# How News-Pulse Attributes Stock Price Movements to Fundamental, Emotional, and Funding Factors > Discover how News-Pulse attributes stock price movements to fundamental, emotional, and funding factors using four AI agents and Team-Lead synthesis. Understand market drivers. - Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire) - Tags: deep-dive - Published: 2026-07-26 --- **News-pulse uses four parallel AI agents to scan company fundamentals, regulatory changes, industry trends, and market sentiment, then fuses their findings through a Team-Lead synthesis to classify price swings as driven by fundamental value shifts, funding environment changes, or emotional sentiment waves.** The **news-pulse** skill in the `xbtlin/ai-berkshire` repository is a fast-response diagnostic tool designed to explain sudden stock-price movements. Unlike simple news aggregators, this system employs a multi-agent architecture that investigates distinct informational dimensions concurrently, then applies structured confidence scoring to deliver evidence-based attribution. ## The Four-Agent Attribution Framework At the core of news-pulse is a temporary team of four specialized **Agents**, each probing a different causal layer of price movement. This design ensures comprehensive coverage while maintaining execution speeds under approximately ten minutes. ### Company-Event Scout: Detecting Fundamental Drivers The **Company-Event Scout** focuses exclusively on firm-specific events that alter intrinsic value. According to [`skills/news-pulse.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md) (lines 54-99), this agent searches for earnings surprises, management changes, capital-structure events, lawsuits, and M&A activity. These findings map directly to **fundamental** attribution. When the Scout discovers a post-market earnings miss or a sudden CEO departure that aligns temporally with a price drop, the system flags the movement as fundamentally driven. ### Regulatory-Watcher and Industry-Peer Analyst: Identifying Funding Factors Two agents collaborate to detect **funding** factors—defined as external capital-flow and policy conditions that impact cash flow without changing core business operations: - **Regulatory-Watcher** (lines 54-99): Monitors policy shifts, central bank communications, and regulator releases that affect financing costs or sector liquidity. - **Industry-Peer Analyst** (lines 54-99): Tracks sector-wide beta movements, peer disclosure changes, and macro supply-demand shocks. Together, these agents identify whether a price swing stems from refinancing risks, liquidity crunches, or broad sector repricing due to capital availability changes. ### Sentiment-Tracker: Capturing Emotional Market Forces The **Sentiment-Tracker** agent mines non-fundamental market psychology indicators. As implemented in the source code (lines 99-102), this agent utilizes [`tools/xueqiu_scraper.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/xueqiu_scraper.py) to pull commentary from high-visibility investors ("big-V" opinions), alongside analyst upgrades/downgrades, short-interest spikes, and technical breakout signals. This channel generates **emotional** attribution when the evidence shows momentum-driven trading, panic selling, or euphoric buying disconnected from underlying business metrics. ## Step-by-Step Attribution Workflow The news-pulse skill executes a nine-stage pipeline defined in [`skills/news-pulse.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md), ensuring rigorous validation before any classification occurs. ### Input Validation and Information Grading First, the system validates parameters including company name, time window, price-move description, and focus areas (lines 21-28). It then assigns an **information-availability grade** (A/B/C for rich/moderate/scarce) that determines each agent's search aggressiveness and noise-filtering strategy (lines 34-41). ### Parallel Agent Execution A temporary team `-newspulse` is instantiated (lines 44-49) to sandbox the four agents. Each receives a distinct prompt crafted for their investigative domain. All four tasks launch simultaneously with `run_in_background:true` (lines 107-115), guaranteeing the workflow completes within the ~10-minute target window. ### Evidence Structuring and Synthesis Each agent returns structured outputs containing: - A **core-findings list** detailing relevant events - A **timeline table** with relevance scores (high/medium/low) These outputs follow the format specified in lines 135-141, ensuring consistent data for the final fusion stage. ## The Team-Lead Synthesis and Attribution Logic The decisive classification occurs in the **Team-Lead synthesis** stage (lines 155-190). This component aggregates the four parallel reports, merges their timelines, and constructs the final **Attribution Report** containing: - **Primary driver classification**: Explicit designation as fundamental, funding, or emotional - **Confidence scoring**: Visual indicators (`🔴 高` / `🟡 中` / `⚪ 低` or textual high/medium/low) reflecting alignment strength between evidence and price-move magnitude - **Data gap flags**: Transparency markers where information was insufficient - **Actionable next steps**: Recommendations such as "thesis re-review" or "await clarification" The confidence weighting ensures attribution represents reasoned judgment rather than event enumeration. For example, a 12% drop coinciding with both an earnings miss (fundamental) and sector-wide risk-off sentiment (emotional) receives weighted scoring to determine the dominant driver. ### Report Persistence Completed reports are written to `reports//-news-.md` (lines 176-183), enabling audit trails and reuse for subsequent deep-research cycles. The temporary team is then deleted to free resources (lines 219-224). ## Practical Usage Examples ### Command-Line Invocation Activate the skill directly from a chat interface: ```markdown /news-pulse 腾讯 12%跌 3天 ``` The skill prompts for missing parameters, then returns the full attribution analysis. ### Programmatic Integration For automated pipelines, invoke via Python: ```python import json from ai_berkshire import invoke_skill payload = { "company": "Tencent", "price_move": "down 12% in 3 days", "window_days": 14, "focus": ["company", "regulation", "industry", "sentiment"] } result = invoke_skill("news-pulse", payload) print(result["summary"]) # One-sentence attribution verdict print(result["timeline"]) # Merged evidence timeline ``` The `invoke_skill` wrapper loads [`codex-skills/news-pulse/SKILL.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-skills/news-pulse/SKILL.md) and executes the defined workflow, returning a dictionary mirroring the markdown report sections. ### Sentiment Data Collection To manually populate the Sentiment-Tracker's data sources: ```bash python3 tools/xueqiu_scraper.py \ --user-id 1247347556 \ --keywords Tencent \ --output /tmp/dyp-Tencent.md ``` This scraper pulls recent posts from influential investors (e.g., Duan Yongping), feeding first-hand opinion data into the emotional attribution channel. ## Summary - **news-pulse** classifies stock-price movements using four parallel agents: Company-Event Scout (fundamental), Regulatory-Watcher/Industry-Peer Analyst (funding), and Sentiment-Tracker (emotional). - The system executes via [`skills/news-pulse.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md), utilizing parallel background tasks (`run_in_background:true`) for sub-10-minute turnaround. - Attribution requires **Team-Lead synthesis** (lines 155-190) that merges evidence timelines and applies confidence scoring (high/medium/low). - Reports persist to `reports//-news-.md` for auditability, while underlying sentiment data can be gathered via [`tools/xueqiu_scraper.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/xueqiu_scraper.py). ## Frequently Asked Questions ### What distinguishes funding factors from fundamental factors in news-pulse attribution? **Funding factors** involve external capital conditions—policy shifts, refinancing activity, liquidity changes, and sector-wide supply-demand shocks that affect cash flow without altering the core business model. **Fundamental factors** involve intrinsic value changes such as earnings surprises, management actions, or capital-structure events directly tied to the firm's operations (as defined in [`skills/news-pulse.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md) lines 54-99). ### How long does the news-pulse analysis take to complete? The parallel agent architecture ensures completion within approximately **10 minutes**. The system launches all four scouting tasks simultaneously using `run_in_background:true` (lines 107-115), with the Team-Lead synthesis occurring only after all agents return their structured findings. ### Where are the attribution reports stored? Completed reports are written to `reports//-news-.md` (lines 176-183). This path structure enables easy retrieval for audit trails and subsequent deep-research workflows within the `xbtlin/ai-berkshire` repository. ### Can I customize which attribution factors to analyze? Yes. The `focus` parameter accepts an array selecting specific investigation channels: `["company", "regulation", "industry", "sentiment"]`. You may specify a subset to narrow the scope, though the full attribution power requires all four dimensions for accurate confidence weighting (lines 21-28).