Using LLM for Stock Prediction: A Comprehensive Guide

Large language models are increasingly used to synthesize unstructured financial data into actionable trading signals. Unlike traditional quantitative models that rely exclusively on structured price history, LLMs can parse earnings call transcripts, SEC filings, news sentiment, and macro research in a single inference pass. Platforms such as Oxlo.ai make this practical at scale by removing token-based billing penalties, allowing analysts to submit full documents without truncation. This capability shifts the bottleneck from model architecture to context management and inference economics, particularly when processing lengthy documents like 10-K reports or multi-year analyst transcripts.

Why LLMs for Equity Forecasting

Financial markets generate vast amounts of unstructured text. Earnings reports, central bank statements, and social media sentiment contain signals that are difficult to encode in tabular features. LLMs excel at entity extraction, causal reasoning, and sentiment classification across these sources. A model like DeepSeek R1