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In the shadows of opacity: Firm information quality and latent factor model performance

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  • Wang, Chuyu
  • Zhang, Guanglong

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.

Suggested Citation

  • Wang, Chuyu & Zhang, Guanglong, 2025. "In the shadows of opacity: Firm information quality and latent factor model performance," International Review of Financial Analysis, Elsevier, vol. 100(C).
    • Handle: RePEc:eee:finana:v:100:y:2025:i:c:s1057521925000572
    DOI: 10.1016/j.irfa.2025.103970
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    More about this item

    Keywords

    Latent factor model; Firm information quality; Accounting-based firm characteristics; Trading-based firm characteristics;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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