Earnings Call Analysis with NLP

Earnings calls combine prepared comments by company leaders with questions and answers from analysts. NLP can turn this text into features such as word-count sentiment, uncertainty language, topics, readability, or speaker-level measures. Price, Doran, Peterson, and Bliss studied earnings-call textual tone and reported that it contained incremental information related to announcement-window abnormal returns and post-earnings-announcement drift in their sample. Such findings support studying language as one information source; they do not mean a tone score predicts what any particular stock will do next. Language measures have important limits. Generic sentiment dictionaries can misclassify words whose meanings differ in finance, and a positive or negative word can appear in a negated or conditional phrase. Transcript quality, speaker attribution, prepared versus Q&A sections, earnings surprises, industry, and time period can all affect results. A high uncertainty count may reflect question difficulty, cautious wording, or transcription choices rather than undisclosed negative news. Researchers should specify the text source, unit of analysis, lexicon or model, labels, and outcome horizon. A responsible analysis compares language features with baseline numeric data and evaluates them on later data that was not used for model selection. Check whether effects survive controls and alternate specifications, and report uncertainty rather than turning correlations into a buy/sell instruction. NLP can help summarize and investigate communication, but it cannot establish intent, truth, or future financial performance on its own. Investment decisions require broader analysis and individual context beyond this educational guide.