CMA Inter Paper 11 (FMDA) cheat sheet

Financial management covers planning, raising, allocating, and controlling funds to maximize owner wealth while balancing risk, liquidity, and profitability.

Profit maximization ignores timing, risk, and capital employed; wealth maximization uses the present value of expected future cash flows and aligns with shareholder value.

An agency conflict arises when managers pursue perquisites or empire-building; monitoring, incentives, and governance reduce it.

Core functions include estimating capital needs, choosing the financing mix, investing in assets, managing working capital, and setting dividend policy.

The risk-return trade-off means higher expected return generally requires accepting higher variability or a greater probability of loss.

Time value of money means a rupee today is worth more than a rupee later because it can be invested; NPV and IRR incorporate this principle.

The liquidity-profitability trade-off means holding more current assets reduces insolvency risk but lowers return on assets.

The Indian regulatory framework includes the Companies Act 2013, SEBI regulations, RBI directions, and tax law; compliance affects financing and payout choices.

Equity shares are permanent capital with a residual claim and voting rights; the cost is not tax-deductible and dividends are discretionary.

Preference shares carry a fixed dividend, priority over equity, and usually limited voting; they may be cumulative, participating, convertible, or redeemable.

Debt finance includes term loans, debentures, and bonds; interest is tax-deductible but creates fixed obligations and covenants.

Retained earnings are internal equity; their opportunity cost equals shareholders' required return, and they avoid flotation costs.

Hybrids such as convertible debentures, warrants, and optionally convertible instruments blend debt and equity features.

A finance lease transfers substantially all risks and rewards of ownership to the lessee; an operating lease is shorter-term and retains more lessor risk.

Trade credit and factoring finance receivables; commercial paper is short-term unsecured corporate borrowing, usually rated.

External sources include ECB, FDI, and preferential allotment; each has regulatory, currency, and dilution implications.

Current ratio = current assets / current liabilities; quick ratio = (current assets - inventory - prepayments) / current liabilities.

Debt-equity = total debt / shareholders' equity; interest coverage = EBIT / interest; lower coverage signals higher financial risk.

Inventory turnover = COGS / average inventory; receivables turnover = credit sales / average receivables; days = 365 / turnover.

Gross profit margin = gross profit / sales; net profit margin = PAT / sales; operating margin = EBIT / sales.

ROA = PAT / average total assets; ROE = PAT / average equity; DuPont: ROE = net margin × asset turnover × equity multiplier.

EPS = (PAT - preference dividend) / weighted average equity shares; P/E = market price per share / EPS.

Dividend payout = DPS / EPS; retention ratio = 1 - payout; sustainable growth ≈ ROE × retention when ratios are constant.

Common-size and trend analysis compare percentages over time; ratio comparability requires consistent accounting policies.

WACC = Σ (weight_i × after-tax cost_i), using market values where possible; the weights must sum to one.

After-tax cost of debt = Kd(1 - tax rate) for interest-bearing debt; flotation costs raise the effective cost.

Cost of preference = preference dividend / net proceeds; redeemable preference requires a yield-to-maturity style calculation.

CAPM cost of equity: Ke = Rf + β(Rm - Rf); beta measures systematic risk, and the risk-free rate should match the horizon.

Dividend growth model: Ke = D1/P0 + g; g must be sustainable and less than Ke; P0 is the ex-dividend price net of flotation costs.

Cost of retained earnings equals shareholders' required return, often approximated by Ke; it is not zero.

Marginal cost of capital is the cost of the next rupee raised; it rises as the financing volume increases.

Book-value weights are stable but may misstate the economic mix; market-value weights reflect current opportunity cost.

DOL = contribution / EBIT = % change in EBIT / % change in sales; higher fixed operating costs increase DOL.

DFL = EBIT / [EBIT - interest - preference dividend/(1 - tax)] = % change in EPS / % change in EBIT.

DCL = DOL × DFL = contribution / [EBIT - interest - preference dividend/(1 - tax)].

Break-even sales = fixed costs / contribution margin ratio; break-even units = fixed costs / contribution per unit.

EBIT-EPS indifference point: EPS is equal under two financing plans; solve (EBIT - I1)(1 - t) - PD1 divided by n1 = (EBIT - I2)(1 - t) - PD2 divided by n2.

NI approach: more debt lowers WACC and raises value because debt is cheaper; NOI approach says WACC and value are independent of leverage.

MM without taxes: value is independent of leverage; with taxes, value rises by the tax shield tD, subject to distress costs.

Pecking order: firms prefer internal funds, then debt, then external equity; trade-off theory balances tax shield and distress costs.

NPV = Σ CFt/(1 + r)^t - initial outlay; accept if NPV > 0; among mutually exclusive projects, choose the highest NPV.

IRR is the discount rate making NPV zero; accept if IRR > required return; multiple IRRs can occur with non-conventional cash flows.

Payback period is the time to recover the initial outlay; it ignores cash flows after payback and ignores time value unless discounted.

Profitability index = PV of future cash flows / initial investment; accept if PI > 1 for independent projects.

ARR = average annual accounting profit / average investment; it uses profits, not cash flows, and ignores time value.

Incremental cash flows exclude sunk costs and allocated fixed overhead; include opportunity costs and working-capital changes.

