Earnings Conference Calls and Stock Returns: The interplay between tone and information specificity
After the adoption of RegFD, quarterly earnings conference calls (ECCs) have become an important channel for voluntary corporate disclosure. However, the extent to which the information content affects capital markets is little researched. Using Stanford NER algorithm and Loughran and McDonald (2011) wordlists on 2,639 conference calls transcripts, I examine the incremental informativeness of quarterly earnings conference calls in terms of tone and specificity, and the impact of their interplay in capital markets. In this thesis, I perform a path analysis including analysts’ tone and its impact on capital markets, measured in terms of cumulative abnormal returns in a two-day window (CAR 0,1). The mediating variable is information specificity, as a proxy for a quality characteristic of information driven by analysts’ tone. I find that analysts’ tone is a significant direct and indirect predictor of abnormal returns (CAR 0,1). While the direct path has been confirmed by prior literature, I document the importance of indirect paths of how analysts’ tone affects CAR (0,1). Specifically, I document that a more negative analysts’ tone during Q&A sessions affects positively information specificity disclosed by managers in their answers. Further, I document that information specificity is associated positively with CAR (0,1). My findings are consistent with prior literature establishing the importance of Q&A session of ECCs in explaining post-earnings announcement drifts. Further, I contribute by documenting a significant mediating path in the analysis, information specificity. These findings are of interest to investors, analysts and regulators to better understand the analysts’ role in shaping firms’ information environment and quality.
MSc in Accounting and Financial Management
cumulative abnormal returns
Master Degree Project