.

Using weekly data as specified up to September 2021 for training and subsequent weekly data through June 2023 for out-of-sample testing, they find that:

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