The Internet Appendix collects additional empirical results supporting the main text. We show that our imputation results are robust to different masking mechanisms and over time. We also confirm that our imputation results are robust with respect to filters based on market capitalization, share...

This paper reviews the literature on idiosyncratic equity volatility since the publication of “Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk” in 2001. We respond to replication studies by Chiah, Gharghori, and Zhong and by Leippold and Svaton,...

This paper reviews the literature on idiosyncratic equity volatility since the publication of “Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk” in 2001. We respond to replication studies by Chiah, Gharghori, and Zhong and by Leippold and Svaton,...

This paper considers extensions of 2-dimensional factor models to higher-dimension data that can be represented as tensors. I describe decompositions of tensors that generalize the standard matrix singular value decomposition and principal component analysis to higher dimensions. I estimate the...

This paper considers extensions of 2-dimensional factor models to higher-dimension data that can be represented as tensors. I describe decompositions of tensors that generalize the standard matrix singular value decomposition and principal component analysis to higher dimensions. I estimate the...

This paper reviews the literature on idiosyncratic equity volatility since the publication of "Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk" in 2001. We respond to replication studies by Chiah, Gharghori, and Zhong and by Leippold and Svaton, and we...

Missing data is a prevalent, yet often ignored, feature of company fundamentals. In this paper, we document the structure of missing financial data and show how to systematically deal with it. In a comprehensive empirical study we establish four key stylized facts. First, the issue of missing...

Machine learning methods in asset pricing are often criticized for their black box nature. We study this issue by predicting corporate bond returns using interpretable machine learning on a high-dimensional bond characteristics dataset. We achieve state-of-the-art performance while maintaining...

This paper proposes latent factor models for multidimensional panels called 3D-PCA. Factor weights are constructed from a small set of dimension-specific building blocks, which give rise to proportionality restrictions of factor weights. While the set of feasible factors is restricted, factors...