Invxst

Converts unstructured earnings reports, SEC filings, and financial documents into plain-English investment summaries using LLM-based extraction and abstractive summarization. The system likely employs document chunking with sliding windows to preserve context across multi-page filings, then applies extractive key-point identification followed by abstractive generation to produce investor-focused narratives highlighting revenue trends, margin changes, guidance, and risk factors.

Ingests real-time and historical market data from multiple sources (stock prices, options chains, sector indices, economic indicators) and normalizes them into a unified schema for analysis. The system likely maintains connectors to financial data APIs (Alpha Vantage, IEX Cloud, or proprietary feeds) with caching and deduplication logic to handle duplicate ticks, and applies time-series alignment to ensure cross-asset comparisons are temporally consistent.

Aggregates financial news and social media sentiment for individual stocks and analyzes the correlation between sentiment shifts and price movements. The system likely uses NLP-based sentiment classification (positive/negative/neutral) on news articles and social posts, then correlates sentiment changes with subsequent stock returns to quantify the impact of news events on price.

Enables users to define custom screening criteria (valuation multiples, growth rates, dividend yield, technical indicators) and identify stocks matching those criteria from a universe of thousands. The system likely maintains a pre-computed database of fundamental and technical metrics updated daily, then applies user-defined filters using a rule engine to quickly return matching stocks without requiring real-time calculation.

Combines summarized earnings data, market trends, and analyst sentiment into coherent investment theses that articulate bull and bear cases for individual securities. The system likely uses multi-step reasoning (chain-of-thought style) to weigh quantitative signals (valuation metrics, growth rates) against qualitative factors (competitive positioning, management quality) and generates structured arguments with confidence scores, enabling users to understand the reasoning behind AI-generated recommendations.

Monitors user-defined watchlists and thresholds (price targets, volume spikes, earnings dates, sector rotations) and delivers alerts via email, push notifications, or in-app messages when conditions are met. The system likely uses event-driven architecture with streaming data processors (e.g., Kafka-style pipelines) that evaluate rules against incoming market ticks in near-real-time, with deduplication logic to prevent alert fatigue.

Analyzes user portfolio holdings and decomposes returns into contributions from individual positions, sectors, and macro factors (market beta, interest rate sensitivity, currency exposure). The system likely uses time-weighted return calculations and factor attribution models to isolate the impact of each holding on overall portfolio performance, enabling users to understand whether outperformance came from stock picking skill or market timing.

Identifies emerging trends across sectors and macro factors (interest rates, inflation, GDP growth, currency movements) and correlates them with individual stock performance to highlight which securities are well-positioned for current market conditions. The system likely uses time-series correlation analysis and sentiment extraction from financial news to detect regime shifts and sector rotations, then surfaces relevant holdings or opportunities to users.

Calculates and compares valuation multiples (P/E, P/B, EV/EBITDA, PEG ratio) for a given stock against its sector peers, historical averages, and growth rates to assess whether the stock is cheap or expensive. The system likely uses normalized earnings and forward guidance to compute forward-looking multiples, then applies statistical analysis (percentile rankings, z-scores) to contextualize valuations within peer groups.

Aggregates analyst earnings estimates from multiple sources and tracks consensus expectations for revenue, EPS, and other metrics across quarters and years. The system likely maintains a time-series of estimate revisions to detect momentum (rising vs falling estimates) and identifies earnings surprises by comparing actual results against consensus, enabling users to spot stocks with positive or negative estimate revisions.

Quantifies downside risk for individual stocks and portfolios using metrics like beta, standard deviation, value-at-risk (VaR), and maximum drawdown. The system likely calculates these metrics from historical price data and correlations, then contextualizes them against benchmarks and peer groups to help users understand the risk profile of their holdings.

Tracks dividend history, yield, payout ratios, and sustainability for dividend-paying stocks, and identifies dividend growth trends or cuts. The system likely maintains historical dividend data and compares current yields against historical averages to identify high-yield opportunities, while also analyzing payout ratios and free cash flow to assess dividend sustainability.