Learn about how our SEC filing analysis and prediction model works
This app uses AI to analyze SEC filings (10-K, 10-Q, and 8-K) and predict how they will impact stock prices over the next 7 business days. It combines natural language processing, financial data extraction, and machine learning to help investors understand what filings mean for stock performance.
This tool is designed for retail investors, financial analysts, and anyone who wants to understand SEC filings without reading hundreds of pages of dense legal and financial text. It's particularly useful for tracking earnings announcements, quarterly reports, and annual filings.
Most tools either show raw filings with no analysis (like SEC EDGAR) or provide generic summaries. Our app goes further by: (1) extracting specific financial metrics and comparing them to analyst expectations, (2) analyzing management sentiment and tone, (3) identifying new risks, and (4) predicting the actual stock price movement over the next week.
Our model combines multiple data sources: (1) Analyst opinion changes in the 30 days before the filing (most important feature - upgrades/downgrades from major firms), (2) AI-extracted sentiment scores from MD&A sections, (3) Risk analysis comparing new vs. prior filings, (4) Financial metrics from XBRL data, (5) Earnings surprises (actual vs. consensus EPS/revenue), (6) Market context (P/E ratio, market cap), and (7) Historical filing patterns. These features are weighted using a RandomForest machine learning model to predict 7-day forward returns.
We use a RandomForest machine learning model trained on 40+ features extracted from filings and market data. The most important feature is analyst opinion changes (upgrades/downgrades) in the 30 days before filing. Rather than a black-box neural network, our model is interpretable - we can explain exactly why each prediction was made (e.g., '+3 net analyst upgrades', 'positive earnings surprise', 'decreased risk score', 'optimistic management tone'). The model achieves 80% directional accuracy and is continuously refined based on actual outcomes.
Research shows that market reactions to SEC filings typically occur within 3-10 trading days as investors digest the information. We chose 7 days as a balance between capturing immediate reactions and allowing time for institutional analysis. This timeframe also reduces noise from unrelated market movements.
Key variables include: Analyst Opinion Changes (upgrades/downgrades in 30 days before filing - most important feature), Sentiment Score (-1 to +1 from management discussion), Risk Score Delta (change vs. prior filing), EPS Surprise (actual vs. consensus earnings), Revenue Surprise, Guidance Changes, Financial Metrics (revenue growth, margin changes), Market Context (P/E ratio, market cap), Filing Type (10-K, 10-Q, 8-K), and Historical Returns (company-specific patterns).
We track all analyst upgrades and downgrades from major firms (Goldman Sachs, Morgan Stanley, JP Morgan, etc.) in the 30 days before each filing. This creates a 'street momentum' signal that captures institutional sentiment leading into the filing. Net upgrades (upgrades minus downgrades) is the single most important feature in our RandomForest ML model. When analysts are upgrading a stock right before earnings, it often predicts positive short-term price movement. This feature achieves 80% directional accuracy and is prominently displayed in each filing analysis.
We use Claude AI (Anthropic's language model) to analyze the Management Discussion & Analysis (MD&A) section of filings. The AI identifies key phrases, tone shifts, and forward-looking statements to generate a sentiment score from -1 (very pessimistic) to +1 (very optimistic). This score is then adjusted based on actual earnings results when available.
An earnings surprise occurs when a company's actual EPS or revenue differs from analyst consensus estimates. Beats (actual > consensus) typically drive stock prices up, while misses drive them down. We fetch consensus estimates from Yahoo Finance and calculate the surprise magnitude, which is one of the strongest predictors in our model.
Our enhanced risk analysis goes beyond traditional 'Risk Factors' sections. We analyze the entire filing to detect material negative events including: data breaches, litigation and legal proceedings, executive departures or deaths, regulatory investigations, restructuring charges, covenant breaches, product recalls, and financial restatements. This is especially important for 8-K filings which don't have Risk Factors sections but often announce material events. We compare current vs. prior filings to identify new risks, removed risks, and severity changes.
