""" Financial Statement Long-Term Asset Analysis Module ==================================================== Analysis of Long-Term Assets per CFA Institute Curriculum: - Capitalization vs Expensing decisions - Depreciation methods and useful life estimates - Impairment testing and write-downs - Intangible assets and goodwill - Asset revaluation under IFRS - Investment property analysis ===== DATA SOURCES REQUIRED ===== INPUT: - Company financial statements and SEC filings - Management discussion and analysis sections - Auditor reports and financial statement footnotes - Industry benchmarks and competitor data - Economic indicators affecting asset valuations OUTPUT: - Asset valuation metrics and quality indicators - Depreciation analysis and estimated asset age - Impairment risk assessment - Capitalization policy evaluation - Investment recommendations """ import numpy as np import pandas as pd from typing import Dict, List, Optional, Tuple, Union from dataclasses import dataclass, field from enum import Enum import logging # Import from core modules from ..core.base_analyzer import BaseAnalyzer, AnalysisResult, AnalysisType, RiskLevel, TrendDirection, \ ComparativeAnalysis from ..core.data_processor import FinancialStatements, ReportingStandard class DepreciationMethod(Enum): """Depreciation methods for PPE""" STRAIGHT_LINE = "straight_line" DECLINING_BALANCE = "declining_balance" DOUBLE_DECLINING = "double_declining_balance" SUM_OF_YEARS_DIGITS = "sum_of_years_digits" UNITS_OF_PRODUCTION = "units_of_production" class AmortizationMethod(Enum): """Amortization methods for intangibles""" STRAIGHT_LINE = "straight_line" PATTERN_OF_BENEFITS = "pattern_of_benefits" INDEFINITE_LIFE = "indefinite_life" class ImpairmentModel(Enum): """Impairment testing models""" US_GAAP_TWO_STEP = "us_gaap_two_step" IFRS_ONE_STEP = "ifrs_one_step" GOODWILL_QUALITATIVE = "qualitative_assessment" class AssetCategory(Enum): """Categories of long-term assets""" PROPERTY_PLANT_EQUIPMENT = "ppe" INTANGIBLE_DEFINITE_LIFE = "intangible_definite" INTANGIBLE_INDEFINITE_LIFE = "intangible_indefinite" GOODWILL = "goodwill" INVESTMENT_PROPERTY = "investment_property" RIGHT_OF_USE_ASSETS = "rou_assets" @dataclass class CapitalizationAnalysis: """Analysis of capitalization vs expensing decisions""" total_capitalized: float total_expensed: float capitalization_ratio: float # Interest capitalization interest_capitalized: float interest_expensed: float interest_cap_ratio: float # R&D capitalization (IFRS only) rd_capitalized: float rd_expensed: float # Policy assessment capitalization_aggressiveness: str policy_quality_score: float concerns: List[str] = field(default_factory=list) @dataclass class DepreciationAnalysis: """Comprehensive depreciation analysis""" depreciation_expense: float accumulated_depreciation: float gross_ppe: float net_ppe: float # Derived metrics depreciation_rate: float average_asset_age: float remaining_useful_life: float percent_depreciated: float # Method assessment depreciation_method: DepreciationMethod useful_life_estimate: float salvage_value_estimate: float # Trend indicators depreciation_trend: TrendDirection capex_to_depreciation: float asset_renewal_indicator: str @dataclass class IntangibleAssetAnalysis: """Analysis of intangible assets""" total_intangibles: float identifiable_intangibles: float goodwill: float # Composition software: float patents_trademarks: float customer_relationships: float other_intangibles: float # Quality metrics intangible_intensity: float goodwill_to_equity: float goodwill_to_assets: float # Amortization amortization_expense: float weighted_average_life: float # Impairment history cumulative_impairments: float impairment_risk_score: float @dataclass class ImpairmentAnalysis: """Asset impairment analysis""" impairment_charges: float cumulative_impairments: float # By asset category ppe_impairments: float intangible_impairments: float goodwill_impairments: float # Risk indicators impairment_indicators: List[str] recovery_probability: float # Testing compliance testing_frequency: str last_test_date: str carrying_value_vs_recoverable: float @dataclass class InvestmentPropertyAnalysis: """Investment property analysis (IFRS)""" investment_property_value: float measurement_model: str # cost or fair_value # Fair value metrics fair_value: float unrealized_gains_losses: float rental_income: float # Return metrics yield_on_investment_property: float occupancy_rate: float class LongTermAssetAnalyzer(BaseAnalyzer): """ Comprehensive long-term asset analyzer implementing CFA Institute standards. Covers PPE analysis, intangibles, goodwill, impairment testing, and investment property. """ def __init__(self, enable_logging: bool = True): super().__init__(enable_logging) self._initialize_asset_formulas() self._initialize_asset_benchmarks() def _initialize_asset_formulas(self): """Initialize long-term asset specific formulas""" self.formula_registry.update({ 'depreciation_rate': lambda dep_exp, avg_gross_ppe: self.safe_divide(dep_exp, avg_gross_ppe), 'asset_age': lambda accum_dep, annual_dep: self.safe_divide(accum_dep, annual_dep), 'remaining_life': lambda net_ppe, annual_dep: self.safe_divide(net_ppe, annual_dep), 'percent_depreciated': lambda accum_dep, gross_ppe: self.safe_divide(accum_dep, gross_ppe), 'capex_to_depreciation': lambda capex, dep: self.safe_divide(capex, dep), 'goodwill_to_equity': lambda goodwill, equity: self.safe_divide(goodwill, equity), 'intangible_intensity': lambda intangibles, assets: self.safe_divide(intangibles, assets), 'fixed_asset_turnover': lambda revenue, net_ppe: self.safe_divide(revenue, net_ppe) }) def _initialize_asset_benchmarks(self): """Initialize long-term asset benchmarks""" self.asset_benchmarks = { 'percent_depreciated': { 'new': 0.25, 'moderate': 0.50, 'aged': 0.70, 'fully_depreciated': 0.90 }, 'capex_to_depreciation': { 'heavy_investment': 2.0, 'moderate_investment': 1.5, 'maintenance': 1.0, 'underinvestment': 0.7 }, 'goodwill_to_equity': { 'low': 0.20, 'moderate': 0.40, 'high': 0.60, 'very_high': 0.80 }, 'intangible_intensity': { 'low': 0.10, 'moderate': 0.25, 'high': 0.40, 'very_high': 0.60 }, 'fixed_asset_turnover': { 'manufacturing': {'low': 2.0, 'moderate': 4.0, 'high': 6.0}, 'services': {'low': 5.0, 'moderate': 10.0, 'high': 20.0}, 'general': {'low': 3.0, 'moderate': 5.0, 'high': 8.0} } } def analyze(self, statements: