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- W4366998576 abstract "Determining which companies are more difficult to value is a topic of significant interest in finance. Prior studies have employed many univariate proxies for valuation uncertainty to classify firms into high- and low-valuation uncertainty groups. In this study, I employ principal component analysis (PCA), a dimensionality reduction technique, using 11 valuation uncertainty proxies to extract the valuation uncertainty latent factor contained in the first principal component, which is proposed as a new measure of a firm’s valuation uncertainty, and show that it accurately captures a firm’s valuation uncertainty. I contend that using a PCA-derived valuation uncertainty index offers two benefits. First, integrating multiple valuation uncertainty proxies into a single metric improves our ability to quantify a firm’s valuation uncertainty. Second, it can assist in identifying the proxies that are most useful in measuring a firm’s valuation uncertainty." @default.
- W4366998576 created "2023-04-27" @default.
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- W4366998576 date "2023-04-26" @default.
- W4366998576 modified "2023-10-18" @default.
- W4366998576 title "MEASURING VALUATION UNCERTAINTY: A PCA APPROACH" @default.
- W4366998576 doi "https://doi.org/10.24135/afl.v12i1.605" @default.
- W4366998576 hasPublicationYear "2023" @default.
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