Key learnings: (1) Analyst upgrades/downgrades in the 30 days before a filing is the single most predictive feature - net upgrades correlate strongly with positive 7-day returns. (2) Mega-cap companies (>$500B market cap) show more muted price reactions to filings due to institutional ownership and liquidity - their stocks move ~30% less than mid-caps post-filing. (3) Earnings surprises are 3x more predictive than sentiment for 8-K filings. (4) Risk score increases in 10-Ks have delayed impact (peak effect at 10-14 days vs. 3-7 days). (5) Management tone shifts (optimistic → cautious) are more predictive than absolute sentiment levels. (6) Guidance changes in tech companies have 2x the impact compared to industrials. (7) 8-K filings filed after market hours show stronger next-day reactions than those filed during trading hours.
We collected historical filings from 640+ companies (S&P 500 and high-volume stocks) going back 2+ years. For each filing, we: (1) Extracted features as if analyzing in real-time, (2) Made predictions, (3) Waited 7 trading days, (4) Calculated actual returns from Yahoo Finance, and (5) Compared predicted vs. actual. This process was repeated for thousands of filings to measure accuracy.
The model demonstrates strong directional accuracy (correctly predicting whether the stock will go up or down). Specific accuracy metrics vary by filing type, market conditions, and company characteristics. Our research shows particularly strong performance for mid-cap companies ($10B-$100B market cap) and 8-K earnings announcements with clear earnings surprises. The model's predictions improve when multiple signals align (e.g., earnings beat + optimistic sentiment + reduced risk).
The model cannot predict: (1) External shocks (geopolitical events, market crashes), (2) Sector-wide movements unrelated to the specific filing, (3) Manipulation or fraud not disclosed in filings, (4) Intraday volatility (we predict 7-day returns, not short-term trading). Additionally, past performance doesn't guarantee future results.
We use several techniques: (1) Feature selection based on financial theory (not just correlation mining), (2) Out-of-sample testing on recent filings not used for model development, (3) Cross-validation across different market conditions and sectors, (4) Regularization to prevent over-weighting any single feature. The model is designed to be interpretable and generalizable.
We track 640+ companies including all S&P 500 constituents and high-volume stocks across major sectors: Technology (AAPL, MSFT, GOOGL, NVDA, etc.), Finance (JPM, BAC, GS), Healthcare (UNH, JNJ, PFE), Consumer (AMZN, WMT, TSLA), Energy (XOM, CVX), and more. The list is continuously updated to include newly public companies and remove delisted ones.
We analyze three main filing types: (1) 10-K (Annual Reports) - comprehensive yearly financials and risks, (2) 10-Q (Quarterly Reports) - quarterly financials and updates, and (3) 8-K (Current Events) - major announcements like earnings releases, management changes, or material events. Each filing type has different characteristics that the model accounts for.
Filing data comes from the SEC EDGAR database (official government source). Financial metrics are extracted from inline XBRL tags embedded in filings. Analyst upgrades/downgrades and consensus estimates come from Yahoo Finance (aggregated analyst forecasts and recommendation history from major firms). Stock prices for model validation come from Yahoo Finance historical data. All sources are free and publicly available.
The Latest Filings page shows filings from the last 90 days, updated in real-time as companies file with the SEC. When you analyze a filing, the app fetches the latest version directly from EDGAR and generates fresh predictions. The model is continuously refined as we analyze more filings and observe actual outcomes.
IMPORTANT DATA DISCLAIMERS: We rely on external data sources (SEC EDGAR, Yahoo Finance) and cannot guarantee their accuracy, completeness, availability, or validity. Limitations include: (1) SEC EDGAR - Filings may contain errors, restatements, or be amended after initial submission. XBRL data may be tagged incorrectly by companies. (2) Yahoo Finance - Stock prices may be delayed (15-20 minutes), contain gaps, or have inaccuracies. Analyst data aggregation may be incomplete or outdated. Consensus estimates may not reflect all analysts. (3) Data Availability - External APIs may be temporarily unavailable, rate-limited, or discontinued without notice. (4) No Independent Verification - We do not independently verify the accuracy of data from external sources. Users should cross-reference important information with official company filings and licensed financial data providers. (5) Historical Data - Past data may be revised or restated, affecting model accuracy. We are not responsible for losses resulting from inaccurate, incomplete, or unavailable data from third-party sources.