FinancialStatements, comparative_data: Optional[List[FinancialStatements]] = None, industry_data: Optional[Dict] = None) -> List[AnalysisResult]: """ Comprehensive long-term asset analysis Args: statements: Current period financial statements comparative_data: Historical financial statements for trend analysis industry_data: Industry benchmarks and peer data Returns: List of analysis results covering all long-term asset aspects """ results = [] # Property, Plant & Equipment Analysis results.extend(self._analyze_ppe(statements, comparative_data, industry_data)) # Depreciation Analysis results.extend(self._analyze_depreciation(statements, comparative_data)) # Capitalization vs Expensing Analysis results.extend(self._analyze_capitalization_policy(statements, comparative_data)) # Intangible Assets Analysis results.extend(self._analyze_intangible_assets(statements, comparative_data)) # Goodwill Analysis results.extend(self._analyze_goodwill(statements, comparative_data)) # Impairment Analysis results.extend(self._analyze_impairment(statements, comparative_data)) # Investment Property Analysis (if applicable) results.extend(self._analyze_investment_property(statements)) # IFRS Revaluation Analysis (if applicable) if statements.company_info.reporting_standard == ReportingStandard.IFRS: results.extend(self._analyze_revaluation(statements, comparative_data)) return results def _analyze_ppe(self, statements: FinancialStatements, comparative_data: Optional[List[FinancialStatements]] = None, industry_data: Optional[Dict] = None) -> List[AnalysisResult]: """Analyze Property, Plant & Equipment""" results = [] balance_sheet = statements.balance_sheet income_statement = statements.income_statement notes = statements.notes gross_ppe = balance_sheet.get('ppe_gross', 0) accumulated_depreciation = balance_sheet.get('accumulated_depreciation', 0) net_ppe = balance_sheet.get('ppe_net', gross_ppe - accumulated_depreciation) total_assets = balance_sheet.get('total_assets', 0) revenue = income_statement.get('revenue', 0) if net_ppe <= 0: return results # PPE Intensity (Capital Intensity) if total_assets > 0: ppe_intensity = self.safe_divide(net_ppe, total_assets) intensity_interpretation = "Capital-intensive business" if ppe_intensity > 0.4 else \ "Moderate capital intensity" if ppe_intensity > 0.2 else \ "Low capital intensity - asset-light model" results.append(AnalysisResult( analysis_type=AnalysisType.ACTIVITY, metric_name="PPE Intensity", value=ppe_intensity, interpretation=intensity_interpretation, risk_level=RiskLevel.LOW, methodology="Net PPE / Total Assets", limitations=["Industry-dependent metric"] )) # Fixed Asset Turnover if net_ppe > 0 and revenue > 0: # Calculate average net PPE if historical data available avg_net_ppe = net_ppe if comparative_data and len(comparative_data) > 0: prev_net_ppe = comparative_data[-1].balance_sheet.get('ppe_net', 0) if prev_net_ppe > 0: avg_net_ppe = (net_ppe + prev_net_ppe) / 2 fixed_asset_turnover = self.safe_divide(revenue, avg_net_ppe) industry_type = industry_data.get('type', 'general') if industry_data else 'general' benchmark = self.asset_benchmarks['fixed_asset_turnover'].get(industry_type, self.asset_benchmarks['fixed_asset_turnover']['general']) if fixed_asset_turnover >= benchmark['high']: turnover_interpretation = "Excellent fixed asset utilization" turnover_risk = RiskLevel.LOW elif fixed_asset_turnover >= benchmark['moderate']: turnover_interpretation = "Good fixed asset utilization" turnover_risk = RiskLevel.LOW else: turnover_interpretation = "Below-average fixed asset utilization - potential overcapacity" turnover_risk = RiskLevel.MODERATE results.append(AnalysisResult( analysis_type=AnalysisType.ACTIVITY, metric_name="Fixed Asset Turnover", value=fixed_asset_turnover, interpretation=turnover_interpretation, risk_level=turnover_risk, benchmark_comparison=self.compare_to_industry(fixed_asset_turnover, industry_data.get('fixed_asset_turnover') if industry_data else None), methodology="Revenue / Average Net PPE", limitations=["Affected by asset age and accounting policies"] )) # PPE Composition Analysis ppe_composition = { 'land': notes.get('land', 0), 'buildings': notes.get('buildings', 0), 'machinery_equipment': notes.get('machinery_equipment', 0), 'furniture_fixtures': notes.get('furniture_fixtures', 0), 'construction_in_progress': notes.get('construction_in_progress', 0) } total_composition = sum(ppe_composition.values()) if total_composition > 0 and gross_ppe > 0: cip_ratio = self.safe_divide(ppe_composition['construction_in_progress'], gross_ppe) if cip_ratio > 0.15: cip_interpretation = "Significant construction in progress - capacity expansion" cip_risk = RiskLevel.MODERATE elif cip_ratio > 0.05: cip_interpretation = "Moderate construction activity" cip_risk = RiskLevel.LOW else: cip_interpretation = "Limited construction in progress" cip_risk = RiskLevel.LOW results.append(AnalysisResult( analysis_type=AnalysisType.ACTIVITY, metric_name="Construction in Progress Ratio", value=cip_ratio, interpretation=cip_interpretation, risk_level=cip_risk, methodology="Construction in Progress / Gross PPE" )) return results def _analyze_depreciation(self, statements: FinancialStatements, comparative_data: Optional[List[FinancialStatements]] = None) -> List[AnalysisResult]: """Comprehensive depreciation analysis""" results = [] balance_sheet = statements.balance_sheet income_statement = statements.income_statement cash_flow = statements.cash_flow notes = statements.notes gross_ppe = balance_sheet.get('ppe_gross', 0) accumulated_depreciation = balance_sheet.get('accumulated_depreciation', 0) net_ppe = balance_sheet.get('ppe_net', gross_ppe - accumulated_depreciation) depreciation_expense = income_statement.get('depreciation', 0) if depreciation_expense == 0: depreciation_expense = cash_flow.get('depreciation_cf', 0) if gross_ppe <= 0 or depreciation_expense <= 0: return results # Percent Depreciated (Asset Age Indicator) percent_depreciated = self.safe_divide(accumulated_depreciation, gross_ppe) benchmark = self.asset_benchmarks['percent_depreciated'] if percent_depreciated >= benchmark['fully_depreciated']: age_interpretation = "Assets nearly fully depreciated - major replacement cycle likely" age_risk = RiskLevel.HIGH elif percent_depreciated >= benchmark['aged']: age_interpretation = "Aging asset base - increased maintenance and replacement