The following summarises the content of each course using key words. More information about each course can be seen by clicking the “More details” buttons.
III.1 Finance, Valuation and Markets
Financial statements. Meaning and interpretation. Core transactions and their effects. The modelling of integrated financial statements. Issues to consider. Step-by-step methodology. Balancing the balance sheet. Circular references. Introduction to M&A modelling. Introduction to project finance modelling. Cost of capital. Capital Asset Pricing Model. Weighted cost of capital. Leveraged and unleveraged cost of capital (classical and Modigliani-Miller approaches). Multi-factor models. Fama-French. Classical risk-return measures. Value-at-risk. Semi-deviation. Sharpe and Treynor ratios. Valuation principles and approaches in corporate finance. Comparables and asset-based approaches. Cash flow valuation. Enterprise and equity valuations and conversions. Annuities and value-driver formulas. Advanced cash flow valuation. Using annuities and terminal values effectively. Explicit forecast periods and best practices. Fade periods. Multi-stage terminal value periods and annuities. Using the Adjusted Present Value (APV) approach. Portfolio optimisation in business and finance. Markowitz model. Analytic and numerical solutions. Huang-Litzenberger formulas. Using Solver. Risk-neutral valuation. Options valuation. Black-Scholes’ formulae. Binomial trees. Random walks and Brownian motion. Simulation methods. Introduction to grid-based methods and finite differences. Introduction to sequential optimisation. Tree-based decision-making and real options. Credit-risk modelling. Vasicek and Merton formula. Default probabilities. Transition matrices. Portfolio losses. Capital requirements.
III.2 VBA Macros and Automation
Automation of common tasks. Key steps to getting started. Using Loops and Conditionality. Modules, variables, and objects. Working with ranges. Best practices. Run-time inputs and alerts. Debugging and checking. Worksheet events and changes. User-defined functions. Sensitivity and scenario analysis. Circular references. GoalSeek. Portfolio optimisation with Solver. Bespoke calculations for statistical and risk analysis. Monte Carlo Simulation with VBA. Automating steps in data cleaning. Automation of filters and extraction. Data refresh. Multiple database queries and repeated use of criteria ranges. Determining the position and size of a data set. Reversing data sets by rows or columns. Working through files in folders. Adding or deleting worksheet categories. Reversing and splitting text items. Algorithms in machine learning. Introduction and key concepts. Model calibration using optimisation. Value of information. Entropy. Cluster analysis. Optimal decision trees and information sequencing. Reinforcement learning. Other machine learning algorithms and methods