Built with: Next.js 14 (React framework), TypeScript (type-safe code), Prisma ORM (database), PostgreSQL (production database), Anthropic Claude AI (NLP analysis), Python yfinance (stock data), SEC EDGAR & XBRL APIs (filing data), Tailwind CSS & shadcn/ui (design), and Recharts (data visualization).
Analysis typically takes 30-60 seconds per filing, depending on length and complexity. Steps include: (1) Fetching filing HTML from SEC (5-10s), (2) Parsing XBRL financial data (5-10s), (3) Fetching prior filing for comparison (5-10s), (4) AI analysis with Claude (15-30s), (5) Fetching market data from Yahoo Finance (5s), and (6) Generating prediction (instant).
This tool is designed for research and education, not automated trading. While the predictions are data-driven, they should be used as one input among many for investment decisions. We do not provide trading advice or guarantee returns. Always do your own due diligence and consider your risk tolerance.
This platform is provided strictly for educational and research purposes. It is designed to help users learn about SEC filings, financial analysis techniques, and machine learning applications in finance. This tool should NOT be used as the sole basis for investment decisions.
NO. Nothing on this platform constitutes investment advice, financial advice, trading advice, or any other type of professional advice. All content, predictions, analyses, and information are provided for informational and educational purposes only. You should consult with a licensed financial advisor before making any investment decisions.
IMPORTANT: All predictions and analyses are based on historical data and machine learning models, which have inherent limitations. Past performance does NOT guarantee future results. Stock markets are unpredictable and influenced by countless factors beyond what any model can capture. Using this tool's predictions for actual trading carries significant risk of financial loss. You could lose some or all of your invested capital.
DISCLAIMERS: (1) NO WARRANTY - This service is provided 'as is' without warranties of any kind, express or implied. (2) NO GUARANTEE OF ACCURACY - We make no guarantees about the accuracy, completeness, or reliability of any predictions, analyses, or data. (3) DATA DELAYS - Market data may be delayed or contain errors. (4) MODEL LIMITATIONS - Our machine learning models are experimental and may produce incorrect predictions. (5) TECHNICAL ISSUES - The service may be unavailable, slow, or contain bugs at any time.
LIMITATION OF LIABILITY: To the maximum extent permitted by law, we shall NOT be liable for any direct, indirect, incidental, special, consequential, or punitive damages arising from: (1) your use or inability to use this service, (2) any predictions or analyses provided, (3) investment losses based on information from this platform, (4) errors, bugs, or inaccuracies in the service, or (5) unauthorized access to your data. Your use of this service is entirely at your own risk.
NO. We do not have any fiduciary duty to users of this platform. We are not registered as an investment adviser, broker-dealer, or any other type of financial services provider. We do not manage accounts, execute trades, or provide personalized investment recommendations.
REGULATORY DISCLOSURE: This platform is not regulated by the SEC, FINRA, or any other financial regulatory authority. We are not licensed to provide investment advice. All predictions and analyses are automated outputs from machine learning models and should be treated as educational demonstrations of AI/ML techniques, not as professional investment research.
USER RESPONSIBILITIES: By using this service, you acknowledge that: (1) You are solely responsible for your investment decisions, (2) You will conduct your own due diligence before making any trades, (3) You understand the risks of stock market investing, (4) You will comply with all applicable laws and regulations, (5) You will not rely on this tool for investment decisions, and (6) You may lose some or all of your money if you trade based on predictions from this platform.
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FORWARD-LOOKING STATEMENTS: Any predictions, forecasts, or forward-looking statements are inherently uncertain and based on assumptions that may prove incorrect. Actual results may differ materially from predictions. Factors that could cause actual results to differ include: market volatility, economic conditions, company-specific events, regulatory changes, geopolitical events, and limitations in our models.
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