Depreciation tax shield = depreciation × tax rate; add back depreciation to profit when estimating operating cash flow.

NPV and IRR conflict for mutually exclusive projects when scale or timing differs; NPV is preferred because it measures value added.

Dividend irrelevance (MM) assumes perfect markets, no taxes, and no flotation costs; firm value depends on earning power, not payout.

Walter model: P = [D + (E - D)r/Ke] / Ke; r > Ke favors retention, while r < Ke favors payout.

Gordon growth model: P0 = D1/(Ke - g); g = retention ratio × ROE; the model assumes constant growth and Ke > g.

A residual dividend policy pays dividends only from earnings left after funding positive-NPV projects at the target capital structure.

The Lintner model says firms adjust dividends gradually toward a target payout, smoothing changes and avoiding cuts.

A bonus issue capitalizes reserves into shares; a stock split increases share count and lowers face value without changing reserves.

The clientele effect means payout policy attracts investor groups with different tax or income preferences; signaling can move prices.

In India, dividend distribution tax has changed over time; tax treatment depends on current law, so evaluate net-of-tax returns.

Operating cycle = inventory days + receivable days - payable days; a shorter cycle generally lowers the working-capital funding need.

An aggressive current-asset financing policy uses short-term funds for current assets; a conservative policy uses more long-term funds.

EOQ = √(2DS/H), where D = annual demand, S = ordering cost per order, and H = holding cost per unit per year.

Reorder point = lead-time demand + safety stock; safety stock buffers demand and lead-time uncertainty.

Cash conversion cycle = days inventory outstanding + days sales outstanding - days payables outstanding.

Baumol model: optimal cash transfer = √(2 × annual cash need × transaction cost / opportunity cost per rupee).

Miller-Orr sets upper and lower cash limits and returns to a target when limits are breached, allowing random cash flows.

Factoring sells receivables at a discount; commercial paper and bank overdrafts are short-term funding; monitor the working-capital gap and MPBF.

Business analytics uses data, statistics, and models to support decisions; it converts raw data into actionable insight.

Descriptive analytics summarizes past performance; diagnostic analytics explains causes; predictive analytics forecasts; prescriptive analytics recommends actions.

CRISP-DM phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Analytics maturity moves from reporting to forecasting to optimization; value depends on decision relevance, not data volume alone.

Big data is characterized by volume, velocity, variety, veracity, and value (the 5Vs).

Data governance covers ownership, quality, security, privacy, and compliance; poor governance undermines model reliability.

A KPI is a quantifiable measure tied to a business objective; dashboards should track a few decision-relevant KPIs.

Correlation does not imply causation; causal claims need experimental or quasi-experimental design.

Nominal data are labels with no order; ordinal data have order but unequal intervals; interval data have equal intervals but an arbitrary zero; ratio data have a true zero.

Structured data fit rows and columns; unstructured data include text, images, and audio; semi-structured data include JSON and XML.

Primary data are collected for the current purpose; secondary data were collected earlier or by others and need validity checks.

Data cleaning handles missing values, duplicates, outliers, inconsistent formats, and invalid codes; document every transformation.

The mean is sensitive to outliers; the median is robust; the mode is the most frequent value; choose summaries based on data type and skew.

Variance and standard deviation measure dispersion; range is maximum minus minimum; interquartile range covers the middle 50%.

Skewness measures asymmetry; positive skew has a long right tail; kurtosis measures tail heaviness relative to a normal distribution.

Data quality dimensions include accuracy, completeness, consistency, timeliness, validity, and uniqueness.

Spreadsheets support formulas, pivot tables, what-if analysis, and charts; absolute and relative references control copying behavior.

Spreadsheet risks include hard-coded values, broken links, and inconsistent formulas; use validation, documentation, and review.

The Python data stack includes pandas for tabular manipulation, NumPy for arrays, matplotlib/seaborn for charts, and scikit-learn for models.

Jupyter notebooks combine code, output, and narrative; reproducibility requires fixed environments and version control.

SQL extracts and aggregates data from relational databases before analysis in spreadsheets or Python.

Chart choice: bar for categories, line for time trends, scatter for relationships, histogram for distribution, and box plot for spread and outliers.

Power Query/ETL automates data import and cleaning; Power Pivot/DAX supports data models and measures.

Data visualization should use clear labels, appropriate scales, and minimal chart junk; misleading axes distort interpretation.

Analytics applications include customer churn prediction, credit scoring, fraud detection, demand forecasting, and workforce attrition.

Finance uses analytics for budgeting, variance analysis, working-capital monitoring, risk scoring, and transaction anomaly detection.

Marketing uses segmentation, campaign attribution, price optimization, and recommendation systems; measure uplift, not just correlation.

Operations use demand forecasting, inventory optimization, predictive maintenance, and route optimization.

Emerging areas include AI/ML, NLP, generative AI, IoT, cloud analytics, and blockchain; each raises governance and skill needs.

Prescriptive analytics can use optimization and simulation; outputs require human judgment and constraint validation.

Data privacy laws and ethics limit collection and use; anonymization, consent, and purpose limitation are key controls.

Model risk arises from bias, drift, overfitting, and poor data; monitor performance and retrain when conditions change.