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Justin Corleone
Justin Corleone

MATLAB for Financial Modelling: A Comprehensive Book on Theory, Implementation and Practice

Financial Modelling: Theory, Implementation and Practice with MATLAB

Financial modelling is a vital skill for anyone who wants to understand, analyze, or predict the performance of a business, a project, or an investment. Financial modelling can help you make better decisions, optimize your strategies, and manage your risks. But how can you learn financial modelling in a practical and effective way?

Financial Modelling: Theory, Implementation and Practice with MATLAB


In this article, we will introduce you to a powerful tool that can help you with financial modelling: MATLAB. We will explain what financial modelling is, why you should use MATLAB for financial modelling, how to use MATLAB for financial modelling, and what are some examples of financial modelling with MATLAB. We will also show you how to access the MATLAB file that contains the code and data for the book "Financial Modelling: Theory, Implementation and Practice" by Joerg Kienitz and Daniel Wetterau. This book is a comprehensive guide that covers various topics in financial modelling, such as data analysis, optimization, simulation, machine learning, artificial intelligence, pricing, hedging, portfolio optimization, risk management, credit risk modelling, and more.

By the end of this article, you will have a clear understanding of how to use MATLAB for financial modelling, and how to apply it to real-world problems. You will also be able to download the MATLAB file and run the code yourself.

What is Financial Modelling?

Financial modelling is the process of building a forecast of an organizations future financial performance. A financial model considers the organizations past results, as well as current earnings and expenses, to predict the impact of their future decisions, the performance of particular assets and the overall financial health of the organization.

Financial models are used for various purposes, such as:

  • Valuation: To estimate the value of a business or an asset based on its expected cash flows.

  • Investment analysis: To evaluate the profitability and risk of an investment project or opportunity.

  • Budgeting and planning: To set goals and allocate resources for a business or a project.

  • Scenario analysis: To test how different assumptions or events affect the outcome of a model.

  • Decision making: To support and justify strategic or operational decisions.

Financial models can be built using different methods and techniques, depending on the purpose and complexity of the model. Some of the common methods and techniques are:

  • Discounted cash flow (DCF) analysis: To calculate the present value of future cash flows using a discount rate.

  • Sensitivity analysis: To measure how the output of a model changes with respect to changes in one or more input variables.

  • Monte Carlo simulation: To generate random scenarios based on probability distributions and statistical methods.

  • Regression analysis: To estimate the relationship between a dependent variable and one or more independent variables.

  • Machine learning: To use algorithms and data to learn from patterns and make predictions.

Why Use MATLAB for Financial Modelling?

MATLAB is a software platform that combines a high-level programming language with a rich set of tools and libraries for numerical computation, data analysis, visualization, and application development. MATLAB is widely used by engineers, scientists, researchers, and students for various applications, including financial modelling.

There are many benefits and features of using MATLAB for financial modelling, such as:

Data Analysis and Visualization

MATLAB can help you with data analysis and visualization for financial modelling in several ways, such as:

  • Importing and exporting data from various sources and formats, such as Excel, CSV, databases, web services, etc.

  • Manipulating and transforming data using functions and operators, such as filtering, sorting, aggregating, reshaping, etc.

  • Performing statistical analysis using built-in or custom functions, such as descriptive statistics, hypothesis testing, correlation, regression, etc.

  • Creating interactive and dynamic charts and graphs using various types and styles, such as line plots, bar charts, pie charts, histograms, scatter plots, etc.

  • Exploring and discovering patterns and trends in data using tools such as Data Cursor, Brushing, Linking, etc.

Optimization and Simulation

MATLAB can help you with optimization and simulation for financial modelling in several ways, such as:

  • Solving linear and nonlinear optimization problems using solvers and algorithms, such as linear programming, quadratic programming, integer programming, genetic algorithm, etc.

  • Performing Monte Carlo simulation using random number generators and probability distributions, such as uniform, normal, binomial, Poisson, etc.

  • Creating custom simulation models using Simulink, a graphical environment for modelling dynamic systems.

  • Analyzing the results of optimization and simulation using tools such as Optimization Toolbox️ , Global Optimization Toolbox️ , Statistics and Machine Learning Toolbox️ , etc.

Machine Learning and Artificial Intelligence

MATLAB can help you with machine learning and artificial intelligence for financial modelling in several ways, such as: