> ## Documentation Index > Fetch the complete documentation index at: https://docs.secapi.ai/llms.txt > Use this file to discover all available pages before exploring further. # Investment manager — Agent Prompt Library > Quant research, factor decomposition, 13F-vs-insider divergence, and earnings-context workflows for portfolio managers and analysts. 12 ready-to-run prompts live on `api.secapi.ai/mcp` today. Quant research, factor decomposition, 13F-vs-insider divergence, and earnings-context workflows for portfolio managers and analysts. Run any prompt by pasting it into Claude Desktop, Claude Code, or Cursor with the SEC API MCP server installed. Or use the CLI: ```bash theme={null} secapi agents prompts read investment-manager-factor-decomposition secapi agents prompts copy investment-manager-factor-decomposition | pbcopy ``` *** ## Headline workflow The headliner for this persona is also rendered in the [vertical solutions page](/for-investment-managers). ### 1. Decompose portfolio factor exposures with intelligence overlay *Pull factor exposures and overlay security intelligence for a portfolio or watchlist.* **Category:** Factor research · **Difficulty:** advanced · **Prompt id:** `investment-manager-factor-decomposition` **Prompt** ``` You are an investment research agent with access to SEC API. For any portfolio or watchlist of US equities, pull a security-intelligence bundle for each name, run factor exposure decomposition (momentum, value, quality, low-volatility, size), compare quarter-over-quarter 13F ownership changes for key institutional holders, and flag material insider transactions from the past 90 days. Output a per-name memo with factor weights, top three institutional rotations, top three insider clusters, and one paragraph of analyst commentary. Always cite requestId and provenance for every figure. ``` **Expected tool chain** 1. `portfolio.analyze` — Compute portfolio-level factor weights and country exposure 2. `factors.decomposition` — Decompose per-security factor loadings 3. `owners.compare_13f` — Quarter-over-quarter institutional rotation per name 4. `insiders.list` — Recent Form 4 insider transactions per name **Expected output:** Per-name research memo (markdown) with factor weights, institutional rotations, insider clusters, and requestId citations. *** ## All workflows ### 2. Compare 13F holdings quarter-over-quarter for smart-money rotation *Identify quarter-over-quarter institutional rotation across an institutional holder's portfolio.* **Category:** Ownership intelligence · **Difficulty:** intermediate · **Prompt id:** `investment-manager-13f-quarterly-rotation` **Prompt** ``` For Berkshire Hathaway (CIK 0001067983), pull the latest 13F holdings, compare against the previous quarter, surface the top ten new positions, top ten exits, and top ten size changes. For each surfaced ticker, fetch the latest insider transactions to triangulate manager conviction. Output a markdown table with ticker, position delta (shares + dollars), insider-buy ratio, and a one-line interpretation per row. ``` **Expected tool chain** 1. `entities.resolve` — Resolve the holder identifier to a best-match entity 2. `owners.get_13f` — Latest holdings snapshot 3. `owners.compare_13f` — Quarter-over-quarter delta 4. `insiders.list` — Cross-validate with insider activity per surfaced ticker **Expected output:** Markdown table of top 30 rotations with insider-buy ratio and one-line interpretation each. ### 3. Build a factor-similar peer set with momentum and quality overlays *Generate a factor-similar peer set and rank by momentum/quality overlay.* **Category:** Screening · **Difficulty:** intermediate · **Prompt id:** `investment-manager-related-stocks-screen` **Prompt** ``` For NVDA, generate the closest 25 factor-similar US tickers, decompose each candidate's factor exposure, and rank by composite momentum + quality score. Pull each candidate's trailing-twelve-month financial ratios for valuation context. Output a ranked table with ticker, momentum score, quality score, P/E, ROIC, and a one-line thesis on why the peer is worth tracking. ``` **Expected tool chain** 1. `entities.resolve` — Resolve the seed symbol 2. `factors.related_stocks` — Factor-similar peer set 3. `factors.decomposition` — Per-candidate factor weights 4. `companies.ratios` — TTM valuation/quality ratios per candidate **Expected output:** Ranked markdown table of 25 candidates with factor scores, valuation ratios, and one-line thesis per row. ### 4. Run a macro-regime-aware factor screen *Pick factors aligned with the current macro regime and screen a watchlist accordingly.