comparable-company-analysis

Comparable Company Analysis Skill (Comparable Company Analysis)

Overview

This skill focuses onpeer comparison and valuation positioning of listed companies. When the user provides a single target company, it automatically screens comparable companies through industry analysis, integrates operating metrics, financial ratios, and valuation multiples, and generates a standardized "Comparable Company Analysis Excel Report," helping quickly determine the target company's relative position and valuation level among peers.

Core Trigger Conditions

• The user asks, "Help me do a comparable company analysis for Company XX," "How does Company XX compare with its peers," or "Is Company XX's valuation expensive?"
• The user needs to quickly compare differences between the target company and peers in operating, financial, and valuation dimensions.
• Involves cross-market (A-share, Hong Kong stock, U.S. stock) horizontal comparison needs for listed companies.

Data Toolset (gildata-aidata)

Tool Name	Core Function	Key Parameters
FinancialRatioComparison	Financial Ratio Comparison	Obtain core ratios such as ROE, gross margin, net margin, and debt-to-asset ratio
ValueAnalysisComparison	Valuation Multiple Comparison	Obtain valuation indicators such as PE, PB, PS, and EV/EBITDA
FinancialDataComparison	Financial Data Comparison	Obtain absolute value data such as revenue, net profit, total assets, and net assets
MarketPerformanceComparison	Market Performance Comparison	Obtain market indicators such as price change, turnover rate, total market capitalization, and circulating market capitalization

Workflow

1. Intent Parsing: Extract the target company name or stock code entered by the user (supports A-shares, Hong Kong stocks, and U.S. stocks).
2. Parallel Queries：
   • Call FinancialRatioComparison to obtain peer financial ratios.
   • Call ValueAnalysisComparison to obtain peer valuation multiples.
   • Call FinancialDataComparison to obtain peer core financial data.
   • Call MarketPerformanceComparison to obtain peer market performance.
3. Data Cleaning and Statistics：
   • Integrate multi-dimensional data into a unified dataset.
   • Calculate statistical benchmarks:Industry Mean (Mean)、Industry Median (Median)、Maximum/Minimum。
   • Calculate the target company'sIndustry Percentile (Percentile), visually displaying its relative position.
4. Excel Report Generation：
   • Use Python (pandas + openpyxl) to generate a structured Excel file.
   • The report contains multiple Sheets:估值倍数对比、财务比率对比、经营数据对比、市场表现对比、同业排名定位。
   • Apply conditional formatting highlights to key indicators (such as when the target company's valuation is significantly higher/lower than the industry mean).
5. Delivery and Interpretation：
   • Send the generated Excel report to the user.
   • Provide a brief textual interpretation pointing out the target company's core strengths and potential risks (e.g., "valuation premium is evident but growth is insufficient").

Report Output Structure (Excel)

Sheet Name	Content Description	Core Metric Examples
01_Valuation Multiples Comparison	PE, PB, PS, EV/EBITDA and statistical benchmarks	Target company PE vs industry median
02_Financial Ratio Comparison	ROE, gross margin, net margin, debt-to-asset ratio	Profitability and leverage level
03_Operating Data Comparison	Operating revenue, net profit, total assets, net assets	Scale and growth
04_Market Performance Comparison	Total market cap, free-float market cap, price change, turnover rate	Capital attention and liquidity
05_Peer Ranking Positioning	Percentile ranking of the target company among comparable companies	Comprehensive competitiveness radar chart data

Usage Examples

• "Help me analyze comparable companies for Kweichow Moutai and see whether its valuation is high."
• "Compare the financial indicators of Tencent Holdings and its Hong Kong internet peers."
• "Make an Excel report analyzing comparable companies for Apple (AAPL)."
• "Compared with peers, what level is Company XX's ROE at?"

Notes

• Ensure the generated Excel file format is standardized and the data is accurate, so users can use it directly for investment research reporting.
• For Hong Kong and U.S. stock companies, if dedicated comparison interfaces are limited, you can query individual stock data separately and then manually aggregate and compare them in Python.
• In the textual interpretation, you must incorporate industry characteristics (e.g., growth for tech stocks, PB for bank stocks) and avoid mechanical comparisons.
• All data queries must be based on the latest available financial period (usually TTM or the most recent annual/quarterly report).