EarningsEdge

Automatically extracts structured data from unstructured earnings call transcripts and SEC filings (10-K, 10-Q, 8-K) using NLP-based document parsing and entity recognition. The system identifies key sections (management discussion, guidance, risk factors) and normalizes formatting across different filing formats and company styles, enabling downstream analysis on standardized data structures rather than raw text.

Applies fine-tuned sentiment classification models to earnings transcripts, management commentary, and analyst Q&A sections to quantify management tone, confidence levels, and risk perception. The system uses transformer-based models (likely BERT or similar) trained on financial language corpora to detect nuanced sentiment beyond simple positive/negative polarity, including hedging language, uncertainty markers, and shifts in tone across different speakers (CEO vs. CFO).

Analyzes the potential impact of earnings announcements on a user's portfolio, aggregating earnings data, sentiment, and price predictions across all holdings. The system calculates portfolio-level exposure to earnings events (e.g., 'your portfolio has 5 earnings announcements in the next week') and estimates potential portfolio volatility or returns based on individual stock predictions. May include scenario analysis (e.g., 'if all earnings beat, portfolio return is +2%') and correlation analysis between holdings.

Enables export of earnings data, sentiment scores, and predictions in standard formats (CSV, JSON, Excel) for integration with external tools (spreadsheets, trading platforms, custom analysis tools). May include API endpoints for programmatic access to earnings data and real-time data feeds. Supports integration with popular platforms (TradingView, Interactive Brokers, etc.) via webhooks or native integrations.

Aggregates sentiment signals from multiple sources (earnings transcripts, analyst reports, social media, news articles, options market data) into a unified sentiment score or signal. The system likely uses weighted averaging or ensemble methods to combine heterogeneous data sources, with configurable weights reflecting data quality, timeliness, and predictive power. Integration points may include APIs for news aggregation (Bloomberg, Reuters), social media sentiment (Twitter/X, StockTwits), and options market data (implied volatility, put/call ratios).

Compares actual reported earnings metrics (EPS, revenue, guidance) against consensus estimates and historical trends to quantify the magnitude and direction of surprises. The system retrieves consensus estimates from data providers (FactSet, Bloomberg, Yahoo Finance API), calculates surprise ratios (actual vs. estimate), and flags statistically significant deviations. May include anomaly detection to identify unusual patterns (e.g., massive beats on revenue but misses on guidance) that warrant deeper investigation.

Generates forward-looking probability scores or confidence levels for stock price movements following earnings announcements, based on machine learning models trained on historical earnings data, sentiment signals, surprise metrics, and price action. The model likely uses gradient boosting (XGBoost, LightGBM) or neural networks to combine multiple features (earnings surprise, sentiment, volatility, sector trends) into a single prediction score. Outputs may include directional probability (likelihood of up/down move), magnitude estimates (expected % move), and confidence intervals.

Enables users to create custom watchlists of companies and set rule-based alerts for earnings events, sentiment thresholds, or price movements. The system likely uses a rules engine to evaluate conditions (e.g., 'alert me if earnings surprise > 10% AND sentiment score > 0.7') and triggers notifications via email, SMS, or in-app push. Watchlist data is persisted in a user database, and alerts are evaluated in real-time or on a scheduled basis as new earnings data arrives.

Enables side-by-side comparison of earnings metrics, guidance, and sentiment across peer companies or industry groups. The system normalizes metrics across companies (e.g., revenue growth %, margin trends, guidance changes) and highlights outliers or divergences. May include peer grouping logic (automatic or manual) and visualization tools to show relative performance. Useful for identifying which companies in a sector are outperforming or underperforming relative to peers.

Analyzes historical earnings patterns for individual companies to identify recurring trends, seasonality, or management behavior patterns. The system compares current earnings against historical baselines (same quarter last year, 5-year average, trend line) and flags deviations. May include analysis of management guidance accuracy over time, consistency of beat/miss patterns, and changes in key metrics (margins, growth rates). Useful for assessing management credibility and predicting future earnings quality.

Maintains a real-time earnings calendar showing upcoming earnings announcement dates, times, and expected metrics. The system integrates with company investor relations data, SEC filing schedules, and market data providers to populate and update the calendar. Users can filter by date range, sector, market cap, or custom criteria. May include integration with trading platforms to enable one-click trading on earnings events.

Provides a backtesting engine for testing earnings-based trading strategies against historical data. Users define strategy rules (e.g., 'buy if earnings surprise > 10% AND sentiment > 0.7, hold for 5 days') and the system simulates trades on historical earnings data, calculating returns, win rate, Sharpe ratio, and other performance metrics. May include walk-forward analysis to test strategy robustness across different time periods and market regimes.