Given a dataset of historical financial statements (income statement, balance sheet, cash flow statement) for a company, write Python code to calculate key financial ratios (e.g., current ratio, debt-to-equity ratio, gross profit margin) and identify trends over the past five years. Your solution should handle missing data points gracefully and output the results in a structured format (e.g., pandas DataFrame).

The ideal answer should follow a MECE (Mutually Exclusive, Collectively Exhaustive) framework to ensure all aspects of the request are covered systematically. First, define a Python function to parse and load financial data from various statements into a unified structure, handling missing values using interpolation or forward-fill. Second, implement functions for each key financial ratio (current ratio, debt-to-equity, gross profit margin), ensuring robust error handling for division by zero. Third, develop a trend analysis function that iterates through the five-year period, applies the ratio calculations, and identifies year-over-year changes or compound annual growth rates. Finally, structure the output into a pandas DataFrame with clear column headers for ratios and years, including a summary of identified trends. This approach ensures comprehensive data handling, accurate calculation, and clear presentation.

import pandas as pd
import numpy as np

def analyze_financial_statements(income_df, balance_df, cashflow_df):
    # Merge dataframes on 'Year' or 'Date' for unified access
    # Assuming 'Year' column exists in all DFs
    df = pd.merge(income_df, balance_df, on='Year', how='outer', suffixes=('_inc', '_bal'))
    df = pd.merge(df, cashflow_df, on='Year', how='outer', suffixes=('_merged', '_cf'))
    df = df.sort_values(by='Year').set_index('Year')

    # Handle missing data: forward-fill for simplicity, more complex methods possible
    df = df.fillna(method='ffill').fillna(method='bfill')

    ratios = pd.DataFrame(index=df.index)

    # Current Ratio: Current Assets / Current Liabilities
    ratios['Current Ratio'] = df['Current Assets'] / df['Current Liabilities']

    # Debt-to-Equity Ratio: Total Debt / Shareholder Equity
    ratios['Debt-to-Equity Ratio'] = df['Total Debt'] / df['Shareholder Equity']

    # Gross Profit Margin: (Revenue - Cost of Goods Sold) / Revenue
    ratios['Gross Profit Margin'] = (df['Revenue'] - df['Cost of Goods Sold']) / df['Revenue']

    # Identify trends over the past five years
    # Assuming the last 5 years are relevant based on sorted index
    trends = ratios.tail(5).pct_change().dropna()
    trends.columns = [col + ' Trend' for col in trends.columns]

    return pd.concat([ratios.tail(5), trends], axis=1)

# Example Usage (dummy data)
income_data = {'Year': [2018, 2019, 2020, 2021, 2022], 'Revenue': [100, 110, 105, 120, 130], 'Cost of Goods Sold': [60, 65, 63, 70, 75]}
balance_data = {'Year': [2018, 2019, 2020, 2021, 2022], 'Current Assets': [40, 45, 42, 50, 55], 'Current Liabilities': [20, 22, 21, 25, 27], 'Total Debt': [30, 32, 31, 35, 38], 'Shareholder Equity': [50, 55, 53, 60, 65]}
cashflow_data = {'Year': [2018, 2019, 2020, 2021, 2022], 'Operating Cash Flow': [15, 18, 16, 20, 22]}

income_df = pd.DataFrame(income_data)
balance_df = pd.DataFrame(balance_data)
cashflow_df = pd.DataFrame(cashflow_data)

results_df = analyze_financial_statements(income_df, balance_df, cashflow_df)
print(results_df)