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Evaluate Acquisition Target

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SKILL.md

Skill: Evaluate Acquisition Target

Domain

private_equity

Description

Evaluates potential acquisition targets analyzing financial performance, valuation, strategic fit, and integration complexity for M&A decisions.

Tags

M&A, acquisition, due-diligence, valuation, private-equity, investment

Use Cases

  • •Target screening
  • •Valuation analysis
  • •Strategic fit assessment
  • •Integration planning

Proprietary Business Rules

Rule 1: Financial Health Score

Multi-factor financial performance assessment.

Rule 2: Valuation Benchmarking

Multiple-based valuation against comparables.

Rule 3: Strategic Alignment

Fit assessment against strategic criteria.

Rule 4: Integration Risk Assessment

Complexity and risk evaluation for integration.

Input Parameters

  • •target_id (string): Target company identifier
  • •financials (dict): Target financial statements
  • •market_data (dict): Market and industry data
  • •strategic_criteria (dict): Acquirer strategic priorities
  • •comparable_transactions (list): Recent M&A comparables
  • •qualitative_factors (dict): Non-financial factors

Output

  • •overall_score (float): Target attractiveness score
  • •valuation_range (dict): Estimated valuation
  • •financial_assessment (dict): Financial health analysis
  • •strategic_fit (dict): Strategic alignment score
  • •integration_risk (dict): Integration complexity assessment
  • •recommendation (string): Investment recommendation

Implementation

The evaluation logic is implemented in target_evaluator.py and references data from acquisition_criteria.json.

Usage Example

python
from target_evaluator import evaluate_target

result = evaluate_target(
    target_id="TGT-001",
    financials={"revenue": 50000000, "ebitda": 8000000, "growth_rate": 0.15},
    market_data={"industry": "software", "market_size": 5000000000},
    strategic_criteria={"focus_sectors": ["software"], "min_revenue": 25000000},
    comparable_transactions=[{"ev_ebitda": 12, "ev_revenue": 4}],
    qualitative_factors={"management_quality": "strong", "customer_concentration": 0.20}
)

print(f"Overall Score: {result['overall_score']}")

Test Execution

python
from target_evaluator import evaluate_target

result = evaluate_target(
    target_id=input_data.get('target_id'),
    financials=input_data.get('financials', {}),
    market_data=input_data.get('market_data', {}),
    strategic_criteria=input_data.get('strategic_criteria', {}),
    comparable_transactions=input_data.get('comparable_transactions', []),
    qualitative_factors=input_data.get('qualitative_factors', {})
)