[Paper Review] Information Asymmetry Index: The View of Market Analysts

Roberto Frota Décourt, Heitor Almeida+2 more|arXiv (Cornell University)|Sep 10, 2024

Complex Systems and Time Series AnalysisEconomics, Econometrics and Finance3 citations

TL;DR

This paper proposes an Information Asymmetry Index (IAI) based on market analysts' perceptions, using an Elo-rating-inspired algorithm to rank firms by perceived information transparency. The index shows strong predictive power, with coverage, volatility, Tobin’s q, and firm size as significant determinants of informational asymmetry.

ABSTRACT

The purpose of the research was to build an index of informational asymmetry with market and firm proxies that reflect the analysts' perception of the level of informational asymmetry of companies. The proposed method consists of the construction of an algorithm based on the Elo rating and captures the perception of the analyst that choose, between two firms, the one they consider to have better information. After we have the informational asymmetry index, we run a regression model with our rating as dependent variable and proxies used by the literature as the independent variable to have a model that can be used for other researches that need to measure the level of informational asymmetry of a company. Our model presented a good fit between our index and the proxies used to measure informational asymmetry and we find four significant variables: coverage, volatility, Tobin q, and size.

Motivation & Objective

• To develop a novel index measuring informational asymmetry based on market analysts' subjective assessments of firm information transparency.
• To create a replicable, data-driven method that captures analysts' choices between firms in terms of perceived information quality.
• To validate the index against established financial proxies used in the literature to measure informational asymmetry.
• To provide a standardized, research-ready measure of informational asymmetry for use in future empirical finance studies.

Proposed method

• Adapt the Elo rating system to model analysts' pairwise choices between firms, treating each choice as a match where the preferred firm gains rating points.
• Construct a dynamic rating system where firms are ranked based on the frequency and consistency of being selected as having better information by analysts.
• Use the final ratings as the Information Asymmetry Index (IAI), with higher values indicating lower perceived information asymmetry.
• Calibrate the model using analyst coverage data, firm characteristics, and historical rating choices to ensure stability and sensitivity to key variables.
• Validate the index through regression analysis, using standard proxies for informational asymmetry as dependent variables.
• Apply statistical modeling to test the relationship between the IAI and firm-level fundamentals such as size, volatility, and Tobin’s q.

Experimental results

Research questions

• RQ1 Can analysts' pairwise choices between firms be systematically modeled to infer a measure of perceived informational asymmetry?
• RQ2 How well does the proposed Information Asymmetry Index correlate with established proxies for information asymmetry in financial markets?
• RQ3 Which firm-level characteristics significantly predict the level of perceived information asymmetry as captured by the IAI?
• RQ4 To what extent does the IAI improve the explanatory power of models measuring information asymmetry compared to traditional proxies?

Key findings

• The Information Asymmetry Index (IAI) demonstrates a strong fit with established proxies for informational asymmetry, indicating high reliability.
• Coverage, volatility, Tobin’s q, and firm size were found to be statistically significant predictors of the IAI, with p-values below conventional thresholds.
• Firms with higher analyst coverage and lower volatility were consistently rated as having less information asymmetry, aligning with theoretical expectations.
• Tobin’s q and firm size showed significant negative correlations with the IAI, suggesting that more valuable and larger firms are perceived as more transparent.
• The Elo-based algorithm effectively captures analysts’ perceptions, producing a stable and interpretable index across time and firm samples.
• The model’s regression results confirm that the IAI can serve as a valid, alternative measure of informational asymmetry in empirical finance research.