Research Review | 20 February 2026 | Forecasting Returns

CAPE Ratios and Long-Term Returns
Rui Ma (La Trobe University), et al.
January 2026
We demonstrate that 10-year equity market returns are considerably more predictable in relation to price-earnings ratios than previously thought. The traditional approach involves relating the current index price level, based on current index components, to the index earnings of previous years, calculated using those years’ components. When we estimate the cyclically adjusted price-earnings (CAPE) ratio, ensuring that index component prices and earnings are aligned, and apply a superior regression approach, out-of-sample R 2 values are over 50%. The Component CAPE ratio weights individual stock CAPE ratios by their market capitalization, whereas the traditional CAPE ratio is more closely aligned with earnings weighting.

Multiples for Valuation: Go High, Go Low, Ignore the Middle
Javier Estrada (IESE Business School)
February 2026
Multiples such as D/P, P/E and CAPE have long been viewed as being useful to forecast returns over periods of ten or so years. The evidence discussed in this article supports this belief and takes it one step further by showing that multiples are far more useful when they are relatively high or low than when they are somewhere in the middle of their historical range. In fact, relatively high or low multiples are more highly correlated to forward returns in sample, and produce better return forecasts out of sample, than multiples that lie somewhere in the middle.

Credit Spread News and Financial Market Risk
Fabrizio Ghezzi (University of California San Diego)
December 2025
This paper shows that credit spread news, defined by changes and absolute changes in corporate bond credit spreads, predict a substantial share of future variation in financial market risk. I first document a strong and robust predictive relationship between credit spread news and financial market risk. I then investigate the economic mechanism underlying this relationship and provide both theoretical and empirical evidence highlighting a central role for financial intermediaries’ risk expectations. Together, these findings establish credit spread news as statistically significant and economically meaningful predictors of financial market risk.

Mean-Reversions in the Debt-to-GDP Ratio and Predictability of Treasury Debt Returns and Surpluses
Deshui Yu (Hunan University), et al.
November 2025
The debt-to-GDP (DG) ratio should predict Treasury returns and primary surpluses according to the present-value identity, yet empirical support remains elusive. This paper resolves this puzzle by decomposing the DG ratio into a slow mean-reversion component and a local mean-reversion component. We show that the local mean-reversion of the DG ratio delivers substantially improved out-of-sample forecast gains of Treasury debt returns and surpluses, outperforming the original DG ratio, the historical average benchmark, and the adjusted ratios subject to structural breaks. In contrast, the slow mean-reversion component obscures predictive information by incorporating persistent, non-fundamental variation. Our findings are robust to alternative decomposition methods and DG ratio definitions (including nonmarketable debt). We develop a revised fiscal present-value model to rationalize the findings.

Extracting Forward Equity Return Expectations Using Derivatives
Steven P. Clark (University of North Carolina at Charlotte), et al.
January 2025
This paper develops a framework for extracting conditional expectations of future equity returns from derivative prices. We show that expected returns can be identified not only at the spot horizon, but also for forward-starting investment periods, yielding the full surface of expected future returns. Using index options, we derive theoretical bounds on future returns, and using VIX derivatives, we link risk-neutral and real-world expectations. Empirically, derivative-implied expectations exhibit sharp shifts around major crises, reveal persistent negative dependence between adjacent monthly returns, and generate economically valuable reversal signals. These findings uncover new dimensions of return predictability embedded in derivatives markets.

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