Goal

Quantify how CEO sentiment during earnings calls affects stock price.

Overview

Earnings calls are high-stakes events where corporate leaders discuss their company's performance and future outlook. But it's not just about the numbers; the *tone* and *sentiment* of their words can significantly sway investor perception. This project delves into the power of language. You'd gather **earnings call transcripts**, meticulously pair them with corresponding **stock price movements**, and potentially enrich your dataset with **SEC filings** for contextual financial information.

ML Magic

This is where **Natural Language Processing (NLP)** shines. Leverage pre-trained language models like **BERT** or the finance-specific **FinBERT** to extract nuanced sentiment from the verbose transcripts. Then, feed these sentiment scores into **regression models** to determine their correlation and causal impact on stock price fluctuations. Can a particularly optimistic CEO ignite a rally, or a cautious tone trigger a sell-off? This model will tell you!

Architecture

• Data Sources: Transcript Providers, Stock Price APIs, SEC EDGAR Database
• Data Ingestion: Automated Scripts
• Data Storage: MongoDB (Transcripts), PostgreSQL (Stock Data)
• NLP Pipeline: Hugging Face Transformers, FinBERT Sentiment, Feature Extraction
• ML Training: scikit-learn (Regression), XGBoost, Time-series Split
• Deployment: Batch Analysis Job, Results Dashboard (Streamlit/Dash)
• Monitoring: Model Performance, Data Freshness

Impact

This project brings cutting-edge NLP into finance in a meaningful, measurable way. It moves beyond raw data to understand the psychological underpinnings of market reactions, allowing analysts to quantify the 'CEO effect' on a company's valuation.