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| Pricing | Free | Free |
| Capabilities | 12 decomposed | 7 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
Enables evaluation of AI systems' ability to perform chained mathematical operations (addition, subtraction, multiplication, division, comparisons) across both structured tables and unstructured text extracted from SEC filings. The dataset provides ground-truth question-answer pairs where answers require synthesizing data from multiple locations within earnings reports and applying sequential arithmetic operations, testing whether models can decompose complex financial queries into discrete computational steps.
Unique: Combines real SEC filing documents (not synthetic) with crowdsourced questions requiring multi-step arithmetic, creating a hybrid dataset that tests both domain knowledge extraction and quantitative reasoning in a single evaluation task. Unlike generic math word problems, answers require locating figures within 10+ page documents first.
vs alternatives: More challenging than DROP or SVAMP because it requires financial domain knowledge AND document retrieval before arithmetic, whereas generic math benchmarks assume figures are already extracted
Assesses whether AI systems understand financial terminology, accounting concepts, and domain-specific metrics by requiring them to answer questions about real earnings reports from S&P 500 companies. The dataset tests recognition of financial line items (revenue, COGS, operating expenses, net income), ability to distinguish between different financial statements (income statement vs balance sheet), and understanding of financial ratios and metrics without explicit instruction on their definitions.
Unique: Uses authentic SEC filings rather than synthetic financial data, exposing models to real-world accounting variations, footnote complexity, and the actual structure of professional financial documents. This tests transfer learning from general text to specialized domain without domain-specific pretraining.
vs alternatives: More authentic than synthetic financial QA datasets because it uses real earnings reports with their inherent complexity, but narrower than general financial knowledge benchmarks because it focuses only on historical data interpretation
FinQA scores higher at 60/100 vs EarningsEdge at 41/100.
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Enables evaluation of AI systems' ability to extract numerical data from both structured HTML/text tables and unstructured prose within the same document, then reason over the extracted values. The dataset contains questions where relevant data appears in different formats — some figures are in formatted tables with clear row/column headers, while others are embedded in narrative text or footnotes — requiring robust parsing and entity linking before computation can occur.
Unique: Combines structured table data with unstructured narrative in the same evaluation, forcing systems to handle format heterogeneity and resolve references across different data representations. Most table QA datasets use clean, isolated tables; this tests real-world document complexity.
vs alternatives: More realistic than isolated table QA benchmarks (like SQA or WikiTableQuestions) because it requires handling narrative context and format mixing, but simpler than full document understanding because tables are already in text format (no OCR needed)
Provides a curated, crowdsourced-annotated dataset of 8,281 question-answer pairs with multi-step reasoning requirements, enabling systematic evaluation of AI systems on financial numerical reasoning. The dataset includes quality control mechanisms through crowdworker annotation, answer validation against ground truth, and coverage across diverse financial metrics and company types within the S&P 500, creating a reproducible evaluation standard for the financial AI community.
Unique: Provides a publicly available, reproducible benchmark specifically designed for financial numerical reasoning with real SEC filings, enabling standardized comparison across different financial AI systems. Most financial datasets are proprietary or synthetic; this is open-source and authentic.
vs alternatives: More specialized and challenging than generic QA benchmarks (SQuAD, MRQA) because it requires financial domain knowledge and multi-step arithmetic, but narrower in scope than comprehensive financial understanding benchmarks because it focuses only on numerical reasoning
Assesses AI systems' ability to perform multi-hop reasoning by requiring them to locate and combine information from different sections of earnings reports. Questions may require finding a figure in the income statement, then locating a related metric in the balance sheet, then performing arithmetic across both — testing whether models can maintain context across document boundaries and understand relationships between different financial statement sections.
Unique: Embeds multi-hop reasoning requirements within authentic financial documents where hops correspond to real relationships between financial statement sections, rather than synthetic reasoning chains. This tests whether models understand domain structure, not just generic multi-hop patterns.
vs alternatives: More realistic than synthetic multi-hop datasets (HotpotQA, 2WikiMultiHopQA) because reasoning hops follow actual financial relationships, but less controlled because document structure varies and reasoning paths are implicit rather than explicitly annotated
Enables evaluation of whether AI systems can identify which arithmetic operations (addition, subtraction, multiplication, division, comparison) are required to answer financial questions, then execute them correctly. The dataset implicitly tests operation selection — a question asking 'what is the profit margin' requires division (net income / revenue), while 'what is total assets' requires addition — forcing models to understand financial semantics before applying math.
Unique: Embeds arithmetic operation selection within financial domain context, requiring models to understand that 'margin' semantically maps to division and 'total' maps to addition. This tests semantic grounding of operations, not just arithmetic execution.
vs alternatives: More semantically grounded than generic math word problem datasets because operation selection is implicit in financial terminology, but less explicit than datasets with annotated operation types because operations must be inferred
Provides evaluation capability for AI systems to compare financial metrics across multiple S&P 500 companies or aggregate metrics across different time periods within the same company's earnings reports. While individual questions reference single documents, the dataset structure enables evaluation of systems that can retrieve and compare relevant companies, requiring understanding of which metrics are comparable across entities and how to normalize for company size or accounting differences.
Unique: Provides a foundation for evaluating cross-company financial comparison by including diverse S&P 500 companies with different business models and scales, enabling assessment of whether systems can normalize and compare metrics appropriately. Most financial QA datasets focus on single-document questions.
vs alternatives: Enables cross-company evaluation unlike single-document QA datasets, but requires external retrieval and comparison logic because the dataset itself contains only single-document questions