Level II Courses

OVERVIEW IN KEYWORDS

The following summarises the content of each course using key words. More information about each can be seen by clicking the “More details” buttons.

II.a Economics and Financial Evaluation

Growth formulae and present values. Measuring growth. Classical and logarithmic measures. Growth calculations. Inflating and discounting. Time-period conversions. Interest rates and calculations. Time-value of money. Bond yield. Yield curve. Risk premia. Interest compounding methods and formulas. Links between interest compounding and model time periods. Accruals. Bootstrapping and yield curves. Annuities and discounting. Discounted cash flow. Annuity formulas. Terminal value calculations. Capitalization rates. Economic evaluation. Breakeven, time to breakeven, cumulative breakeven, discounted breakeven. Calculation of returns. Internal rate of return (IRR). Uses and limitations. Discounted cash flow, net present value (NPV). Comparison of IRR with NPV. Loans and mortgages. Payback amounts and periods. Capital versus interest. Implied interest rates. Depreciation and amortization. Methods and calculation approaches. Physical and financial assets. Cost of capital, risk and return. Risk and return. Portfolio effects. Correlation and diversification. Markowitz theory. Capital Asset Pricing Model. Cost of equity. Ratio analysis. Key ratios: Measuring profitability, operating performance and financial position. Definitions and variations. Dupont analysis. Variance analysis.

II.b Data Analysis, Manipulation and Statistics

Databases in Excel. Filters and sorting. Excel Tables. Advanced filters and criteria-based extraction. Pivot Tables. Slicers. Drilldowns. Dynamic ranges and functions. Unique values, filters, and sorting. Variable-sized ranges. Advanced lookup functions. Data Structures for modelling. Statistical analysis and functions. Single- and multi-variable statistics. Confidence intervals. Linear regression. Multiple regression. Array calculations. Statistical analysis. Introduction to probability distributions. Inverse functions. Random sampling. Data manipulation with Excel. Cleaning and correcting. Splitting and joining. Flat tables. Identifying unique items. Creation of keys. Text and Date functions. Using PowerQuery for data analysis and manipulation. Loading data. Generating reports. Grouped queries. Pivoting and unpivoting. Appending and consolidating data sets. Advanced queries and manipulation with the M-language. PowerPivot. DAX measures. The Excel DataModel. Evaluation contexts and measures. Functions and iterator functions. Sensitivity analysis. CUBE functions. Time-intelligence.

II.c Corporate Finanace, Business Analysis, and Valuation

Introduction to financial statements. Meaning and interpretation. Core transactions and their effects on each statement. The modelling of integrated financial statements. Objectives. Differences with historical analysis. Issues to consider. Step-by-step methodology. Generalising the models to include specific features. Tips and tricks. Balancing the balance sheet. Circular references. Consistency checking. Error elimination.Ratio analysis. Ratio for long-term and short-term performance: Profitability, operations, efficiency, financing and gearing, and solvency. Creating consistent measures. Dupont analysis and variance analysis. Decomposition into two or more factors. Normal and logarithmic scales. Two-factor variance analysis. Symmetric and non-symmetric approaches. Business valuation. Valuation of corporations and equity. Using comparables and multiples. Capital structure. Cash flow methods. Introduction to discounting. Annuity formulas. Linking long- and short-term forecasts.

II.d Model Planning, Design and Best Practices

Frameworks and tools for contextual analysis of a decision. Frameworks and tools to analyse decision context and business strategy. Macro economics, five forces analysis, portfolio composition analysis. Introduction to risk, uncertainty and optimisation, and their role in decision-making and model design.
In-depth model planning. Mapping the decision to a model. Criteria and model outputs. Data versus formula-driven structures. Data sources and structures. Defining logic, variables, flow and granularity. Historical versus forecasted analysis. Role of inflation. Updating requirements. Horizontal or vertical time axis. Structural limitations versus parameter flexibility. Benefits of modularity. Approaches to link modules. Decision sequences. Logic reversals.
Best practices in model building. Principles for design, layout, structure, and formatting. Principles of transparency. Complexity reduction and optimisation. Circularities and other issues.

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