Financial Analyst Skill
Overview
Production-ready financial analysis toolkit providing ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. Designed for financial modeling, forecasting & budgeting, management reporting, business performance analysis, and investment analysis.
5-Phase Workflow
Phase 1: Scoping
- Define analysis objectives and stakeholder requirements
- Identify data sources and time periods
- Establish materiality thresholds and accuracy targets
- Select appropriate analytical frameworks
Phase 2: Data Analysis & Modeling
- Collect and validate financial data (income statement, balance sheet, cash flow)
- Validate input data completeness before running ratio calculations (check for missing fields, nulls, or implausible values)
- Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)
- Build DCF models with WACC and terminal value calculations; cross-check DCF outputs against sanity bounds (e.g., implied multiples vs. comparables)
- Construct budget variance analyses with favorable/unfavorable classification
- Develop driver-based forecasts with scenario modeling
Phase 3: Insight Generation
- Interpret ratio trends and benchmark against industry standards
- Identify material variances and root causes
- Assess valuation ranges through sensitivity analysis
- Evaluate forecast scenarios (base/bull/bear) for decision support
Phase 4: Reporting
- Generate executive summaries with key findings
- Produce detailed variance reports by department and category
- Deliver DCF valuation reports with sensitivity tables
- Present rolling forecasts with trend analysis
Phase 5: Follow-up
- Track forecast accuracy (target: +/-5% revenue, +/-3% expenses)
- Monitor report delivery timeliness (target: 100% on time)
- Update models with actuals as they become available
- Refine assumptions based on variance analysis
Tools
1. Ratio Calculator (scripts/ratio_calculator.py)
Calculate and interpret financial ratios from financial statement data.
Ratio Categories:
- Profitability: ROE, ROA, Gross Margin, Operating Margin, Net Margin
- Liquidity: Current Ratio, Quick Ratio, Cash Ratio
- Leverage: Debt-to-Equity, Interest Coverage, DSCR
- Efficiency: Asset Turnover, Inventory Turnover, Receivables Turnover, DSO
- Valuation: P/E, P/B, P/S, EV/EBITDA, PEG Ratio
python scripts/ratio_calculator.py sample_financial_data.json
python scripts/ratio_calculator.py sample_financial_data.json --format json
python scripts/ratio_calculator.py sample_financial_data.json --category profitability
2. DCF Valuation (scripts/dcf_valuation.py)
Discounted Cash Flow enterprise and equity valuation with sensitivity analysis.
Features:
- WACC calculation via CAPM
- Revenue and free cash flow projections (5-year default)
- Terminal value via perpetuity growth and exit multiple methods
- Enterprise value and equity value derivation
- Two-way sensitivity analysis (discount rate vs growth rate)
python scripts/dcf_valuation.py valuation_data.json
python scripts/dcf_valuation.py valuation_data.json --format json
python scripts/dcf_valuation.py valuation_data.json --projection-years 7
3. Budget Variance Analyzer (scripts/budget_variance_analyzer.py)
Analyze actual vs budget vs prior year performance with materiality filtering.
Features:
- Dollar and percentage variance calculation
- Materiality threshold filtering (default: 10% or $50K)
- Favorable/unfavorable classification with revenue/expense logic
- Department and category breakdown
- Executive summary generation
python scripts/budget_variance_analyzer.py budget_data.json
python scripts/budget_variance_analyzer.py budget_data.json --format json
python scripts/budget_variance_analyzer.py budget_data.json --threshold-pct 5 --threshold-amt 25000
4. Forecast Builder (scripts/forecast_builder.py)
Driver-based revenue forecasting with rolling cash flow projection and scenario modeling.
Features:
- Driver-based revenue forecast model
- 13-week rolling cash flow projection
- Scenario modeling (base/bull/bear cases)
- Trend analysis using simple linear regression (standard library)
python scripts/forecast_builder.py forecast_data.json
python scripts/forecast_builder.py forecast_data.json --format json
python scripts/forecast_builder.py forecast_data.json --scenarios base,bull,bear
Knowledge Bases
| Reference |
Purpose |
references/financial-ratios-guide.md |
Ratio formulas, interpretation, industry benchmarks |
references/valuation-methodology.md |
DCF methodology, WACC, terminal value, comps |
references/forecasting-best-practices.md |
Driver-based forecasting, rolling forecasts, accuracy |
references/industry-adaptations.md |
Sector-specific metrics and considerations (SaaS, Retail, Manufacturing, Financial Services, Healthcare) |
Templates
| Template |
Purpose |
assets/variance_report_template.md |
Budget variance report template |
assets/dcf_analysis_template.md |
DCF valuation analysis template |
assets/forecast_report_template.md |
Revenue forecast report template |
Key Metrics & Targets
| Metric |
Target |
| Forecast accuracy (revenue) |
+/-5% |
| Forecast accuracy (expenses) |
+/-3% |
| Report delivery |
100% on time |
| Model documentation |
Complete for all assumptions |
| Variance explanation |
100% of material variances |
Input Data Format
All scripts accept JSON input files. See assets/sample_financial_data.json for the complete input schema covering all four tools.
Dependencies
None - All scripts use Python standard library only (math, statistics, json, argparse, datetime). No numpy, pandas, or scipy required.
1---2name: financial-analyst-23description: Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzing financial statements, building valuation models, assessing budget variances, or constructing financial projections and forecasts. Also applicable when users mention financial modeling, cash flow analysis, company valuation, financial projections, or spreadsheet analysis.4---5
6# Financial Analyst Skill
7
8## Overview
9
10Production-ready financial analysis toolkit providing ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. Designed for financial modeling, forecasting & budgeting, management reporting, business performance analysis, and investment analysis.
