Develop Your Specialist Skills!

Keep Pace with the Latest Thinking!

Stay on Top of Your Game!!

The Lifelong Learning Library (LLL) is a rich and ever-expanding set of resources that allows you to develop additional specialist skills and to keep pace with the latest thinking and developments in financial modelling, data analysis and related fields.

As for the materials in the main CertFM Program, all courses are developed by eminent practitioners and experts in their fields.

The LLL-courses are free-form access. They can be accessed and read in any order. Although they may contain quizzes, any such quizzes are to assist in learning reinforcement only. The quizzes are not mandatory, and the answers to questions can be viewed after taking each quiz.

Please note that the progress support services are not available for courses within the LLL.

The Lifelong Learning Library

The LLL currently consists of the following courses:

  • Advanced Corporate Finance and Valuation.
  • Optimisation Modelling in Business and Finance.
  • Principles of Project Finance Modelling.
  • Business Risk Management and Modelling.
  • Real Options Modelling and Valuation.
  • Essentials of Quantitative Finance.
  • Introduction to Credit Risk Modelling.
  • Principles of Machine Learning. 

The courses are numbered for convenience of reference only (they can be taken in any order).

LL.1  Advanced Corporate Finance and Valuation

Further topics in financial statement modelling. Modelling tax, deferred taxes, and other financing structures. Industry-specific approaches. Introduction to M&A modelling. Introduction to project finance modelling. Further topics in corporate valuation. Fade periods. Multi-stage terminal value periods and annuities. The Adjusted Present Value (APV) method. Advanced approaches to leverage-based cost of capital. Modigliani-Miller methods. Fama-French model. Advanced ratio analysis. Multi-variable Dupont analysis. Piotroski F-score. Kaplan-Schweser decomposition. Sharpe and Treynor ratios. Cycle-based ratios.

LL.2 Optimisation Modelling in Business and Finance

How optimisation arises. Why optimisation models can be challenging to build. Drivers of optimisation: Constraints and/or objectives. Business portfolio optimisation, criteria and challenges. Examples: Portfolio optimisation in oil and gas, and in pharmaceuticals and biotech. Markowitz theory. Portfolio optimisation in finance. Analytic and numerical solutions. Huang-Litzenberger formulas. Using Solver. Value-at-risk. Semi-deviation. Modelling exercises and quizzes.

LL.3 Principles of Project Finance Modelling

Introduction to project finance modelling. Differences to corporate modelling. Key ratios. Debt capacity and interest coverage. How circular references arise. Resolving circularity. Modelling exercises.

LL.4 Business Risk Management and Modelling

Role of risk assessment in business and corporate management. Frameworks to analyse and manage risk. Classical processes in risk management. The importance of risk identification. Risk identification processes. Risk mapping and descriptions. Failures in risk management. Business applications of risk analysis. Cost estimation and budgets. Risk registers. Cash flow uncertainty. Modelling exercises and quizzes.

LL.5 Real Options Modelling and Valuation

Introduction and definitions. Sequential optimisation. Tree-based decision-making. Switching options. Expansion options. Value of information and testing. Applications to mining, oil and gas, and resource projects: Impact of real options on operations, capex and project valuation.

LL.6 Essentials of Quantitative Finance

Principles of risk-neutral valuation. Validity and limitations. Options valuation. Black-Scholes’ formulae. Binomial trees. Random walks and Brownian motion. Simulation methods. Introduction to other grid-based methods. 

LL.7 Introduction to Credit Risk Modelling

Introduction to credit-risk modelling. Vasicek and Merton formula. Default probabilities. Transition matrices. Portfolio losses. Capital requirements.

LL.8 Principles of Machine Learning

Introduction to machine learning. Introduction and key concepts. Model calibration using optimisation. Conditional probability. Bayesian analysis. Value of information. Entropy. Cluster analysis. Optimal decision trees and information sequencing. Reinforcement learning. Other machine learning algorithms and methods

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