Equity Premium

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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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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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

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

What characteristics of U.S. equity factor return series are most relevant to respective factor performance? In his May 2023 paper entitled “The Cross-Section of Factor Returns” David Blitz explores long-term average returns and market alphas, 60-month market betas and factor performance cyclicality for U.S. equity factors. He also assesses potentials of three factor rotation strategies: low-beta, seasonal and return momentum. Using monthly returns for 153 published U.S. equity market factors, classified statistically into 13 groups, during July 1963 through December 2021, he finds that:

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Do the stocks that dominate shareholder wealth-building (accounting for share price changes, dividends, repurchases/new share issuances and investor money flows) increasing concentrate within a small pool? In his May 2023 paper entitled “Shareholder Wealth Enhancement”, Hendrik Bessembinder identifies the stocks with the largest increases and largest decreases in shareholder wealth since 1926. He examines the degree to which increases in shareholder wealth concentrate among stocks over time. Using monthly data (including delisting returns) for 28,114 publicly traded U.S. common stocks and contemporaneous 1-month U.S. Treasury bill yield as a benchmark during 1926 through 2022, he finds that:

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What kind of return patterns are typical of beginnings and ends of equity bull and bear markets? In his April 2023 paper entitled “Investor Overreaction: Evidence From Bull and Bear Markets”, Valeriy Zakamulin examines return patterns of U.S. stock market bull and bear states as a way to decide when investors tend to overreact. He uses the S&P 500 Index as a proxy for the U.S. stock market. He applies a pattern recognition algorithm to: (1) identify index peaks and troughs; and, (2) ensure that a full bear-bull cycle lasts at least 16 months and bear or bull states last at least 5 months, unless the index rises or falls by more than 20%. He then standardizes the duration of each market state to 10 intervals and assumes that the bull or bear return evolves quadratically with state age. Because the available sample is relatively small, he applies bootstrapping to enhance reliability of findings. Using monthly S&P 500 Index returns (excluding dividends) during January 1926 through December 2022, he finds that: Keep Reading