MENAP: multimodal efficiency-constrained news-augmented asset pricing via cost-aware preference optimization

Abstract

Abstract Multimodal financial signals spanning textual news, market/macro factors, and auxiliary metadata—offer complementary views of investor attention and risk, but exploiting them at scale increasingly relies on large language model (LLM) agents whose multi-step reasoning and verbose generations can be prohibitively costly. We present MENAP ( Multimodal Efficiency-constrained News-Augmented Pricing ), a cost-aware preference-efficient framework that integrates multimodal inputs into the canonical “news-to-state-to-pricing-to-portfolio” pipeline while explicitly optimizing the news interpretation agent for both signal quality and inference efficiency. MENAP treats each daily multimodal interpretation as a trajectory and performs offline preference optimization, where preferred trajectories are further regularized by efficiency rewards that penalize total token usage and the number of refinement steps, encouraging concise yet informative multimodal summaries. Crucially, the downstream mixed pricing network and evaluation protocol remain unchanged, enabling fair comparisons and straightforward deployment. Experiments follow a standard construction: 2 years of Wall Street Journal news (2021-09-29 to 2023-09-29) aligned with daily returns from CRSP, market and risk-free rates from the Ken French library, and macroeconomic factors following Jensen et al., with a fixed split of 9 months for training, 3 months for validation, and 1 year for testing. We evaluate MENAP on both portfolio performance (Sharpe ratio and maximum drawdown for TP/EW/VW portfolios) and pricing accuracy (average absolute alpha, t-statistics, and the GRS test on 78 anomaly portfolios), and additionally report LLM efficiency metrics (tokens and steps). The results show that MENAP delivers a superior effectiveness–efficiency trade-off, reducing inference cost while maintaining or improving economic outcomes.