costs expected" age_risk = RiskLevel.MODERATE elif percent_depreciated >= benchmark['moderate']: age_interpretation = "Moderate asset age - normal replacement cycle" age_risk = RiskLevel.LOW else: age_interpretation = "Relatively new asset base" age_risk = RiskLevel.LOW results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Percent of Assets Depreciated", value=percent_depreciated, interpretation=age_interpretation, risk_level=age_risk, methodology="Accumulated Depreciation / Gross PPE", limitations=["Based on historical cost and depreciation policies"] )) # Average Asset Age average_age = self.safe_divide(accumulated_depreciation, depreciation_expense) results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Average Asset Age (Years)", value=average_age, interpretation=f"Average asset age of {average_age:.1f} years based on depreciation patterns", risk_level=RiskLevel.MODERATE if average_age > 10 else RiskLevel.LOW, methodology="Accumulated Depreciation / Annual Depreciation Expense", limitations=["Assumes consistent depreciation method over time"] )) # Remaining Useful Life remaining_life = self.safe_divide(net_ppe, depreciation_expense) results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Estimated Remaining Useful Life (Years)", value=remaining_life, interpretation=f"Approximately {remaining_life:.1f} years of remaining useful life at current depreciation rates", risk_level=RiskLevel.HIGH if remaining_life < 3 else RiskLevel.MODERATE if remaining_life < 5 else RiskLevel.LOW, methodology="Net PPE / Annual Depreciation Expense" )) # Depreciation Rate depreciation_rate = self.safe_divide(depreciation_expense, gross_ppe) results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Annual Depreciation Rate", value=depreciation_rate, interpretation=f"Annual depreciation rate of {self.format_percentage(depreciation_rate)}", risk_level=RiskLevel.LOW, methodology="Depreciation Expense / Gross PPE" )) # CapEx to Depreciation Ratio capex = cash_flow.get('capex', 0) if capex > 0 and depreciation_expense > 0: capex_to_dep = self.safe_divide(capex, depreciation_expense) benchmark = self.asset_benchmarks['capex_to_depreciation'] if capex_to_dep >= benchmark['heavy_investment']: capex_interpretation = "Significant investment in new assets - capacity expansion" capex_risk = RiskLevel.LOW elif capex_to_dep >= benchmark['moderate_investment']: capex_interpretation = "Moderate capital investment - growth and maintenance" capex_risk = RiskLevel.LOW elif capex_to_dep >= benchmark['maintenance']: capex_interpretation = "Capital investment approximately matches depreciation - maintenance level" capex_risk = RiskLevel.LOW else: capex_interpretation = "Capital investment below depreciation - potential underinvestment" capex_risk = RiskLevel.MODERATE results.append(AnalysisResult( analysis_type=AnalysisType.ACTIVITY, metric_name="CapEx to Depreciation Ratio", value=capex_to_dep, interpretation=capex_interpretation, risk_level=capex_risk, methodology="Capital Expenditures / Depreciation Expense", limitations=["Does not distinguish between maintenance and growth capex"] )) # Depreciation Method Assessment dep_method = notes.get('depreciation_method', 'straight_line') useful_life = notes.get('average_useful_life', 0) if useful_life > 0: implied_rate = 1 / useful_life actual_rate = depreciation_rate rate_difference = abs(actual_rate - implied_rate) if rate_difference > 0.02: method_interpretation = f"Depreciation rate differs from implied useful life rate - possible accelerated depreciation or policy changes" else: method_interpretation = f"Depreciation rate consistent with stated {useful_life:.0f}-year useful life" results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Depreciation Policy Consistency", value=rate_difference, interpretation=method_interpretation, risk_level=RiskLevel.MODERATE if rate_difference > 0.03 else RiskLevel.LOW, methodology="Comparison of actual vs implied depreciation rate" )) return results def _analyze_capitalization_policy(self, statements: FinancialStatements, comparative_data: Optional[List[FinancialStatements]] = None) -> List[AnalysisResult]: """Analyze capitalization vs expensing decisions""" results = [] income_statement = statements.income_statement cash_flow = statements.cash_flow notes = statements.notes # Interest Capitalization Analysis interest_capitalized = notes.get('interest_capitalized', 0) interest_expensed = income_statement.get('interest_expense', 0) total_interest = interest_capitalized + interest_expensed if total_interest > 0 and interest_capitalized > 0: interest_cap_ratio = self.safe_divide(interest_capitalized, total_interest) if interest_cap_ratio > 0.3: cap_interpretation = "High interest capitalization - may be aggressive" cap_risk = RiskLevel.MODERATE elif interest_cap_ratio > 0.1: cap_interpretation = "Moderate interest capitalization - consistent with construction activity" cap_risk = RiskLevel.LOW else: cap_interpretation = "Conservative interest capitalization policy" cap_risk = RiskLevel.LOW results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Interest Capitalization Ratio", value=interest_cap_ratio, interpretation=cap_interpretation, risk_level=cap_risk, methodology="Interest Capitalized / Total Interest Cost", limitations=["Should correlate with construction in progress levels"] )) # R&D Capitalization (IFRS permits, US GAAP generally prohibits) rd_expense = income_statement.get('rd_expenses', 0) rd_capitalized = notes.get('rd_capitalized', 0) total_rd = rd_expense + rd_capitalized if total_rd > 0 and rd_capitalized > 0: rd_cap_ratio = self.safe_divide(rd_capitalized, total_rd) reporting_standard = statements.company_info.reporting_standard if reporting_standard == ReportingStandard.IFRS: if rd_cap_ratio > 0.5: rd_interpretation = "Aggressive R&D capitalization - scrutinize development phase criteria" rd_risk = RiskLevel.MODERATE else: rd_interpretation = "R&D capitalization within typical IFRS practice" rd_risk = RiskLevel.LOW else: rd_interpretation = "R&D capitalization under US GAAP is unusual - review specific guidance" rd_risk = RiskLevel.MODERATE results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="R&D Capitalization Ratio", value=rd_cap_ratio, interpretation=rd_interpretation, risk_level=rd_risk, methodology="Capitalized R&D / Total R&D Spending" )) # Software Development Capitalization