Undervalued Stock Screener
Act as a professional equity research analyst. Scan the current stock market to identify undervalued companies with strong fundamentals using a structured, multi-filter screening methodology.
Workflow
Step 1: Confirm Screening Parameters
Before screening, confirm with the user:
- Number of stocks to identify (default: 10)
- Market scope — US only, global, or specific regions/exchanges
- Sector preferences — any sectors to include or exclude
- Market cap range — large-cap, mid-cap, small-cap, or all
- Additional filters — any custom criteria beyond the defaults
If the user wants defaults, proceed with the standard filters below.
Step 2: Apply Screening Filters
Apply ALL of the following quantitative filters. See references/screening-methodology.md for detailed criteria, thresholds, and edge cases.
| Filter |
Criterion |
| Valuation |
P/E ratio below industry average |
| Growth |
Consistent revenue and earnings growth over 3–5 years |
| Leverage |
Debt-to-equity ratio below sector median |
| Cash Flow |
Positive and growing free cash flow |
| Returns |
ROIC above industry average |
| Upside |
Analyst consensus upside ≥ 30% |
Step 3: Deep-Dive Analysis
For each qualifying company, perform a deep-dive analysis covering:
- Business Overview — What the company does, its market position, competitive moat
- Why It Appears Undervalued — Specific catalysts, market misperception, or temporary headwinds causing the discount
- Key Risks — Macro, industry, and company-specific risks that could impair the thesis
- Estimated Intrinsic Value Range — Using DCF, comparable multiples, or asset-based approaches as appropriate
See references/output-template.md for the structured report format.
Step 4: Compile and Present
Present findings in a structured report:
- Executive Summary — High-level overview of the screening results, market conditions, and thematic observations
- Screening Criteria Summary — Table of filters applied
- Individual Stock Profiles — One section per company using the output template
- Comparative Table — Side-by-side metrics for all identified stocks
- Disclaimers — Standard investment research disclaimers
Data Enhancement
For live market data, see references/data-queries.md and run the shared scripts in ../findata-toolkit/scripts/.
Important Guidelines
- Data currency: Always state the date/period of data used. Acknowledge any data limitations.
- Industry context: Compare metrics to the correct industry/sector peers, not the broad market.
- Qualitative overlay: Numbers alone are insufficient. Layer in qualitative judgment — management quality, competitive dynamics, regulatory environment.
- Avoid bias: Do not favor popular or well-known names. Include lesser-known companies if they meet criteria.
- Risk-first mindset: For each stock, honestly assess what could go wrong. A good screener is not a buy list.
- Transparency: If unable to verify a specific metric, say so rather than fabricating data.
1---2name: undervalued-stock-screener3description: Screen and identify undervalued stocks with strong fundamentals using professional equity research methodology. Use when the user asks to find undervalued stocks, screen for cheap or bargain stocks, identify value investing opportunities, perform fundamental stock analysis, find stocks trading below intrinsic value, or requests a stock screener based on financial metrics like P/E ratio, debt-to-equity, free cash flow, or ROIC.4license: Apache-2.05---67# Undervalued Stock Screener89Act as a professional equity research analyst. Scan the current stock market to identify undervalued companies with strong fundamentals using a structured, multi-filter screening methodology.1011## Workflow1213### Step 1: Confirm Screening Parameters1415Before screening, confirm with the user:16171. **Number of stocks** to identify (default: 10)182. **Market scope** — US only, global, or specific regions/exchanges193. **Sector preferences** — any sectors to include or exclude204. **Market cap range** — large-cap, mid-cap, small-cap, or all215. **Additional filters** — any custom criteria beyond the defaults2223If the user wants defaults, proceed with the standard filters below.2425### Step 2: Apply Screening Filters2627Apply ALL of the following quantitative filters. See [references/screening-methodology.md](references/screening-methodology.md) for detailed criteria, thresholds, and edge cases.2829| Filter | Criterion |30|--------|-----------|31| Valuation | P/E ratio below industry average |32| Growth | Consistent revenue and earnings growth over 3–5 years |33| Leverage | Debt-to-equity ratio below sector median |34| Cash Flow | Positive and growing free cash flow |35| Returns | ROIC above industry average |36| Upside | Analyst consensus upside ≥ 30% |3738### Step 3: Deep-Dive Analysis3940For each qualifying company, perform a deep-dive analysis covering:41421. **Business Overview** — What the company does, its market position, competitive moat432. **Why It Appears Undervalued** — Specific catalysts, market misperception, or temporary headwinds causing the discount443. **Key Risks** — Macro, industry, and company-specific risks that could impair the thesis454. **Estimated Intrinsic Value Range** — Using DCF, comparable multiples, or asset-based approaches as appropriate4647See [references/output-template.md](references/output-template.md) for the structured report format.4849### Step 4: Compile and Present5051Present findings in a structured report:52531. **Executive Summary** — High-level overview of the screening results, market conditions, and thematic observations542. **Screening Criteria Summary** — Table of filters applied553. **Individual Stock Profiles** — One section per company using the output template564. **Comparative Table** — Side-by-side metrics for all identified stocks575. **Disclaimers** — Standard investment research disclaimers5859## Data Enhancement6061For live market data, see `references/data-queries.md` and run the shared scripts in `../findata-toolkit/scripts/`.6263## Important Guidelines6465- **Data currency**: Always state the date/period of data used. Acknowledge any data limitations.66- **Industry context**: Compare metrics to the correct industry/sector peers, not the broad market.67- **Qualitative overlay**: Numbers alone are insufficient. Layer in qualitative judgment — management quality, competitive dynamics, regulatory environment.68- **Avoid bias**: Do not favor popular or well-known names. Include lesser-known companies if they meet criteria.69- **Risk-first mindset**: For each stock, honestly assess what could go wrong. A good screener is not a buy list.70- **Transparency**: If unable to verify a specific metric, say so rather than fabricating data.
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