Detecting Financial Signals from Alternative Data: A Hybrid GenAI and Machine Learning Approach

Abstract

Financial markets increasingly rely on signals hidden within unstructured data sources. This article introduces a hybrid system combining Large Language Models with traditional machine learning to extract, interpret, and validate financial indicators from alternative data. The architecture uncovers nascent financial indicators embedded within corporate discourse, digital platforms, and unconventional resources via four interconnected frameworks: comprehensive normalization of heterogeneous data streams; contextual examination of narrative elements; mathematical validation of predictive correlations; and specialized agent-based analytical processing. Implementation domains encompass portfolio optimization, enterprise financial operations, and compliance oversight, facilitating anticipatory strategic positioning through the identification of market movements before conventional visibility. Performance assessments reveal extended predictive horizons, refined signal accuracy metrics, and substantially improved interpretative capabilities relative to traditional analytical approaches currently deployed across financial sectors.