software_capitalized = notes.get('software_capitalized', 0) software_expensed = notes.get('software_expensed', 0) total_software = software_capitalized + software_expensed if total_software > 0 and software_capitalized > 0: software_cap_ratio = self.safe_divide(software_capitalized, total_software) if software_cap_ratio > 0.6: sw_interpretation = "High software capitalization - review technological feasibility criteria" sw_risk = RiskLevel.MODERATE else: sw_interpretation = "Software capitalization within typical range" sw_risk = RiskLevel.LOW results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Software Capitalization Ratio", value=software_cap_ratio, interpretation=sw_interpretation, risk_level=sw_risk, methodology="Capitalized Software / Total Software Costs" )) # Overall Capitalization Aggressiveness Assessment capex = cash_flow.get('capex', 0) operating_expenses = income_statement.get('operating_expenses', 0) if operating_expenses > 0 and capex > 0: capex_to_opex = self.safe_divide(capex, operating_expenses) # Compare to historical if available if comparative_data and len(comparative_data) >= 2: historical_ratios = [] for past_statements in comparative_data: past_capex = past_statements.cash_flow.get('capex', 0) past_opex = past_statements.income_statement.get('operating_expenses', 0) if past_opex > 0 and past_capex > 0: historical_ratios.append(past_capex / past_opex) if historical_ratios: avg_historical = np.mean(historical_ratios) ratio_change = (capex_to_opex - avg_historical) / avg_historical if avg_historical > 0 else 0 if ratio_change > 0.2: trend_interpretation = "Significant increase in capitalization relative to expenses" trend_risk = RiskLevel.MODERATE elif ratio_change < -0.2: trend_interpretation = "Decrease in capitalization - more conservative approach" trend_risk = RiskLevel.LOW else: trend_interpretation = "Stable capitalization policy" trend_risk = RiskLevel.LOW results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Capitalization Policy Trend", value=ratio_change, interpretation=trend_interpretation, risk_level=trend_risk, methodology="Change in CapEx/OpEx ratio vs historical average" )) return results def _analyze_intangible_assets(self, statements: FinancialStatements, comparative_data: Optional[List[FinancialStatements]] = None) -> List[AnalysisResult]: """Analyze intangible assets excluding goodwill""" results = [] balance_sheet = statements.balance_sheet income_statement = statements.income_statement notes = statements.notes intangible_assets = balance_sheet.get('intangible_assets', 0) goodwill = balance_sheet.get('goodwill', 0) total_assets = balance_sheet.get('total_assets', 0) # Separate identifiable intangibles from goodwill identifiable_intangibles = intangible_assets - goodwill if intangible_assets > goodwill else intangible_assets if identifiable_intangibles <= 0: return results # Intangible Intensity if total_assets > 0: intangible_intensity = self.safe_divide(identifiable_intangibles, total_assets) benchmark = self.asset_benchmarks['intangible_intensity'] if intangible_intensity >= benchmark['very_high']: intensity_interpretation = "Very high intangible asset intensity - knowledge-based business model" intensity_risk = RiskLevel.MODERATE elif intangible_intensity >= benchmark['high']: intensity_interpretation = "High intangible asset intensity" intensity_risk = RiskLevel.LOW elif intangible_intensity >= benchmark['moderate']: intensity_interpretation = "Moderate intangible assets" intensity_risk = RiskLevel.LOW else: intensity_interpretation = "Low intangible asset base" intensity_risk = RiskLevel.LOW results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Identifiable Intangible Intensity", value=intangible_intensity, interpretation=intensity_interpretation, risk_level=intensity_risk, methodology="Identifiable Intangibles / Total Assets" )) # Intangible Composition intangible_composition = { 'software': notes.get('software_intangibles', 0), 'patents_trademarks': notes.get('patents_trademarks', 0), 'customer_relationships': notes.get('customer_relationships', 0), 'licenses': notes.get('licenses_intangibles', 0), 'other': notes.get('other_intangibles', 0) } total_composition = sum(intangible_composition.values()) if total_composition > 0: for category, value in intangible_composition.items(): if value > 0: category_ratio = self.safe_divide(value, identifiable_intangibles) if category_ratio > 0.1: # Only report significant categories category_name = category.replace('_', ' ').title() results.append(AnalysisResult( analysis_type=AnalysisType.ACTIVITY, metric_name=f"Intangible Composition - {category_name}", value=category_ratio, interpretation=f"{category_name} represents {self.format_percentage(category_ratio)} of intangibles", risk_level=RiskLevel.LOW, methodology=f"{category_name} / Total Identifiable Intangibles" )) # Amortization Analysis amortization_expense = income_statement.get('amortization', 0) if amortization_expense > 0 and identifiable_intangibles > 0: amortization_rate = self.safe_divide(amortization_expense, identifiable_intangibles) implied_life = 1 / amortization_rate if amortization_rate > 0 else 0 results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Implied Intangible Useful Life", value=implied_life, interpretation=f"Implied average intangible useful life of {implied_life:.1f} years", risk_level=RiskLevel.MODERATE if implied_life > 15 else RiskLevel.LOW, methodology="Identifiable Intangibles / Amortization Expense", limitations=["May include indefinite-life intangibles not being amortized"] )) # Internally Generated vs Acquired Intangibles acquired_intangibles = notes.get('acquired_intangibles', 0) internal_intangibles = notes.get('internal_intangibles', 0) if acquired_intangibles > 0 or internal_intangibles > 0: total_disclosed = acquired_intangibles + internal_intangibles if total_disclosed > 0: acquired_ratio = self.safe_divide(acquired_intangibles, total_disclosed) if acquired_ratio > 0.8: source_interpretation = "Predominantly acquisition-based intangibles" elif acquired_ratio > 0.5: source_interpretation = "Mix of acquired and internally developed intangibles" else: source_interpretation = "Predominantly internally generated intangibles" results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Acquired Intangible Ratio", value=acquired_ratio, interpretation=source_interpretation, risk_level=RiskLevel.LOW, methodology="Acquired