CMA Intermediate Financial Management and Business Data Analytics Classes

CMA Intermediate Paper 11 — Financial Management and Business Data Analytics

Paper 11 is the most forward-looking paper in CMA Intermediate Group 2 — it combines classical financial management with business data analytics, reflecting how the modern CMA role has evolved. Today's CMAs are not just costing specialists — they are business intelligence professionals who combine financial acumen with data analysis capabilities.

Financial Management

• Capital Budgeting — NPV (Net Present Value), IRR (Internal Rate of Return), MIRR, Profitability Index, Payback Period, Discounted Payback; capital rationing; risk in capital budgeting (sensitivity analysis, scenario analysis, simulation basics)
• Cost of Capital — Cost of equity (CAPM and Gordon's Dividend Growth Model), cost of preference shares, cost of debt (pre-tax and post-tax), WACC computation
• Capital Structure — Modigliani-Miller (with and without tax), Trade-off theory, Pecking Order theory, EBIT-EPS analysis, optimal capital structure determination
• Working Capital Management — Operating cycle, estimation of working capital requirement, cash management (Baumol model, Miller-Orr model), receivables management (credit policy), inventory management (EOQ, JIT)
• Dividend Decisions — Gordon's model, Walter's model, MM irrelevance theory, dividend policy factors
• Sources of Finance — Equity, debt, hybrid instruments, leasing (finance and operating lease under Ind AS 116), venture capital, crowdfunding

Business Data Analytics

• Introduction to data analytics — Descriptive, predictive, and prescriptive analytics; structured vs unstructured data
• Tools and techniques — Excel-based data analysis, pivot tables, basic SQL concepts for data extraction
• Data visualisation — Dashboard design principles, charts and graphs for financial reporting
• Regression analysis for forecasting — Simple and multiple regression applied to cost and revenue forecasting
• Big Data and its impact on cost management — Real-time cost monitoring, IoT data for manufacturing cost analysis
• Applications in cost management — Activity-based cost analysis using data analytics, variance analysis automation, exception reporting