EarningsEdge vs Power Query
Side-by-side comparison to help you choose.
| Feature | EarningsEdge | Power Query |
|---|---|---|
| Type | Product | Product |
| UnfragileRank | 27/100 | 32/100 |
| Adoption | 0 | 0 |
| Quality | 1 | 1 |
| Ecosystem | 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 12 decomposed | 18 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Combines domain-specific NLP (trained on financial language patterns) with SEC filing schema knowledge to extract not just raw text but semantically meaningful sections (guidance vs. risk vs. historical performance), rather than generic document parsing that treats all text equally
vs alternatives: Faster than manual transcript review and more accurate than regex-based keyword extraction because it understands financial document structure and disambiguates forward-looking statements from historical data
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).
Unique: Uses financial-domain fine-tuned models rather than general-purpose sentiment classifiers, enabling detection of hedging language, uncertainty markers, and management confidence shifts that generic models would miss. Likely includes speaker attribution (CEO vs. CFO tone differences) and section-level analysis rather than document-level aggregation.
vs alternatives: More accurate than simple keyword-based sentiment (which conflates 'risk' mentions with negative sentiment) because it understands financial context and can distinguish between neutral risk disclosure and actual management concern
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.
Unique: Aggregates earnings data and predictions across a user's entire portfolio to provide portfolio-level risk assessment, rather than analyzing individual stocks in isolation. Includes scenario analysis and correlation analysis to estimate portfolio-level impact.
vs alternatives: More comprehensive than individual stock analysis because it shows how earnings events across multiple holdings interact and impact overall portfolio risk, enabling better risk management decisions
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.
Unique: Provides multiple export formats and integration points (API, webhooks, native integrations) to enable flexible data access and workflow integration, rather than forcing users to work within the platform's UI. Likely includes rate limiting and authentication for secure API access.
vs alternatives: More flexible than platform-only analysis because it enables integration with external tools and custom workflows, but requires more technical setup than using the platform's built-in features
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).
Unique: Combines earnings-specific sentiment (domain-trained models) with broader market sentiment (news, social, options) using weighted ensemble methods, rather than treating all sentiment sources equally. Likely includes source quality weighting and temporal decay to prioritize recent, high-quality signals.
vs alternatives: More comprehensive than earnings-only analysis because it captures institutional positioning (options) and retail sentiment (social media) alongside management commentary, providing a fuller picture of market perception
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.
Unique: Combines consensus estimate comparison with anomaly detection to flag not just magnitude of surprises but also unusual patterns (e.g., beat on revenue but miss on guidance, or guidance cut despite earnings beat), which are more predictive of price movement than simple surprise magnitude
vs alternatives: More actionable than raw earnings data because it contextualizes results against expectations and flags anomalies that might signal hidden issues or opportunities, rather than requiring manual comparison of reported vs. consensus numbers
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.
Unique: Combines earnings-specific features (surprise, guidance, sentiment) with market microstructure data (volatility, options pricing) in an ensemble ML model, rather than using simple heuristics or single-factor models. Likely includes confidence intervals and feature importance to help traders understand model uncertainty and drivers.
vs alternatives: More sophisticated than simple earnings surprise heuristics because it accounts for market context (volatility, sector trends) and historical patterns, but less transparent than rule-based systems, making it harder to validate or adjust for regime changes
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.
Unique: Combines earnings-specific data (surprise, sentiment, guidance) with user-defined rules and real-time evaluation, enabling traders to automate their monitoring workflow without manual checking. Likely includes alert history and performance tracking to help users refine their rules.
vs alternatives: More flexible than simple earnings announcement alerts because it allows rule-based combinations of multiple signals (surprise + sentiment + price action), reducing false positives and enabling more sophisticated trading strategies
+4 more capabilities
Construct data transformations through a visual, step-by-step interface without writing code. Users click through operations like filtering, sorting, and reshaping data, with each step automatically generating M language code in the background.
Automatically detect and assign appropriate data types (text, number, date, boolean) to columns based on content analysis. Reduces manual type-setting and catches data quality issues early.
Stack multiple datasets vertically to combine rows from different sources. Automatically aligns columns by name and handles mismatched schemas.
Split a single column into multiple columns based on delimiters, fixed widths, or patterns. Extracts structured data from unstructured text fields.
Convert data between wide and long formats. Pivot transforms rows into columns (aggregating values), while unpivot transforms columns into rows.
Identify and remove duplicate rows based on all columns or specific key columns. Keeps first or last occurrence based on user preference.
Detect, replace, and manage null or missing values in datasets. Options include removing rows, filling with defaults, or using formulas to impute values.
Power Query scores higher at 32/100 vs EarningsEdge at 27/100. However, EarningsEdge offers a free tier which may be better for getting started.
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Apply text operations like case conversion (upper, lower, proper), trimming whitespace, and text replacement. Standardizes text data for consistent analysis.
+10 more capabilities