Financial Calculations & Valuation Advanced

Automated Credit Monitoring Arrangement (CMA) Data 5-Year Projection Builder in Python

Generate institutional bank loan CMA Data operating statements, balance sheets, and fund flow statements in Python Pandas.

#pandas#numpy

Problem Statement

Preparing Credit Monitoring Arrangement (CMA) reports for term loan and working capital sanction requires intricate multi-year balance sheet balancing. This script automates 5-year projections.

Formula / Core Logic

Projects Net Sales, Operating Expenses, Working Capital changes, and balances Assets = Liabilities + Equity annually.

Installation

pip install pandas numpy

cma-data-financial-projection-builder-python.py

import pandas as pd
import numpy as np
\.def generate_cma_projections(base_sales, growth_rate=0.15, years=5):
    yrs = [f'Year_{i}' for i in range(1, years + 1)]
    sales = [base_sales * ((1 + growth_rate) ** i) for i in range(years)]
    ebitda = [s * 0.15 for s in sales]
    pat = [e * 0.65 for e in ebitda]
    
    df = pd.DataFrame({'Sales': sales, 'EBITDA': ebitda, 'PAT': pat}, index=yrs)
    return df

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Frequently Asked Questions

Q: What is the primary purpose of CMA Data in bank credit appraisal?

Banks examine CMA data to verify fund utilization, operating cash generation, liquidity cushions, and debt servicing capacity.