Intangibles / (Acquired + Internal)" )) return results def _analyze_goodwill(self, statements: FinancialStatements, comparative_data: Optional[List[FinancialStatements]] = None) -> List[AnalysisResult]: """Analyze goodwill and acquisition-related intangibles""" results = [] balance_sheet = statements.balance_sheet notes = statements.notes goodwill = balance_sheet.get('goodwill', 0) total_assets = balance_sheet.get('total_assets', 0) total_equity = balance_sheet.get('total_equity', 0) intangible_assets = balance_sheet.get('intangible_assets', 0) if goodwill <= 0: return results # Goodwill to Total Assets if total_assets > 0: goodwill_to_assets = self.safe_divide(goodwill, total_assets) if goodwill_to_assets > 0.3: gw_interpretation = "Very high goodwill relative to assets - significant acquisition history" gw_risk = RiskLevel.HIGH elif goodwill_to_assets > 0.2: gw_interpretation = "High goodwill concentration - monitor for impairment" gw_risk = RiskLevel.MODERATE elif goodwill_to_assets > 0.1: gw_interpretation = "Moderate goodwill level" gw_risk = RiskLevel.LOW else: gw_interpretation = "Limited goodwill exposure" gw_risk = RiskLevel.LOW results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Goodwill to Total Assets", value=goodwill_to_assets, interpretation=gw_interpretation, risk_level=gw_risk, methodology="Goodwill / Total Assets", limitations=["Subject to annual impairment testing"] )) # Goodwill to Equity if total_equity > 0: goodwill_to_equity = self.safe_divide(goodwill, total_equity) benchmark = self.asset_benchmarks['goodwill_to_equity'] if goodwill_to_equity >= benchmark['very_high']: gwe_interpretation = "Goodwill exceeds significant portion of equity - high impairment risk exposure" gwe_risk = RiskLevel.HIGH elif goodwill_to_equity >= benchmark['high']: gwe_interpretation = "High goodwill relative to equity" gwe_risk = RiskLevel.MODERATE elif goodwill_to_equity >= benchmark['moderate']: gwe_interpretation = "Moderate goodwill to equity ratio" gwe_risk = RiskLevel.LOW else: gwe_interpretation = "Low goodwill relative to equity base" gwe_risk = RiskLevel.LOW results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Goodwill to Equity", value=goodwill_to_equity, interpretation=gwe_interpretation, risk_level=gwe_risk, methodology="Goodwill / Total Equity", limitations=["Impairment could significantly impact equity"] )) # Goodwill as Portion of Intangibles if intangible_assets > 0: goodwill_portion = self.safe_divide(goodwill, intangible_assets) if goodwill_portion > 0.7: portion_interpretation = "Goodwill dominates intangible asset base - synergies from acquisitions" elif goodwill_portion > 0.4: portion_interpretation = "Significant goodwill among intangible assets" else: portion_interpretation = "Identifiable intangibles dominate - more transparent value allocation" results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Goodwill Portion of Intangibles", value=goodwill_portion, interpretation=portion_interpretation, risk_level=RiskLevel.MODERATE if goodwill_portion > 0.6 else RiskLevel.LOW, methodology="Goodwill / Total Intangible Assets" )) # Goodwill Trend Analysis if comparative_data and len(comparative_data) > 0: historical_goodwill = [] for past_statements in comparative_data: past_goodwill = past_statements.balance_sheet.get('goodwill', 0) historical_goodwill.append(past_goodwill) historical_goodwill.append(goodwill) if len(historical_goodwill) > 1: goodwill_growth = (goodwill / historical_goodwill[0]) - 1 if historical_goodwill[0] > 0 else 0 if goodwill_growth > 0.2: growth_interpretation = "Significant goodwill growth - active acquisition strategy" growth_risk = RiskLevel.MODERATE elif goodwill_growth < -0.1: growth_interpretation = "Goodwill decrease - likely impairment charges" growth_risk = RiskLevel.HIGH else: growth_interpretation = "Stable goodwill balance" growth_risk = RiskLevel.LOW results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Goodwill Growth Rate", value=goodwill_growth, interpretation=growth_interpretation, risk_level=growth_risk, methodology="(Current Goodwill - Historical) / Historical Goodwill" )) # Goodwill by Reporting Unit (if disclosed) reporting_units = notes.get('goodwill_by_segment', {}) if reporting_units: concentration_values = list(reporting_units.values()) if len(concentration_values) > 1 and sum(concentration_values) > 0: max_concentration = max(concentration_values) / sum(concentration_values) if max_concentration > 0.7: concentration_interpretation = "Goodwill concentrated in single reporting unit - concentrated impairment risk" else: concentration_interpretation = "Goodwill distributed across reporting units" results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Goodwill Concentration", value=max_concentration, interpretation=concentration_interpretation, risk_level=RiskLevel.MODERATE if max_concentration > 0.6 else RiskLevel.LOW, methodology="Largest Reporting Unit Goodwill / Total Goodwill" )) return results def _analyze_impairment(self, statements: FinancialStatements, comparative_data: Optional[List[FinancialStatements]] = None) -> List[AnalysisResult]: """Analyze asset impairment and impairment indicators""" results = [] income_statement = statements.income_statement balance_sheet = statements.balance_sheet notes = statements.notes # Current period impairment charges impairment_charges = income_statement.get('impairment_losses', 0) ppe_impairment = notes.get('ppe_impairment', 0) intangible_impairment = notes.get('intangible_impairment', 0) goodwill_impairment = notes.get('goodwill_impairment', 0) total_impairment = impairment_charges + ppe_impairment + intangible_impairment + goodwill_impairment if total_impairment > 0: net_income = income_statement.get('net_income', 0) if net_income != 0: impairment_impact = self.safe_divide(total_impairment, abs(net_income)) if impairment_impact > 0.5: impact_interpretation = "Major impairment charges significantly impacting earnings" impact_risk = RiskLevel.HIGH elif impairment_impact > 0.2: impact_interpretation = "Material impairment impact on earnings" impact_risk = RiskLevel.MODERATE else: impact_interpretation = "Moderate impairment impact" impact_risk = RiskLevel.LOW results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Impairment Impact on Earnings", value=impairment_impact, interpretation=impact_interpretation, risk_level=impact_risk, methodology="Total