Switching between states and the COVID-19 turbulence

Paper: arXiv 2512.20477

Authors: Ilias Aarab

Abstract

In Aarab (2020), I examine U.S. stock return predictability across economic regimes and document evidence of time-varying expected returns across market states in the long run. The analysis introduces a state-switching specification in which the market state is proxied by the slope of the yield curve, and proposes an Aligned Economic Index built from the popular predictors of Welch and Goyal (2008) (augmented with bond and equity premium measures). The Aligned Economic Index under the state-switching model exhibits statistically and economically meaningful in-sample ($R^2 = 5.9%$) and out-of-sample ($R^2_{"\text{oos"}} = 4.12%$) predictive power across both recessions and expansions, while outperforming a range of widely used predictors. In this work, I examine the added value for professional practitioners by computing the economic gains for a mean-variance investor and find substantial added benefit of using the new index under the state switching model across all market states. The Aligned Economic Index can thus be implemented on a consistent real-time basis. These findings are crucial for both academics and practitioners as expansions are much longer-lived than recessions. Finally, I extend the empirical exercises by incorporating data through September 2020 and document sizable gains from using the Aligned Economic Index, relative to more traditional approaches, during the COVID-19 market turbulence.

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs a state-switching regression model and partial least squares (PLS) for index construction, indicating non-trivial econometric complexity. It also features extensive empirical validation, including in-sample and out-of-sample metrics, portfolio allocation exercises with transaction costs, and analysis through September 2020 to assess performance during COVID-19 turbulence.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Predict U.S. Equity Premium across Economic Regimes"] --> B{"Methodology"}
  
  B --> C["State-Switching Model<br>Market State = Yield Curve Slope"]
  B --> D["Aligned Economic Index<br>Welch & Goyal (2008) + Bond/Equity Premium"]
  
  C --> E["Computational Process<br>In-Sample & Out-of-Sample Analysis"]
  D --> E
  
  E --> F["Outcomes & Findings"]
  
  F --> G["In-Sample: R² = 5.9%<br>Out-of-Sample: R² = 4.12%"]
  F --> H["Economic Gains<br>Mean-Variance Investor"]
  F --> I["COVID-19 Extension<br>Sizable gains during turbulence"]

Cited References (40)

1. A Comprehensive Look at the Empirical Performance of Equity Premium Prediction (2008) — link
2. Dividend Yields and Expected Stock Returns (1988) — link
3. Stock Prices, Earnings, and Expected Dividends (1988)
4. On the Predictability of Stock Returns: An Asset-Allocation Perspective (1996) — link
5. On the Out-of-Sample Predictability of Stock Market Returns (2002) — link
6. Predicting Returns with Financial Ratios (2004) — link
7. Cross-Sectional Forecasts of the Equity Premium (2006)
8. Forecasting Stock Returns: A Predictor-Constrained Approach (2018)

0 of 40 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.