In today’s world, many scientific and engineering fields have advanced to new levels of complexity and technology. However, when it comes to financial analysis, the tools have yet to keep up with this progress. Too often, financial analysis tasks are carried out using legacy software that has poor documentation, insufficient scalability, and a narrow field of use. This statement is equally valid for financial companies and educational institutions.
MATLAB provides many examples and studies on current financial topics taught in higher education. It is a great way to demonstrate real-life examples to students in an easy-to-understand manner. In addition, as companies also use MATLAB in their own software ecosystem, students can gain useful experience in MATLAB for finance and gain a competitive edge when entering the labor market. This leads to a well-shaped collaboration between financial education, work and research by using MATLAB for all these tasks:

• Rapid, targeted, well-documented functions and objects for a wide range of high-complexity financial analysis operations
• Toolset for the integration of cutting-edge AI-based approaches for next-generation financial analysis tasks and data processing
• A solid foundation for improving or even substituting legacy toolsets, systems
• Detailed, problem-focused, out-of-the-box examples for using the most common tools used in financial analysis tasks

In this video, we will discuss how to do complex financial tasks using MATLAB’s finance-related tools.

Datafeed Toolbox can help with:

• Professional, automated data acquisition of live financial data and access to news and trading systems.
• Interaction with already existing relational or non-relational databases (MySQL, PostgreSQL, NoSQL for Apache, Cassandra, MongoDB, etc.)

Risk Management Toolbox provides functionality for:

• Consumer, corporate, market, and insurance risk analysis,
• Modeling of exposure at default, loss given, and other indicators.

Datafeed Toolbox can provide functions and workflows for analyzing and modeling time-related data.

Financial Toolbox can help with credit risk, portfolio optimization, cash flow, and regression analysis.

Financial Instruments Toolbox provides functionality to analyze interest-rate (securities, bonds, swaps, caps, floors), derivative instruments (equity, credit, interest-rate based).

After watching this video, you will:

• Gain a deeper understanding of MATLAB’s financial tools capabilities,
• Be able to select which tools they need for each specific task.

Agenda:

• Online Teaching with MATLAB and Simulink
• Introduction to the finance-related toolboxes in MATLAB, with emphasis on the ease of use and flexibility
• Use cases and built-in examples
• Integration of multiple MATLAB toolboxes into custom applications
• AI Example
• Q&A

Featured products

All products mentioned in this webinar are developed by MathWorks.

• MATLAB
• Simulink
• Deep Learning Toolbox
• Statistics and Machine Learning Toolbox
• Datafeed Toolbox
• Risk Management Toolbox
• Datafeed Toolbox 
• Financial Toolbox 
• Financial Instruments Toolbox

Learn more

Learn more about the Campus-Wide License here.