Impairment Charges / |Net Income|" )) # Impairment by asset category if goodwill_impairment > 0: goodwill = balance_sheet.get('goodwill', 0) pre_impairment_goodwill = goodwill + goodwill_impairment impairment_rate = self.safe_divide(goodwill_impairment, pre_impairment_goodwill) results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Goodwill Impairment Rate", value=impairment_rate, interpretation=f"Goodwill impairment of {self.format_percentage(impairment_rate)} indicates acquisition value deterioration", risk_level=RiskLevel.HIGH if impairment_rate > 0.3 else RiskLevel.MODERATE, methodology="Goodwill Impairment / Pre-Impairment Goodwill" )) # Impairment Indicators Assessment impairment_indicators = self._identify_impairment_indicators(statements, comparative_data) if impairment_indicators: indicator_count = len(impairment_indicators) if indicator_count >= 3: indicator_interpretation = "Multiple impairment indicators present - detailed testing required" indicator_risk = RiskLevel.HIGH elif indicator_count >= 1: indicator_interpretation = "Some impairment indicators present - monitoring recommended" indicator_risk = RiskLevel.MODERATE else: indicator_interpretation = "No significant impairment indicators identified" indicator_risk = RiskLevel.LOW results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Impairment Indicator Count", value=indicator_count, interpretation=indicator_interpretation, risk_level=indicator_risk, limitations=impairment_indicators, methodology="Assessment of qualitative and quantitative impairment triggers" )) # Historical Impairment Pattern if comparative_data and len(comparative_data) >= 2: impairment_history = [] for past_statements in comparative_data: past_impairment = past_statements.income_statement.get('impairment_losses', 0) impairment_history.append(past_impairment) impairment_history.append(total_impairment) periods_with_impairment = sum(1 for imp in impairment_history if imp > 0) impairment_frequency = periods_with_impairment / len(impairment_history) if impairment_frequency > 0.5: freq_interpretation = "Frequent impairment charges - potential ongoing asset quality issues" freq_risk = RiskLevel.HIGH elif impairment_frequency > 0.2: freq_interpretation = "Occasional impairment charges" freq_risk = RiskLevel.MODERATE else: freq_interpretation = "Rare impairment history" freq_risk = RiskLevel.LOW results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Impairment Frequency", value=impairment_frequency, interpretation=freq_interpretation, risk_level=freq_risk, methodology="Periods with Impairment / Total Periods Analyzed" )) return results def _identify_impairment_indicators(self, statements: FinancialStatements, comparative_data: Optional[List[FinancialStatements]] = None) -> List[str]: """Identify potential impairment indicators per accounting standards""" indicators = [] income_statement = statements.income_statement balance_sheet = statements.balance_sheet notes = statements.notes # External indicators # Significant decline in market value market_cap = notes.get('market_capitalization', 0) book_value = balance_sheet.get('total_equity', 0) if market_cap > 0 and book_value > 0: market_to_book = market_cap / book_value if market_to_book < 1.0: indicators.append(f"Market value below book value (M/B: {market_to_book:.2f})") # Adverse economic conditions (would need external data) # Internal indicators # Operating losses operating_income = income_statement.get('operating_income', 0) if operating_income < 0: indicators.append("Operating losses indicate potential asset impairment") # Cash flow deterioration if comparative_data and len(comparative_data) > 0: current_ocf = statements.cash_flow.get('operating_cash_flow', 0) prev_ocf = comparative_data[-1].cash_flow.get('operating_cash_flow', 0) if current_ocf < prev_ocf * 0.7 and prev_ocf > 0: indicators.append("Significant decline in operating cash flows") # Revenue decline if comparative_data and len(comparative_data) > 0: current_revenue = income_statement.get('revenue', 0) prev_revenue = comparative_data[-1].income_statement.get('revenue', 0) if current_revenue < prev_revenue * 0.85 and prev_revenue > 0: indicators.append("Significant revenue decline") # Goodwill significantly aged without testing last_impairment_test = notes.get('last_impairment_test_date', '') if not last_impairment_test: goodwill = balance_sheet.get('goodwill', 0) if goodwill > 0: indicators.append("Goodwill present but impairment test date not disclosed") # High asset age gross_ppe = balance_sheet.get('ppe_gross', 0) accumulated_dep = balance_sheet.get('accumulated_depreciation', 0) if gross_ppe > 0: percent_dep = accumulated_dep / gross_ppe if percent_dep > 0.80: indicators.append(f"Assets {self.format_percentage(percent_dep)} depreciated - near end of useful life") return indicators def _analyze_investment_property(self, statements: FinancialStatements) -> List[AnalysisResult]: """Analyze investment property (primarily IFRS)""" results = [] balance_sheet = statements.balance_sheet income_statement = statements.income_statement notes = statements.notes investment_property = balance_sheet.get('investment_property', 0) if investment_property <= 0: return results total_assets = balance_sheet.get('total_assets', 0) # Investment Property Ratio if total_assets > 0: ip_ratio = self.safe_divide(investment_property, total_assets) results.append(AnalysisResult( analysis_type=AnalysisType.ACTIVITY, metric_name="Investment Property Ratio", value=ip_ratio, interpretation=f"Investment property represents {self.format_percentage(ip_ratio)} of total assets", risk_level=RiskLevel.LOW, methodology="Investment Property / Total Assets" )) # Measurement Model measurement_model = notes.get('ip_measurement_model', 'cost') if measurement_model.lower() == 'fair_value': fair_value_gains = notes.get('ip_fair_value_gains', 0) fair_value_losses = notes.get('ip_fair_value_losses', 0) net_fv_change = fair_value_gains - fair_value_losses if investment_property > 0: fv_change_ratio = self.safe_divide(net_fv_change, investment_property) if abs(fv_change_ratio) > 0.1: fv_interpretation = "Significant fair value changes impacting earnings" fv_risk = RiskLevel.MODERATE else: fv_interpretation = "Moderate fair value adjustments" fv_risk = RiskLevel.LOW results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Investment