Giulio Rossetti
Post-Doc Researcher
WBS | University of Warwick
Research
Working Papers
Sorting Bonds, Finding Issuers: What Corporate Bond Factors Trade Job Market Paper
The Corporate Bond Factor Replication Crisis, R&R
Abstract
Corporate bond factor research faces a replication crisis. The crisis stems from two sources that inflate reported factor premia: transaction prices whose measurement error enters both sorting signals and return denominators, creating a correlated errors-in-variables bias, and asymmetric ex-post return filtering that embeds future information into factor construction. Applying our framework to a "factor zoo" of 108 signals across nine thematic clusters, we show that the majority of previously documented factors do not produce statistically significant bond CAPM alphas after correction. We provide an open source framework via Open Bond Asset Pricing, including error-corrected TRACE data, bias-corrected factors, and software for reproducible research.
Keywords: Corporate bond factors; Open source; Factor zoo; Replication crisis; Measurement error; Look-ahead bias; Non-standard errors.
JEL Classification: C12; C13; C58; G11; G12.
Misspecification and Weak Identification in the Nontraded Factor Zoo, Submitted
Abstract
We conduct a broad investigation of whether nontraded factors price the cross-section of returns, accounting for weak identification and global model misspecification. We collect 102 nontraded factors from 51 papers in mainstream finance and economics journals and replicate 145 published specifications using widely used test portfolios. Because these factors are often weakly correlated with asset returns and their models are likely misspecified, conventional risk-premium tests may overstate their pricing ability. Standard econometric methods classify up to 39% as priced, whereas robust procedures reduce that share to 4%–13%; multiple-testing adjustments weaken the evidence, and adding the market factor worsens identification. Complementary beta-sorted portfolio analysis provides similar results: only 11.2% of long–short strategies earn statistically significant average returns, with small premia. Our results do not necessarily imply that nontraded factors are unpriced, but most published proxies receive little support from robust pricing tests or portfolio sorts immune to weak identification.
Common Pitfalls in the Evaluation of Corporate Bond Strategies, Permanent Working Paper
Abstract
We argue that the documented large abnormal returns to investors from a wide array of corporate bond strategies mainly stem from ignoring market microstructure noise in transaction-based bond prices and relying on (ad hoc and asymmetric) out-of-sample return trimming or winsorization. To address these issues, we construct bond data that is largely free of microstructure noise and closely mimics industry-grade quote data. We revisit prior findings in the literature and provide conclusive evidence that most bond anomaly portfolios/factors, once properly constructed, generate negligible average returns and alphas. Finally, we show that the considered factors (and their underlying signals) are only weakly related to average bond returns.
Data and Code: See the Data downloads section on the companion website here.
On the Statistical Properties of Tests of Parameter Restrictions in Beta-Pricing Models with a Large Number of Assets
Abstract
We study the size and power properties of t-tests of parameter restrictions for newly-designed methods that aim at reliably estimating risk premia in linear asset pricing models when the cross-sectional dimension is large. By simulating a variety of empirically calibrated data generating processes for sample sizes that are typically encountered in empirical work, we evaluate the finite-sample performance of the test statistics for scenarios where the factor structure is (i) strong and pervasive; (ii) spurious; (iii) weak/semi-strong and pervasive; (iv) weak/semi-strong and not pervasive; and (v) sparse. PCA-based methods such as those of Lettau and Pelger (2020), Giglio and Xiu (2021), and Giglio et al. (2022) work best when the factors are strong and pervasive, and they continue to exhibit good finite-sample properties when the factors are spurious. However, when the factor structure is semi-strong and pervasive, the split-sample estimator of Anatolyev and Mikusheva (2021) performs substantially better than the PCA-based estimators listed above. In the case of sparse loadings or when the factors are semi-strong and not pervasive, none of the candidate methods displays satisfactory finite-sample properties.
Bond Return Predictability and Anchoring Bias
Abstract
This paper investigates whether deviations of macroeconomic variables from their historical moving averages—capturing investors' anchoring biases—enhance the predictability of bond excess returns. Utilizing a large dataset of macroeconomic indicators and shrinkage methods, we construct a deviation index following the approach of Avramov et al. (2022) to evaluate predictive power against traditional yield-curve predictors. Although deviations from past macroeconomic values demonstrate substantial in-sample forecasting ability, particularly through sparse and principal component analysis, this success does not extend robustly out-of-sample. Our findings indicate that, despite strong theoretical motivation from behavioral finance literature, anchoring biases captured via deviations from historical averages provide limited practical improvement over standard predictors in forecasting bond risk premia.