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Asia-Pacific Financial Markets

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22.10.2021 | Original Research

Information Quality and the Expected Rate of Return: A Structural Equation Modelling Approach

verfasst von: Max Schreder, Pawel Bilinski

Erschienen in: Asia-Pacific Financial Markets | Ausgabe 2/2022

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Abstract

We use structural equation modelling for a robust test of the role information quality plays in explaining the cost of equity capital (CoE). SEM allows us to reliably identify the direct and indirect effects that three information quality attributes, quantity, asymmetry and precision, have on CoE. The method also reduces the error-in-variables problem, which stems from selectivity in proxies for information quality and CoE. Using nine proxies to capture the variation in information quality attributes and nine CoE measures, we document that the direct effects of precision and asymmetry are equally important in explaining variation in CoE, while quantity has a negative direct effect. Quantity has a positive indirect effect on CoE mediated through asymmetry and precision. The strength of the relations we identify varies according to firm size, maturity, profitability and with proxies for CoE, which suggests that sample compositions and measurement choices affect the power of tests. Our results consolidate mixed evidence on the relation between information quality and CoE that is often based on a single measure of information quality and ignores indirect channels.

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Quantity reflects the amount of information available to investors, precision captures information precision, and asymmetry reflects information asymmetry between informed and uninformed investors.

To illustrate, Lambert et al. (2012) argue that Easley and O’Hara’s (2004) negative association between information asymmetry and CoE is driven by changes in information precision.

In other words, SEM reduces the likelihood of Type II error. Section 2 discusses SEM in more detail, particularly how it differs from principal component analysis and structural modelling.

The lack of an association between precision and analyst forecast accuracy may reflect that analyst forecast accuracy is a ‘catch-all’ measure for the overall quality of a firm’s information environment, rather than a proxy for precision alone. This result is intuitive as analyst forecast accuracy not only depends on the precision of information, but also on analysts’ access to private compared to public information, which reflects the level of asymmetry, and on the amount of information available to them, which reflects quantity.

The partial least squares (PLS) technique is similar to principal component analysis, as the method first applies principal component analysis to the data and then performs least square regressions on the components (Huang et al. 2015, Giglio and Xiu 2017, Giovannelli, et al. 2018). Thus, PLS suffers from the same drawbacks as principal component analysis.

Before estimating the SEM models, we standardise all variables to mean zero and variance one to make coefficients comparable between variables. Cronbach’s alphas are based on standardised variables.

The reported R² calculations are independent and do not add up to 100%.

In the untabulated results, we find that fit statistics for our model are consistent with suggestions in Hut and Bentler (1999) with good levels of fit for the structural model (SRMR: 0.048) and acceptable levels for the measurement model (CFI: 0.912; TLI: 0.873; RMSEA: 0.013).

Unreported ANOVA tests show that mean levels for Age, Listing, Market Cap and ROA are significantly different between exchanges.

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Titel: Information Quality and the Expected Rate of Return: A Structural Equation Modelling Approach
verfasst von: Max Schreder
Pawel Bilinski
Publikationsdatum: 22.10.2021
Verlag: Springer Japan
Erschienen in: Asia-Pacific Financial Markets / Ausgabe 2/2022
Print ISSN: 1387-2834
Elektronische ISSN: 1573-6946
DOI: https://doi.org/10.1007/s10690-021-09342-8

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