Property Fair Value Change", value=fv_change_ratio, interpretation=fv_interpretation, risk_level=fv_risk, methodology="Net Fair Value Change / Investment Property Value", limitations=["Fair value model creates earnings volatility"] )) # Rental Yield rental_income = income_statement.get('rental_income', 0) if rental_income > 0 and investment_property > 0: rental_yield = self.safe_divide(rental_income, investment_property) if rental_yield > 0.08: yield_interpretation = "High rental yield - strong income generation" elif rental_yield > 0.05: yield_interpretation = "Moderate rental yield" else: yield_interpretation = "Low rental yield - value-focused strategy" results.append(AnalysisResult( analysis_type=AnalysisType.PROFITABILITY, metric_name="Investment Property Rental Yield", value=rental_yield, interpretation=yield_interpretation, risk_level=RiskLevel.LOW, methodology="Rental Income / Investment Property Value" )) return results def _analyze_revaluation(self, statements: FinancialStatements, comparative_data: Optional[List[FinancialStatements]] = None) -> List[AnalysisResult]: """Analyze asset revaluation (IFRS revaluation model)""" results = [] balance_sheet = statements.balance_sheet notes = statements.notes revaluation_surplus = balance_sheet.get('revaluation_surplus', 0) total_equity = balance_sheet.get('total_equity', 0) if revaluation_surplus <= 0: return results # Revaluation as portion of equity if total_equity > 0: reval_to_equity = self.safe_divide(revaluation_surplus, total_equity) if reval_to_equity > 0.2: reval_interpretation = "Significant revaluation surplus - substantial unrealized gains in assets" reval_risk = RiskLevel.MODERATE elif reval_to_equity > 0.1: reval_interpretation = "Moderate revaluation surplus" reval_risk = RiskLevel.LOW else: reval_interpretation = "Limited revaluation component in equity" reval_risk = RiskLevel.LOW results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Revaluation Surplus Ratio", value=reval_to_equity, interpretation=reval_interpretation, risk_level=reval_risk, methodology="Revaluation Surplus / Total Equity", limitations=["Under IFRS revaluation model; not available under US GAAP for most assets"] )) # Revaluation changes if comparative_data and len(comparative_data) > 0: prev_reval = comparative_data[-1].balance_sheet.get('revaluation_surplus', 0) reval_change = revaluation_surplus - prev_reval if reval_change != 0: change_interpretation = f"Revaluation surplus {'increased' if reval_change > 0 else 'decreased'} by ${abs(reval_change):,.0f}" results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Revaluation Surplus Change", value=reval_change, interpretation=change_interpretation, risk_level=RiskLevel.MODERATE if abs(reval_change) > revaluation_surplus * 0.2 else RiskLevel.LOW, methodology="Current Period - Prior Period Revaluation Surplus" )) # Assets under revaluation model assets_at_revalued = notes.get('assets_revalued_amount', 0) assets_at_cost = notes.get('assets_cost_model', 0) if assets_at_revalued > 0 and assets_at_cost >= 0: total_ppe = assets_at_revalued + assets_at_cost revalued_ratio = self.safe_divide(assets_at_revalued, total_ppe) results.append(AnalysisResult( analysis_type=AnalysisType.QUALITY, metric_name="Assets Under Revaluation Model", value=revalued_ratio, interpretation=f"{self.format_percentage(revalued_ratio)} of PPE carried at revalued amounts", risk_level=RiskLevel.LOW, methodology="Revalued Assets / Total PPE" )) return results def get_key_metrics(self, statements: FinancialStatements) -> Dict[str, float]: """Return key long-term asset metrics""" balance_sheet = statements.balance_sheet income_statement = statements.income_statement cash_flow = statements.cash_flow metrics = {} gross_ppe = balance_sheet.get('ppe_gross', 0) accumulated_dep = balance_sheet.get('accumulated_depreciation', 0) net_ppe = balance_sheet.get('ppe_net', gross_ppe - accumulated_dep) total_assets = balance_sheet.get('total_assets', 0) goodwill = balance_sheet.get('goodwill', 0) intangibles = balance_sheet.get('intangible_assets', 0) revenue = income_statement.get('revenue', 0) depreciation = income_statement.get('depreciation', cash_flow.get('depreciation_cf', 0)) capex = cash_flow.get('capex', 0) # PPE metrics if gross_ppe > 0: metrics['percent_depreciated'] = self.safe_divide(accumulated_dep, gross_ppe) if depreciation > 0: metrics['average_asset_age'] = self.safe_divide(accumulated_dep, depreciation) metrics['remaining_useful_life'] = self.safe_divide(net_ppe, depreciation) if net_ppe > 0 and revenue > 0: metrics['fixed_asset_turnover'] = self.safe_divide(revenue, net_ppe) if depreciation > 0 and capex > 0: metrics['capex_to_depreciation'] = self.safe_divide(capex, depreciation) # Intangible metrics if total_assets > 0: metrics['intangible_intensity'] = self.safe_divide(intangibles, total_assets) metrics['goodwill_to_assets'] = self.safe_divide(goodwill, total_assets) total_equity = balance_sheet.get('total_equity', 0) if total_equity > 0: metrics['goodwill_to_equity'] = self.safe_divide(goodwill, total_equity) return metrics def create_depreciation_analysis(self, statements: FinancialStatements, comparative_data: Optional[List[FinancialStatements]] = None) -> DepreciationAnalysis: """Create comprehensive depreciation analysis object""" balance_sheet = statements.balance_sheet income_statement = statements.income_statement cash_flow = statements.cash_flow notes = statements.notes gross_ppe = balance_sheet.get('ppe_gross', 0) accumulated_depreciation = balance_sheet.get('accumulated_depreciation', 0) net_ppe = balance_sheet.get('ppe_net', gross_ppe - accumulated_depreciation) depreciation_expense = income_statement.get('depreciation', cash_flow.get('depreciation_cf', 0)) capex = cash_flow.get('capex', 0) # Calculate derived metrics depreciation_rate = self.safe_divide(depreciation_expense, gross_ppe) percent_depreciated = self.safe_divide(accumulated_depreciation, gross_ppe) average_asset_age = self.safe_divide(accumulated_depreciation, depreciation_expense) if depreciation_expense > 0 else 0 remaining_useful_life = self.safe_divide(net_ppe, depreciation_expense) if depreciation_expense > 0 else 0 capex_to_depreciation = self.safe_divide(capex, depreciation_expense) if depreciation_expense > 0 else 0 # Determine depreciation method dep_method_str = notes.get('depreciation_method', 