Linguistic Alphas: Decoding the Market Impact of Words in Software Earnings Calls

Markets do not just move on numbers, they move on words. A single phrase in an earnings call can shift billions in market value, yet traditional financial models overlook the power of language. This study uncovers a hidden financial signal in corporate speech, analyzing 437 Software as a Service companies and 40,574 unique words from earnings calls (2003-2024). We introduce a context-aware linguistic model that categorizes words into technicisms, proper names, and mundane words, capturing how subtle phrasing shifts investor sentiment. By distinguishing between commonly overlooked linguistic structures and industry-specific jargon, our approach refines the interpretation of financial discourse. A novel weighting methodology enhances sentiment classification by 74% (experimental) and 42% (theoretical) over traditional models, improving market prediction beyond the Fama French 5 factor model. The impact is substantial. A portfolio built on these linguistic insights yields 30% annualized returns versus the market’s 20%, with a Beta of 0.78. These findings prove that language is not just a reflection of market sentiment, it is an investable asset class. Our results demonstrate that even seemingly neutral words influence investor perception, affecting stock performance in ways conventional models fail to capture. By integrating financial linguistics with asset pricing, this study provides a scalable and adaptable framework for extracting predictive signals from textual data. As Artificial Intelligence-driven financial analysis becomes more sophisticated, mastering the language of finance will be essential for maintaining a competitive edge in market prediction.