We investigate the capabilities of Large Language Models (LLMs) in generating novel alpha factors for option returns. Utilizing a structured prompt-engineering approach, LLMs like GPT-5 can directly create factors for two distinct options markets: the mature U.S. market and the emerging Chinese market. Empirical analysis further reveals that the LLM-generated factors exhibit remarkable and robust performance, delivering statistically signifcant returns in both all-sample and extensive out-of-sample tests. Beyond their statistical signifcance, such factors are economically meaningful. They display low self-correlation, indicating genuine innovation, and are grounded in sound economic rationale derived from market microstructure and behavioral fnance principles, showcasing a key advantage over traditional machine learning models.

Technology firms are often financed on narratives about products, contracts, customers, and technological progress well before these developments appear in accounting statements. We ask when such narratives become economically informative. Our central idea is that narratives should matter more once they can be linked to later verifiable outcomes rather than treated as stand-alone text.Using listed Chinese technology firms, we develop a validated corporate narrative framework for bank-affiliated investment, a setting in which investors must screen with soft information ex ante and then monitor hard realization and downside risk ex post. We use GPT-5.1 to extract business claims from management discussion, investor-relations records, exchange Q&A, and earnings-roadshow materials, and to label later claim–evidence pairs as support, partial support, conflict, duplicate, or irrelevant. We then connect these labels to official announcements, procurement awards, permits, project updates, and negative-event disclosures to construct a validated firm-month signal. The broad merged panel contains 592 firms and 30,169 firm-month observations; the main return tests use 576 firms and 18,230 firm-month observations over 2022–2024. A simple production rule that combines a low-narrative-premium component with hard-narrative and hard-event anchors, together with a separate downside-risk gate, delivers an implementable annualized long-short return of 8.93% in bank-invested firms after trading costs. The signal is much weaker in non-bank firms, predicts future gross-margin improvement more strongly than future ROE, and improves downside screening.