'straight_line') if 'declining' in dep_method_str.lower(): depreciation_method = DepreciationMethod.DECLINING_BALANCE elif 'double' in dep_method_str.lower(): depreciation_method = DepreciationMethod.DOUBLE_DECLINING elif 'sum' in dep_method_str.lower() or 'syd' in dep_method_str.lower(): depreciation_method = DepreciationMethod.SUM_OF_YEARS_DIGITS elif 'units' in dep_method_str.lower() or 'production' in dep_method_str.lower(): depreciation_method = DepreciationMethod.UNITS_OF_PRODUCTION else: depreciation_method = DepreciationMethod.STRAIGHT_LINE useful_life_estimate = notes.get('average_useful_life', 0) salvage_value_estimate = notes.get('salvage_value', 0) # Trend analysis depreciation_trend = TrendDirection.STABLE if comparative_data and len(comparative_data) >= 2: dep_values = [] for past_statements in comparative_data: past_dep = past_statements.income_statement.get('depreciation', past_statements.cash_flow.get('depreciation_cf', 0)) dep_values.append(past_dep) dep_values.append(depreciation_expense) if len(dep_values) >= 3: if dep_values[-1] > dep_values[0] * 1.1: depreciation_trend = TrendDirection.IMPROVING # Increasing depreciation elif dep_values[-1] < dep_values[0] * 0.9: depreciation_trend = TrendDirection.DETERIORATING # Decreasing # Asset renewal indicator if capex_to_depreciation >= 1.5: asset_renewal_indicator = "Heavy investment - asset base expanding" elif capex_to_depreciation >= 1.0: asset_renewal_indicator = "Maintenance level investment" elif capex_to_depreciation >= 0.7: asset_renewal_indicator = "Below replacement level - aging assets" else: asset_renewal_indicator = "Significant underinvestment" return DepreciationAnalysis( depreciation_expense=depreciation_expense, accumulated_depreciation=accumulated_depreciation, gross_ppe=gross_ppe, net_ppe=net_ppe, depreciation_rate=depreciation_rate, average_asset_age=average_asset_age, remaining_useful_life=remaining_useful_life, percent_depreciated=percent_depreciated, depreciation_method=depreciation_method, useful_life_estimate=useful_life_estimate, salvage_value_estimate=salvage_value_estimate, depreciation_trend=depreciation_trend, capex_to_depreciation=capex_to_depreciation, asset_renewal_indicator=asset_renewal_indicator ) def create_intangible_analysis(self, statements: FinancialStatements) -> IntangibleAssetAnalysis: """Create comprehensive intangible asset analysis object""" balance_sheet = statements.balance_sheet income_statement = statements.income_statement notes = statements.notes intangible_assets = balance_sheet.get('intangible_assets', 0) goodwill = balance_sheet.get('goodwill', 0) total_assets = balance_sheet.get('total_assets', 0) total_equity = balance_sheet.get('total_equity', 0) identifiable_intangibles = intangible_assets - goodwill if intangible_assets > goodwill else intangible_assets # Composition software = notes.get('software_intangibles', 0) patents_trademarks = notes.get('patents_trademarks', 0) customer_relationships = notes.get('customer_relationships', 0) other_intangibles = notes.get('other_intangibles', 0) # Metrics intangible_intensity = self.safe_divide(intangible_assets, total_assets) goodwill_to_equity = self.safe_divide(goodwill, total_equity) if total_equity > 0 else 0 goodwill_to_assets = self.safe_divide(goodwill, total_assets) # Amortization amortization_expense = income_statement.get('amortization', 0) weighted_average_life = self.safe_divide(identifiable_intangibles, amortization_expense) if amortization_expense > 0 else 0 # Impairment cumulative_impairments = notes.get('cumulative_intangible_impairments', 0) # Impairment risk score impairment_risk_score = 0 if goodwill_to_equity > 0.5: impairment_risk_score += 30 if goodwill_to_assets > 0.2: impairment_risk_score += 20 if intangible_intensity > 0.4: impairment_risk_score += 15 return IntangibleAssetAnalysis( total_intangibles=intangible_assets, identifiable_intangibles=identifiable_intangibles, goodwill=goodwill, software=software, patents_trademarks=patents_trademarks, customer_relationships=customer_relationships, other_intangibles=other_intangibles, intangible_intensity=intangible_intensity, goodwill_to_equity=goodwill_to_equity, goodwill_to_assets=goodwill_to_assets, amortization_expense=amortization_expense, weighted_average_life=weighted_average_life, cumulative_impairments=cumulative_impairments, impairment_risk_score=impairment_risk_score ) def create_capitalization_analysis(self, statements: FinancialStatements) -> CapitalizationAnalysis: """Create capitalization vs expensing analysis object""" income_statement = statements.income_statement cash_flow = statements.cash_flow notes = statements.notes # Interest capitalization interest_capitalized = notes.get('interest_capitalized', 0) interest_expensed = income_statement.get('interest_expense', 0) total_interest = interest_capitalized + interest_expensed interest_cap_ratio = self.safe_divide(interest_capitalized, total_interest) if total_interest > 0 else 0 # R&D rd_capitalized = notes.get('rd_capitalized', 0) rd_expensed = income_statement.get('rd_expenses', 0) # Overall capitalization capex = cash_flow.get('capex', 0) total_capitalized = capex + interest_capitalized + rd_capitalized operating_expenses = income_statement.get('operating_expenses', 0) total_expensed = operating_expenses + interest_expensed + rd_expensed capitalization_ratio = self.safe_divide(total_capitalized, total_capitalized + total_expensed) if (total_capitalized + total_expensed) > 0 else 0 # Policy assessment concerns = [] if interest_cap_ratio > 0.4: concerns.append("High interest capitalization ratio") capitalization_aggressiveness = "Aggressive" elif rd_capitalized > rd_expensed * 0.5: concerns.append("High R&D capitalization") capitalization_aggressiveness = "Aggressive" elif interest_cap_ratio > 0.2: capitalization_aggressiveness = "Moderate" else: capitalization_aggressiveness = "Conservative" policy_quality_score = 100 - len(concerns) * 20 return CapitalizationAnalysis( total_capitalized=total_capitalized, total_expensed=total_expensed, capitalization_ratio=capitalization_ratio, interest_capitalized=interest_capitalized, interest_expensed=interest_expensed, interest_cap_ratio=interest_cap_ratio, rd_capitalized=rd_capitalized, rd_expensed=rd_expensed, capitalization_aggressiveness=capitalization_aggressiveness, policy_quality_score=policy_quality_score, concerns=concerns )