The Most Dangerous (and Ubiquitous) Shortcut in Financial Planning
John West and Amie Ko (Research Affiliates)
Using historical returns to forecast the future is one of the most common shortcuts in financial planning. Investment advisors who use only past returns to forecast future returns may well be creating unrealistic expectations and poor investment outcomes for their clients. Our online Asset Allocation Interactive, which uses starting yields to forecast future long-term returns, gives advisors a rich toolkit with which to construct portfolios most likely to achieve their clients’ financial goals.
The World Price of Rare Disaster
Jian Chen (Xiamen University), et al.
This paper examines whether perceived rare disaster risks can predict future stock returns in international markets. Relying on six measures of news implied disaster concerns constructed by Manela and Moreira (2017), we use the dynamic factor analysis method to respectively construct an aggregate ex ante measure of rare disasters for each market to capture time-varying risk exposure to various rare disaster events (e.g., wars, economic disasters, and natural disasters) in each market. We find that the measures of perceived rare disaster risks can predict stock returns both in-sample and out-of-sample in international markets. These measures can also generate economic values. Consistent with economic theories, our analysis shows that the ex ante measures of rare disasters predict a fall of future consumption and a rise of marginal utility.
When (If Ever) Has it Paid to Wait for a Stock Market Correction?
Victor Haghani and James White (Elm Partners)
Investors are periodically challenged with this question: with funds ready to invest, but faced with a market that is generally perceived to be expensive, is it better to wait for a market correction before investing? Many investors are certain that a correction must be around the corner, and thus little downside exists to holding excess cash. We explore the question of whether the historical record supports their near-certainty by examining the past 115 years of US stock market history. We conclude that while there may be valid reasons to hold excess cash based on specific forward-looking return estimates, the historical record does not suggest that, conditioned on a variety of entry criteria, waiting for a correction has positive expected return.
Inferring Aggregate Market Expectations from the Cross-Section of Stock Prices
Turan G. Bali (Georgetown University), et al.
We introduce a new approach to predicting market returns that combines the cross-section of dividends, earnings, and book values to explain current stock prices and extract aggregate expected returns. Our measure of market return expectations is strongly correlated with popular ex-ante equity premium measures and business cycle variables. It portends a significant fraction of the time-series variation in stock market returns at horizons of one month to two years. Consistent with the theory on which our measure is based, the predictability that we uncover is particularly strong at longer return forecasting horizons, where it dominates that afforded by popular predictive variables, achieving an out-of-sample R2 of 12.6 percent at the annual horizon. We infer aggregate risk aversion using our measure and find estimates that are economically sensible and vary over time with the business cycle.
Be Fearful When Households are Greedy: The Household Equity Share and Expected Market Returns
David C. Yang (U. of California, Irvine) and Fan Zhang (PrepScholar Education)
We empirically document that the “dumb money” effect exists for the aggregate stock market. We define the “Household Equity Share” (HEShare), the share of household equity and fixed income assets allocated to equities. HEShare negatively forecasts excess returns on the aggregate US stock market, both univariately and after controlling for past changes in equity prices and common market return forecasters. The non-household sector’s equity share does not forecast returns, ruling out economy-wide explanations for HEShare’s return predictability. At times, HEShare predicts negative mean excess returns on the market, suggesting that behavioral factors explain our findings.
Mean-Variance Optimization Using Forward-Looking Return Estimates
Patrick Bielstein (EDHEC) and Matthias X. Hanauer (Robeco Asset Mgt.)
Despite its theoretical appeal, Markowitz mean-variance portfolio optimization is plagued by practical issues. It is especially difficult to obtain reliable estimates of a stock’s expected return. Recent research has therefore focused on minimum volatility portfolio optimization, which implicitly assumes that expected returns for all assets are equal. We argue that investors are better off using the implied cost of capital based on analysts’ earnings forecasts as a forward-looking return estimate. Correcting for predictable analyst forecast errors, we demonstrate that mean-variance optimized portfolios based on these estimates outperform on both an absolute and a risk-adjusted basis the minimum volatility portfolio as well as naive benchmarks, such as the value-weighted and equally-weighted market portfolio. The results continue to hold when extending the sample to international markets, using different methods for estimating the forward-looking return, including transaction costs, and using different optimization constraints.
The Conditional Expected Market Return
Fousseni Chabi-Yo (U. of Mass. Amherst) and J. A. Loudis (U. of Chicago)
We derive lower bounds on the conditional expected excess market return and log market returns. The bounds are related to a volatility index, skewness index, and a kurtosis index. The bounds can be calculated in real time at any date using the cross-section of option prices. The bounds do not depend on any distributional assumptions about market returns or past observations. In this sense, they are model-free. We find that the bounds are highly volatile, positively skewed, and exhibit fat tails. Over our sample, the expected excess market return is on average 5%, while the expected excess log return is on average 3%.
Good and Bad Variance Premia and Expected Returns
Mete Kilic (U. of So. California) and Ivan Shaliastovich (U. of Wisconsin)
We measure “good” and “bad” variance premia that capture risk compensations for the realized variation in positive and negative market returns, respectively. The two variance premium components jointly predict excess returns over the next 1 and 2 years with statistically significant positive (negative) coefficients on the good (bad) component. The R2s reach about 10% for aggregate equity and portfolio returns, and 20% for corporate bond returns. To explain the new empirical evidence, we develop a model that highlights the differential impact of upside and downside risk on equity and variance risk premia