Authors - Komendra Sahu, Mallikharjuna Rao K., Sonali Agarwal Abstract - This study examines whether textual complexity in corporate disclosures predicts stock excess returns. Building on prior research using Loughran–McDonald (LM) tone variables, the baseline ordinary least squares (OLS) results are replicated and the analysis is extended in three directions. a novel Corporate Communication Text Complexity Index (CCTI) is developed using structural and linguistic features of SEC 10-K and 10-Q filings. market-based controls, including volatility and momentum, are incorporated. machine learning models are applied to capture potential nonlinear dependencies. Analysis of a large sample of filings from 2009 to 2024 demonstrates that OLS models have near-zero explanatory power, consistent with previous findings. In contrast, Random Forest models significantly improve predictive performance (R2 = 0.19944), indicating that excess returns are influenced by nonlinear patterns in textual complexity. Polynomial regression also reveals a convex relationship, with extreme textual complexity associated with negative excess returns. Analysis of a large sample of filings from 2009 to 2024 confirms that OLS exhibits near-zero explanatory power. This finding is consistent with prior research. In contrast, Random Forest models substantially improve predictive performance (R2 = 0.19944), indicating that excess returns respond to nonlinear patterns in textual complexity. Polynomial regression reveals a convex relationship, where extreme textual complexity is associated with negative excess returns. Overall, these results indicate that market reactions to complexity are inherently nonlinear and cannot be adequately captured by traditional tone-based linear models.
