In empirical asset pricing, traditional numerical firm characteristics often fall short in fully encapsulating firms’ performances, as highlighted by Ralph (2023). To address this limi- tation, researchers have increasingly turned to alternative data sources, such as news, 10K filings, conference calls, to augment predictive models for stock price movements (Gabaix et al., 2023; Glodd & Hristova, 2023). In this project, we leverage structural deep learning techniques to combine LLama2 embeddings of conference call transcripts with traditional numerical features to generate operating profitability as a novel stock price predictor. We hypothesize that the new variable, predicted operating profitability, informed by rich textual data, can capture additional insights into firms’ true potential (Feldman et al., 2020; Kim & Nikolaev, 2023). By delving into the neural network’s predictive capabilities from tex- tual data, this study contributes to empirical asset pricing literature as well as the ongoing exploration of applying neural networks to discover drivers behind stock price fluctuations.