Macroeconomic Announcement Premium
Hengjie Ai (University of Wisconsin-Madison), et al.
November 2023
The paper reviews the evidence on the macroeconomic announcement premium and its implications on equilibrium asset pricing models. Empirically, a large fraction of the equity market risk premium is realized on a small number of trading days with significant macroeconomic announcements. We review the literature that demonstrates that the existence of the macroeconomic announcement premium implies that investors’ preferences must satisfy generalized risk sensitivity. We show how this conclusion generalizes to environments with heterogeneous investors and demonstrate how incorporating generalized risk sensitivity affects economic analysis in dynamic setups with uncertainty.

The Determinants of the Time-Varying Equity Premium
Austin Murphy and Zeina N. Alsalman (Oakland University)
March 2024
The ex-ante excess return required by investors for taking positions in the stock market is found to be lower when there are greater past dividend growth rates, higher past stock returns, narrower credit spreads, lower unemployment rates, faster past consumption and money growth, higher values for the dollar, more elevated existing/expected interest rates, and less inflation. The findings indicate that the equity risk premium tends to fall (rise) with improving (deteriorating) financial/economic fundamentals. Measures of investor sentiment (stock market volatility) are found to separately have only a minor (insignificant) impact on the equity

Equity Risk Premiums (ERP): Determinants, Estimation, and Implications – The 2024 Edition
Aswath Damodaran (New York University)
February 2024
The equity risk premium is the price of risk in equity markets, and it is not just a key input in estimating costs of equity and capital in both corporate finance and valuation, but it is also a key metric in assessing the overall market. Given its importance, it is surprising how haphazard the estimation of equity risk premiums remains in practice. We begin this paper by looking at the economic determinants of equity risk premiums, including investor risk aversion, information uncertainty and perceptions of macroeconomic risk. In the standard approach to estimating the equity risk premium, historical returns are used, with the difference in annual returns on stocks versus bonds, over a long period, comprising the expected risk premium. We note the limitations of this approach, even in markets with an abundance of data, like the United States, and its complete failure in emerging markets, where the historical data tends to be limited and volatile. We look at two other approaches to estimating equity risk premiums – the survey approach, where investors and managers are asked to assess the risk premium and the implied premium approach, where a forward-looking estimate of the premium is estimated using either current equity prices or risk premiums in other markets. In the next section, we look at the relationship between the equity risk premium and risk premiums in the bond market (default spreads) and in real estate (cap rates) and how that relationship can be mined to generate expected equity risk premiums. We close the paper by examining why different approaches yield different values for the equity risk premium, and how to choose the “right” number to use in analysis.

Equity Premium Events
Benjamin Knox (Federal Reserve), et al.
February 2024
We develop a methodology to determine which days are “equity premium events”: events with significantly elevated equity premia relative to the daily equity term structure. To do so, we use recently available daily S&P 500 option expirations and forward analogs of the Martin (2017) and Tetlock (2023) measures of the equity premium. We use a data-driven approach to identify events that are significantly priced by equity markets without taking a stance on what those events are. Important events include a variety of economic and political events. In the cross-section of macroeconomic releases, FOMC, CPI, and nonfarm payrolls have the largest abnormal equity premia, which increase substantially between June 2022 and June 2023. However, the elevated equity premia on macroeconomic release days are quantitatively far from explaining the large realized excess returns documented in previous work, suggesting a role for unexpectedly good news. To provide intuition for the variation in event equity premia across announcement types and time, we propose an asset pricing framework that decomposes the equity premium for a given macroeconomic release into components due to news variance and the sensitivities of the stock market and the SDF to the news released.

Macroeconomic Expectations and Expected Returns
Yizhe Deng (China Investment Corporation), et al.
March 2024
Using the macroeconomic forecasts of professional economists, we construct a comprehensive macro condition index that summarizes subjective expectations of output, inflation, and labor and housing market conditions. The index predicts stock returns and produces countercyclical equity premium forecasts, both in- and out-of-sample. Our results contrast with the procyclical subjective equity premia documented in recent literature. We show that the index reflects the true but unobserved macroeconomic condition that impacts the equity premium. Moreover, the predictability is not affected by belief biases and operates via a discount rate channel. The index’s predictability conforms to an explanation based on time-varying risk premia.

Forecasting the Equity Premium: Can Machine Learning Beat the Historical Average?
Xingfu Xu and Wei-Han Liu (Southern University of Science and Technology)
August 2023
We empirically predict the equity premium with the selected machine learning methods in Gu, Kelly, and Xiu (2020). We also consider other four popular machine learning methods (support vector regression, k-nearest neighbors, adaptive boosted trees, and extreme gradient boosted trees) and their combination method. Using a large dataset of both macroeconomic and technical predictors, we find that most machine learning methods, especially two tree-based models (random forest and extreme gradient boosted trees), can achieve a very high in-sample fit. However, when it comes to the out-of-sample forecasts of different evaluation periods, our findings support Welch and Goyal (2008) that the competing forecasting models generally fail to outperform the historical average benchmark. Our results are robust to the choice of window estimation schemes, data frequencies, and alternative macroeconomic datasets. Finally, we provide explanations for this failure.

Survey: Market Risk Premium and Risk-Free Rate used for 96 countries in 2024
Pablo Fernandez (IESE Business School), et al.
March 2024
This paper contains the statistics of a survey about the Risk-Free Rate (RF) and the Market Risk Premium (MRP) used in 2024 for 96 countries. We got answers for 104 countries, but we only report the results for 96 countries with more than 6 answers. The paper also contains the links to previous years surveys, from 2008 to 2023.

Equity Premium in Efficient Markets
BN Kausik (independent)
November 2023
Equity premium, the surplus returns of stocks over bonds, has been an enduring puzzle. While numerous prior works approach the problem assuming the utility of money is invariant across contexts, our approach implies that in efficient markets the utility of money is polymorphic, with risk aversion dependent on the information available in each context, i.e. the discount on each future cash flow depends on all information available on that cash flow. Specifically, we prove that in efficient markets, informed investors maximize return on volatility by being risk-neutral with riskless bonds, and risk-averse with equities, thereby resolving the puzzle. We validate our results on historical data with surprising consistency.

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Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno

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