* **Category:** Macro overlay · **Difficulty:** advanced · **Prompt id:** `investment-manager-regime-aware-screen` **Prompt** ``` Pull the current US macro regime classification, surface the top three factor returns trailing 90 days, then screen my watchlist of large-cap US tech names against the regime-favored factors. Output a ranked list of names that align with the regime, including factor weights and a one-paragraph regime-context narrative for the brief. ``` **Expected tool chain** 1. `macro.regimes` — Current regime classification 2. `macro.high_signal_pack` — Top macro indicator pack for regime context 3. `factors.dashboard` — Factor returns aligned with regime 4. `portfolio.analyze` — Apply factor weights to candidate portfolio **Expected output:** Ranked screen of regime-aligned names + 1-paragraph regime narrative. ### 5. Build an earnings-preview context pack *Compile estimates, recent fundamentals, and the latest MD\&A excerpt before an earnings print.* **Category:** Earnings prep · **Difficulty:** starter · **Prompt id:** `investment-manager-earnings-context-pack` **Prompt** ``` For AAPL ahead of next earnings, pull the consensus estimates, the trailing four quarters of income statement, and the most recent MD&A section from the last 10-Q. Identify the three biggest estimate-vs-actual gaps from the prior quarter and surface MD&A passages that explain them. Output a one-page brief with bullets for setup, key debate, and watch-points. ``` **Expected tool chain** 1. `entities.resolve` — Resolve to best-match CIK 2. `market.estimates` — Analyst consensus estimates 3. `companies.income_statements` — Trailing four quarters 4. `sections.get` — Latest MD\&A from 10-Q **Expected output:** 1-page earnings brief with setup, debate, watch-points. ### 6. Pull 5-year fundamental trends for thesis validation *Walk five years of revenue, margins, cash conversion, and ratios for a name.* **Category:** Fundamentals · **Difficulty:** starter · **Prompt id:** `investment-manager-multi-period-fundamental-trend` **Prompt** ``` For MSFT, pull five years of annual income statement, balance sheet, cash flow statement, and key ratios. Surface a year-over-year delta table for revenue, gross margin, operating margin, free cash flow, ROIC, and net debt/EBITDA. Highlight any year where two or more metrics inflected by more than 20 percent and ask the agent to hypothesize the driver. ``` **Expected tool chain** 1. `entities.resolve` — Resolve to best-match CIK 2. `companies.income_statements` — 5-year income trend 3. `companies.cash_flow_statements` — 5-year cash flow trend 4. `companies.ratios` — 5-year ratios trend **Expected output:** Year-over-year delta table + hypothesized drivers for inflection years. ### 7. Generate a security intelligence bundle with footnote citations *Produce a citation-grounded company intelligence bundle for an allocator brief.* **Category:** Issuer brief · **Difficulty:** intermediate · **Prompt id:** `investment-manager-intelligence-bundle-grounded` **Prompt** ``` For TSLA, build a company intelligence brief that combines the latest semantic intelligence query (covering valuation, governance, and risk), the most recent debt-covenant footnote, the latest segment-revenue footnote, and the latest balance sheet snapshot. Output a markdown brief with three sections (signal, balance-sheet posture, footnote red-flags) and inline citations to filing URLs. ``` **Expected tool chain** 1. `intelligence.query` — Top-of-funnel semantic signal 2. `intelligence.footnotes` — Debt-covenant + segment footnotes 3. `sections.get` — Item 7 MD\&A passages for context 4. `companies.balance_sheets` — Most recent balance sheet snapshot **Expected output:** Markdown brief with 3 sections + inline filing URL citations. ### 8. Stress-test portfolio against macro regimes *Project portfolio factor returns across alternative macro regimes.* **Category:** Risk · **Difficulty:** advanced · **Prompt id:** `investment-manager-portfolio-stress-test` **Prompt** ``` Take my current portfolio (NVDA 30%, AAPL 25%, MSFT 25%, GOOGL 20%), run portfolio.analyze for current factor exposures, then pull factor returns under the prevailing regime AND the prior 12-month regime. Build a stress table comparing realized portfolio returns under each regime and surface the two factors with the largest swing. Output a one-page risk note for the IC. ``` **Expected tool chain** 1. `portfolio.analyze` — Current portfolio factor exposures 2. `macro.regimes` — Current and prior regimes 3. `factors.returns` — Factor returns under each regime 4. `factors.dashboard` — Cross-regime factor comparison **Expected output:** 1-page IC risk note with stress table + 2-factor swing commentary. ### 9. Spot insider buying that contradicts institutional rotation *Find names where insiders are buying while institutions are selling.