Equity Premium

The body of U.S. stock market research offers hundreds of factors (the factor zoo) to explain and predict return differences across stocks. Is there a reduced set of factors that most accurately and consistently captures fundamental equity risks? In their March 2018 paper entitled “Searching the Factor Zoo”, Soosung Hwang and Alexandre Rubesam employ Bayesian inference to test all possible multi-factor linear models of stock returns and identify the best models. This approach enables testing of thousands of individual assets in combination with hundreds of candidate factors. They consider a universe of 83 candidate factors: the market return in excess of the risk-free rate, plus 82 factors measured as the difference in value-weighted average returns between extreme tenths (deciles) of stocks sorted on stock/firm characteristics. Their stock universe consists of all U.S. listed stocks excluding financial stocks, stocks with market capitalizations less than the NYSE 20th percentile (microcaps) and stocks priced less than $1. They test microcaps separately. They further test 20 sets of test portfolios (300 total portfolios). The overall sample period is January 1980 through December 2016. To assess factor model performance consistency, they break this sample period into three or five equal subperiods. Using the specified data as available over the 36-year sample period, they find that: Keep Reading

Does implied stock market volatility (IV) predict stock market returns? In their March 2018 paper entitled “Implied Volatility Measures As Indicators of Future Market Returns”, Roberto Bandelli and Wenye Wang analyze the relationship between S&P 500 Index IV and future S&P 500 Index returns. They consider volatilities implied either by S&P 500 Index options (VIX) or by 30-day at-the-money S&P 500 Index straddles. Specifically, they each day:

1. Rank current S&P 500 Index IV according to ranked tenth (decile) of its daily distribution over the past two years. If current IV is higher than any value of IV over the past two years, its rank is 11.
2. Calculate S&P 500 Index returns over the next one, five and 20 trading days.
3. Relate these returns to IV rank.

They calculate statistical significance based on the difference between the average IV-ranked log returns and log returns over all intervals of the same length. Using daily data for the selected variables during December 1991 through November 2017, they find that: Keep Reading

How do housing, equities and government bonds/bills perform worldwide over the long run? In their February 2018 paper entitled “The Rate of Return on Everything, 1870-2015”, Òscar Jordà, Katharina Knoll, Dmitry Kuvshinov, Moritz Schularick and Alan Taylor address the following questions:

1. What is the aggregate real return on investments?
2. Is it higher than economic growth rate and, if so, by how much?
3. Do asset class returns tend to decline over time?
4. Which asset class performs best?

To do so, they compile long-term annual gross returns from market data for housing, equities, government bonds and short-term bills across 16 developed countries (Australia, Belgium, Denmark, Finland, France, Germany, Italy, Japan, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the UK and the U.S.). They decompose housing and equity performances into capital gains, investment incomes (yield) and total returns (sum of the two). For equities, they employ capitalization-weighted indexes to the extent possible. For housing, they model returns based on country-specific benchmark rent-price ratios. Using the specified annual returns for 1870 through 2015, they find that:

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Are there exploitable lead-lag relationships between bonds and stocks, perhaps because bond investors are generally better informed than stock investors or because there is some predictable stocks-bonds rebalancing cycle? To investigate, we examine lead-lag relationships between bond exchange-traded fund (ETF) returns and stock ETF returns. We consider iShares iBoxx $ Investment Grade Corporate Bond (LQD) and  iShares iBoxx $ High-Yield Corporate Bond (HYG) as liquid bond ETFs and SPDR S&P 500 (SPY) as a liquid stock ETF. Using dividend-adjusted daily, weekly and monthly returns for LQD, HYG and SPY during mid-April 2007 (HYG inception) through March 2018, we find that: Keep Reading

How do the corporate experts most responsible for assessing the cost of equity currently feel about future U.S stock market returns? In their March 2018 paper entitled “The Equity Risk Premium in 2018”, John Graham and Campbell Harvey update their continuing study of the views of U.S. Chief Financial Officers (CFOs) and equivalent corporate officers on the prospective U.S. equity risk premium (ERP) relative to the 10-year U.S. Treasury note (T-note) yield, assuming a 10-year investment horizon. Based on 71 quarterly surveys over the period June 2000 through December 2017 (an average 351 responses per survey), they find that: Keep Reading

Are published stock return anomalies exploitable worldwide? In their January 2018 paper entitled “Does it Pay to Follow Anomalies Research? International Evidence”, Ondrej Tobek and Martin Hronec investigate out-of-sample and post-publication performances of 153 cross-sectional stock return anomalies documented in the academic literature, mostly in the top three finance and top three accounting journals. Of the 153 anomalies, 93 involve firm fundamentals, 11 involve firm earnings estimates and 49 involve market frictions. They calculate returns for each anomaly via a hedge portfolio that is long (short) the value-weighted fifth, or quintile, of stocks with the highest (lowest) expected returns for that anomaly. To ensure capacity, they focus on the universe of stocks in the top 90% of NYSE capitalizations. They first examine out-of-sample (after the sample used for discovery but before publication) and post-publication performances of anomalies among U.S. stocks for evidence of performance decay. They then look at anomaly performance outside the U.S. They further test whether strategies that work most widely should be of greatest interest to investors. Finally, they consider a multi-anomaly strategy that each year invests equally in all anomalies that are significant in the U.S. through June, starting in July 1990 for developed country markets and July 2000 for emerging country markets. Using required firm/stock data since July 1963 for the U.S., since January 1987 for Europe, Japan and developed Asia-Pacific and since January 2000 for China and emerging Asia-Pacific, all through December 2016, they find that: Keep Reading