11
12## 5-Phase Workflow
13
14### Phase 1: Scoping
15- Define analysis objectives and stakeholder requirements
16- Identify data sources and time periods
17- Establish materiality thresholds and accuracy targets
18- Select appropriate analytical frameworks
19
20### Phase 2: Data Analysis & Modeling
21- Collect and validate financial data (income statement, balance sheet, cash flow)
22- **Validate input data completeness** before running ratio calculations (check for missing fields, nulls, or implausible values)
23- Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)
24- Build DCF models with WACC and terminal value calculations; **cross-check DCF outputs against sanity bounds** (e.g., implied multiples vs. comparables)
25- Construct budget variance analyses with favorable/unfavorable classification
26- Develop driver-based forecasts with scenario modeling
27
28### Phase 3: Insight Generation
29- Interpret ratio trends and benchmark against industry standards
30- Identify material variances and root causes
31- Assess valuation ranges through sensitivity analysis
32- Evaluate forecast scenarios (base/bull/bear) for decision support
33
34### Phase 4: Reporting
35- Generate executive summaries with key findings
36- Produce detailed variance reports by department and category
37- Deliver DCF valuation reports with sensitivity tables
38- Present rolling forecasts with trend analysis
39
40### Phase 5: Follow-up
41- Track forecast accuracy (target: +/-5% revenue, +/-3% expenses)
42- Monitor report delivery timeliness (target: 100% on time)
43- Update models with actuals as they become available
44- Refine assumptions based on variance analysis
45
46## Tools
47
48### 1. Ratio Calculator (`scripts/ratio_calculator.py`)
49
50Calculate and interpret financial ratios from financial statement data.
51
52**Ratio Categories:**
53- **Profitability:** ROE, ROA, Gross Margin, Operating Margin, Net Margin
54- **Liquidity:** Current Ratio, Quick Ratio, Cash Ratio
55- **Leverage:** Debt-to-Equity, Interest Coverage, DSCR
56- **Efficiency:** Asset Turnover, Inventory Turnover, Receivables Turnover, DSO
57- **Valuation:** P/E, P/B, P/S, EV/EBITDA, PEG Ratio
58
59```bash
60python scripts/ratio_calculator.py sample_financial_data.json
61python scripts/ratio_calculator.py sample_financial_data.json --format json
62python scripts/ratio_calculator.py sample_financial_data.json --category profitability
63```
64
65### 2. DCF Valuation (`scripts/dcf_valuation.py`)
66
67Discounted Cash Flow enterprise and equity valuation with sensitivity analysis.
68
69**Features:**
70- WACC calculation via CAPM
71- Revenue and free cash flow projections (5-year default)
72- Terminal value via perpetuity growth and exit multiple methods
73- Enterprise value and equity value derivation
74- Two-way sensitivity analysis (discount rate vs growth rate)
75
76```bash
77python scripts/dcf_valuation.py valuation_data.json
78python scripts/dcf_valuation.py valuation_data.json --format json
79python scripts/dcf_valuation.py valuation_data.json --projection-years 7
80```
81
82### 3. Budget Variance Analyzer (`scripts/budget_variance_analyzer.py`)
83
84Analyze actual vs budget vs prior year performance with materiality filtering.
85
86**Features:**
87- Dollar and percentage variance calculation
88- Materiality threshold filtering (default: 10% or $50K)
89- Favorable/unfavorable classification with revenue/expense logic
90- Department and category breakdown
91- Executive summary generation
92
93```bash
94python scripts/budget_variance_analyzer.py budget_data.json
95python scripts/budget_variance_analyzer.py budget_data.json --format json
96python scripts/budget_variance_analyzer.py budget_data.json --threshold-pct 5 --threshold-amt 25000
97```
98
99### 4. Forecast Builder (`scripts/forecast_builder.py`)
100
101Driver-based revenue forecasting with rolling cash flow projection and scenario modeling.
102
103**Features:**
104- Driver-based revenue forecast model
105- 13-week rolling cash flow projection
106- Scenario modeling (base/bull/bear cases)
107- Trend analysis using simple linear regression (standard library)
108
109```bash
110python scripts/forecast_builder.py forecast_data.json
111python scripts/forecast_builder.py forecast_data.json --format json
112python scripts/forecast_builder.py forecast_data.json --scenarios base,bull,bear
113```
114
115## Knowledge Bases
116
117| Reference | Purpose |
118|-----------|---------|
119| `references/financial-ratios-guide.md` | Ratio formulas, interpretation, industry benchmarks |
120| `references/valuation-methodology.md` | DCF methodology, WACC, terminal value, comps |
121| `references/forecasting-best-practices.md` | Driver-based forecasting, rolling forecasts, accuracy |
122| `references/industry-adaptations.md` | Sector-specific metrics and considerations (SaaS, Retail, Manufacturing, Financial Services, Healthcare) |
123
124## Templates
125
126| Template | Purpose |
127|----------|---------|
128| `assets/variance_report_template.md` | Budget variance report template |
129| `assets/dcf_analysis_template.md` | DCF valuation analysis template |
130| `assets/forecast_report_template.md` | Revenue forecast report template |
131
132## Key Metrics & Targets
133
134| Metric | Target |
135|--------|--------|
136| Forecast accuracy (revenue) | +/-5% |
137| Forecast accuracy (expenses) | +/-3% |
138| Report delivery | 100% on time |
139| Model documentation | Complete for all assumptions |
140| Variance explanation | 100% of material variances |
141
142## Input Data Format
143
144All scripts accept JSON input files. See `assets/sample_financial_data.json` for the complete input schema covering all four tools.
145
146## Dependencies
147
148**None** - All scripts use Python standard library only (`math`, `statistics`, `json`, `argparse`, `datetime`). No numpy, pandas, or scipy required.
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