 In the shadows of opacity: Firm information quality and latent factor model performanceChuyu Wang and Guanglong Zhang International Review of Financial Analysis, 2025, vol. 100, issue C Abstract: Little is known about how the performance of latent factor models is affected by the quality of firm-disclosed data. Using Chinese data, we demonstrate the superiority of conditional latent factor models (exemplified by the instrumented principal component analysis, IPCA) over unconditional latent factor models (risk-premium principal component analysis, RP-PCA; cross-sectional and time-series principal component analysis, XS-TS-Target-PCA). IPCA’s outperformance is generally more pronounced in explaining trading-based firm characteristics than accounting-based ones. However, in emerging markets such as China, IPCA’s performance is attenuated by the lower quality of firm-disclosed information and poorer stock liquidity. We make the first attempt to investigate how IPCA’s performance is affected by more opaque information environments in emerging markets. Keywords: Latent factor model; Firm information quality; Accounting-based firm characteristics; Trading-based firm characteristics (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:100:y:2025:i:c:s1057521925000572 DOI: 10.1016/j.irfa.2025.103970 Access Statistics for this article International Review of Financial Analysis is currently edited by B.M. Lucey More articles in International Review of Financial Analysis from Elsevier |
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