* **Category:** Smart-money signals · **Difficulty:** intermediate · **Prompt id:** `investment-manager-insider-13f-divergence` **Prompt** ``` For my watchlist of small-cap US healthcare names, identify any tickers where insiders have been net buyers in the past 90 days while one or more 13F filers have been net sellers in the latest quarter. For each match, surface the top insider buyers (Form 4) and the top institutional sellers, plus the latest 90-day insider net-buy dollar volume and the institutional position delta in shares. Output a table ranked by signal strength. ``` **Expected tool chain** 1. `entities.resolve` — Resolve each ticker 2. `insiders.list` — 90-day insider transaction history 3. `owners.get_13f` — Latest institutional snapshot per ticker 4. `owners.compare_13f` — Quarter-over-quarter institutional delta **Expected output:** Ranked table of divergence candidates with insider \$ + institutional Δshares. ### 10. Audit executive compensation alignment with shareholder returns *Compare named-executive compensation against TSR + market context.* **Category:** Governance · **Difficulty:** intermediate · **Prompt id:** `investment-manager-comp-aligned-incentives` **Prompt** ``` For META, pull the latest named-executive compensation disclosures and the prior year's compensation, compare year-over-year base + equity + total, then overlay trailing 12-month total shareholder return and the latest market financials. Output an alignment scorecard (0-10) with bullets on (a) pay-for-performance correlation, (b) equity grant timing relative to drawdowns, (c) any outlier comp items vs peers. ``` **Expected tool chain** 1. `entities.resolve` — Resolve to best-match CIK 2. `comp.list` — Latest named-executive comp 3. `comp.compare` — Year-over-year comp comparison 4. `market.financials` — TSR + price context **Expected output:** Alignment scorecard 0-10 with 3 bullets and supporting figures. ### 11. Decompose segment revenue trajectories *Walk segment revenue + segment-disclosure footnotes for a diversified issuer.* **Category:** Fundamentals · **Difficulty:** advanced · **Prompt id:** `investment-manager-segment-revenue-forensics` **Prompt** ``` For AMZN, pull the segment-revenue disclosure footnote (intelligence.footnotes topic=segment), align with the latest annual income statement segment columns, and walk five years of segment-mix shift. Surface the segment with the largest absolute growth, the segment with the largest margin compression, and the latest 10-K Item 8 segment table excerpt. Output a markdown narrative + segment-mix waterfall. ``` **Expected tool chain** 1. `entities.resolve` — Resolve to best-match CIK 2. `intelligence.footnotes` — Segment-disclosure footnotes 3. `companies.income_statements` — 5-year segmented income 4. `sections.get` — Item 8 financial-statement segment narrative **Expected output:** Markdown narrative + segment-mix waterfall covering 5 years. ### 12. Draft macro tilt scenarios based on cross-country regime divergence *Compare regimes across major markets and draft country/factor tilt research scenarios.* **Category:** Macro overlay · **Difficulty:** advanced · **Prompt id:** `investment-manager-macro-tilt-recommendation` **Prompt** ``` Compare current macro regime classifications across US, Eurozone, Japan, and emerging markets. For each market, pull the latest high-signal indicator pack and the prevailing-regime factor returns. Draft a country/factor tilt research scenario with one paragraph of evidence per market. Output a tilt table + 4-paragraph rationale. ``` **Expected tool chain** 1. `macro.regimes` — Per-country regime classifications 2. `macro.indicators` — Recent indicator history per country 3. `macro.high_signal_pack` — Top-of-stack indicators per country 4. `portfolio.analyze` — Apply tilts to a baseline portfolio **Expected output:** Country/factor tilt table + 4-paragraph rationale. *** ## See also * [Agent prompt library index](/agents/prompt-library) * [Install MCP for Claude Desktop / Claude Code / Cursor](/mcp-install) * [Agent operating layer](/agent-operating-layer) * [SEC API CLI workflows](/javascript-sdk)