Is the strong gain in the U.S. stock market following the November 2016 national election rational or irrational? In their February 2018 paper “Why Has the Stock Market Risen So Much Since the US Presidential Election?”, flagged by a subscriber, Olivier Blanchard, Christopher Collins, Mohammad Jahan-Parvar, Thomas Pellet and Beth Anne Wilson examine sources of the 25% U.S. stock market advance during November 2016 through December 2017. They consider four sources: (1) increases in actual and expected dividends; (2) perceived probability and the fact of a reduction in the corporate tax rate; (3) decrease in the U.S. equity risk premium; and, (4) an irrational price bubble. For the impact of the tax rate reduction on corporate income, they use estimates from the Joint Congressional Committee on Taxation. For the relationship between dividends and the equity risk premium, they assume the difference between dividend-price ratio and risk-free rate equals equity risk premium minus expected dividend growth rate. They also consider the effect of U.S. and European economic policy uncertainty on the U.S. equity risk premium. Using the specified data during November 2016 (and earlier for validation) through December 2017, they find that: Keep Reading

What is the historical relationship between U.S. stock market earnings yield (E/P) and U.S. government bond yield (Y)? In their February 2018 paper entitled “Stock Earnings and Bond Yields in the US 1871 – 2016: The Story of a Changing Relationship”, Valeriy Zakamulin and Arngrim Hunnes examine the relationship between E/P Y over the long run, with focus on structural breaks, causes of breaks and direction of causality. They employ a vector error correction model that allows multiple structural breaks. In assessing causes of breaks, they consider inflation, income taxes and Federal Reserve Bank monetary policy. Using quarterly S&P Composite Index level, index earnings, long-term government bond yield and inflation data during 1871 through 2016, along with contemporaneous income tax rates and Federal Reserve monetary actions, they find that:

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A subscriber requested review of a finding that deviation of 10-year constant maturity U.S. Treasury note (T-note) yield from an intermediate-term linear trend predicts U.S. stock market return. Specifically, when weekly yield is more than one standard deviation of weekly trend divergences below (above) a weekly 70-week linear extrapolation, next-week S&P 500 Index return is on average unusually high (low). To confirm and test usefulness of this finding, we each week:

1. Perform a linear extrapolation of past T-note yields to forecast next-week T-note yield, but using a 52-week rolling window rather than a 70-week window. A 52-week lookback aligns with an annual inflation cycle, while a 70-week lookback seems arbitrary and may be snooped.
2. Calculate the difference between next-week actual and forecasted T-note yields.
3. Calculate the standard deviation of these differences over the 52-week rolling window.

We then segment weekly actual minus forecasted T-note yield differences into: those more than one standard deviation below forecasted yield (Below Lower); those between one standard deviation below and above forecasted yield (Between); and, those more than one standard deviation above forecasted yield (Above Upper). Next, we calculate next-week S&P 500 Index returns for these three segments. Limited by availability of weekly T-note yield data, return calculations commence January 1964. To check robustness of results, we also consider a recent subsample commencing January 2008. To test economic value of findings, we examine a Dynamic Weighted strategy that modifies a benchmark 60% allocation to SPDR S&P 500 (SPY) and 40% allocation to iShares Barclays 7-10 Year Treasuries (IEF), rebalanced weekly, to 80% SPY when T-note condition the prior week is Below Lower and 40% SPY when Above Upper. The strategy backtest commences with inception of IEF at the end of July 2002 and focuses on weekly return statistics, compound annual growth rate (CAGR) and maximum drawdown (MaxDD), ignoring rebalancing/reallocation frictions. Using weekly T-note yields (average of daily values measured on Friday) and contemporaneous S&P 500 Index levels since January 1962, and weekly dividend-adjusted levels of SPY and IEF since July 2002, all through January 2018, we find that: Keep Reading

Do technical trend trading/intrinsic momentum strategies work for widely used equity factors such as size (small minus big market capitalizations), value (high minus low book-to-market ratios), profitability (robust minus weak), investment (conservative minus aggressive) and momentum (winners minus losers)? In their January 2018 paper entitled “What Goes up Must Not Come Down – Time Series Momentum in Factor Risk Premiums”, Maximilian Renz investigates time variation and trend-based predictability of these five factors and the market factor. He first constructs price series for the six long-short factor portfolios. He then considers seven rules based on a short simple moving average (SMA) crossing above (bullish) or below (bearish) a long SMA measured in trading days: SMA(1, 20), SMA(1, 40), SMA(1, 120), SMA(1, 180), SMA(1, 240), SMA(20, 180) and SMA(20, 240). He also considers two intrinsic (absolute or time series) momentum rules based on change in price over the past 180 or 240 trading days (positive bullish and negative bearish). Motivated by prior research by others, he focuses on SMA(1, 180), daily price crossing its 180-day SMA. He measures trend-based statistical predictability of factor premiums and investigates economic value via a strategy that levers factor exposures between 0 and 1.5 using trend-based signals. Finally, he examines whether incorporating trend information improves accuracies of 1-factor (market), 3-factor (adding size and value) and 5-factor (further adding profitability and investment) models of stock returns. Using daily returns for the six selected U.S. stock market equity factors and for 30 industries during July 1963 through December 2015, he finds that: Keep Reading