Rong Wang (汪融)

Working Papers

Resolving the Zero-Beta Rate Puzzle , May 2026

This paper resolves a long-standing zero-beta rate puzzle—the empirical finding that estimated zero-beta rates remain persistently high across factor models. I show that this apparent robustness arises from pervasive model misspecification rather than reflecting a genuinely high risk-free rate. When a factor model fails to perfectly price assets, the zero-beta rate is no longer uniquely identified, and the conventional estimator—based on the minimum-variance zero-beta portfolio—converges toward the mean return of the global minimum-variance portfolio as model misspecification increases. To quantify this mechanism, I introduce a new investment-based measure of model misspecification: the maximum Sharpe ratio attainable by zero-investment, zero-beta portfolios. This measure captures the economic magnitude of pricing errors and links model misspecification to empirically observable investment opportunities. Studying a comprehensive set of classical and modern factor models, I find substantial misspecification, explaining why all models yield similarly elevated zero-beta rates. Simulation analyses confirm that realistic degrees of misspecification can fully reproduce the empirical magnitude of the puzzle even when the true risk-free rate is low.

* Conferences: AFA 2025 Ph.D. Poster Session, SFS Cavalcade North America 2026, WFA 2026, CES China 2026, SFA 2026

Factor Investing and Factor-Neutral Investing , November 2025

Factor investing based on machine-learning (ML) models promises high out-of-sample Sharpe ratios but fails to deliver in practice. I show that the out-of-sample performance of factor mean–variance portfolios constructed from leading ML models is economically unviable, turning strongly negative once realistic trading costs are accounted for. In contrast, factor-neutral investing—which builds optimal zero-beta portfolios to exploit model mispricing rather than harvest factor premia—proves far more robust and implementable. The zero-beta constraints act as a natural shrinkage device, mitigating turnover and improving resilience to trading frictions. In a comprehensive out-of-sample backtest spanning 1990–2024, factor-neutral strategies achieve net-of-cost annualized Sharpe ratios between 0.6 and 1.2 and monthly alphas of 0.4%–1.5%, unexplained by known factors. These results imply that, in practice, the most valuable signal in ML-based factor models is not their estimated factors, but their errors.

Fiscal Policy and the Government Debt Maturity Structure , January 2023

This paper investigates how the maturity structure of government debt influences the transmission of fiscal policy shocks. Using local projection methods combined with external instrumental variables, I find that a longer maturity of government debt significantly attenuates the expansionary effects of fiscal policy on output and inflation. A theoretical model grounded in the fiscal theory of the price level provides a coherent interpretation of these empirical results. The key mechanism is that a longer debt maturity enables the government to realize greater capital gains on its outstanding debt following a fiscal expansion, thereby reducing the incentive to generate inflationary pressures to erode the real value of debt.

Work in Progress

Fiscal Shocks, Beliefs, and Asset Prices with Anna Cieslak and Hao Pang

We develop new narrative, high-frequency fiscal policy shocks that cover a wide range of fiscal actions, including government budgeting, legislation bills, sovereign credit rating events, debt limit discussions, Treasury auctions, and defense spending, among others. Using these shocks, together with textual analysis of a large corpus of news articles, we investigate the impact of fiscal policy on financial markets.

What Has Gone Wrong in the Best Factor Models

I investigate the sources of pervasive factor model misspecification. Using zero-beta portfolios as diagnostic tools, I trace where risk pricing breaks down across model specifications, asset groups, and market conditions. The analysis distinguishes whether these failures arise from missing risk factors, model instability, non-linear pricing, or limits to arbitrage. By uncovering its economic origins, the paper seeks to explain why state-of-the-art factor models—despite their sophistication—continue to leave large and systematic pricing errors unexplained.