The following VB libraries are very useful for numerical computing, mathematical modeling and customized financial algorithm development. All the functions were designed to make computations on arrays (i.e., vectors or matrices) simply and quickly. I have shared comprehensive and robust optimization routines that enable calibration of financial models. My students had provided extensive text processing examples for wrapping and visualizing financial data in seconds.

Mathematical Algorithms
Math Codes
Standard Modules: 126
Function Procedures: 1029
Total Lines of Code: 44725
Math Project Details

Horror Matrices and Other Mathematical Poetry

Quantitative Financial Algorithms
Quant Codes
Standard Modules: 56
Function Procedures: 269
Total Lines of Code: 15671
Quant Project Details

Poems about Numerical Methods

This year I was working on a Data Visualization Technology to aggregate, and visualize fundamental data using different valuation techniques. In this post I am sharing the fundamental models that were used as the initial building blocks for creating and implementing the framework in C#. I attempted to be as comprehensive as possible in the codes; covering the range of comps that Dr. Damodaran offers in the Updated Data section.

Fundamental Models

VBA Codes

ADVFN Data Feeder (received positive feedback from major financial institutions)

I have to thank Dr. Aswath Damodaran from the Stern School of Business at New York University. His books and teachings notes helped to develop the algorithms for valuation scenarios. I also have to thank Professor George Athanassakos for providing the opportunity to work for the Ben Graham Centre of Value Investing and for introducing me to Mr. Warren Buffett.

I still remember when I was an HBA student taking the first Ivey course on Value Investing. I really enjoyed the class and the constructive conversations I had with Dr. George. His invaluable insights allowed me to fully explore the opportunities offered by financial markets. Hunting out bargains where there is a solid margin of safety between the price paid for a stock and its true value.

Finally, I have to thank my student Christopher Gilpin for his outstanding programming and math skills.

nico

The Math Libraries provide an excellent foundation for understanding the computational intricacies involved in financial modeling. By integrating financial modeling with the algorithms in the math libraries, one will actually achieve two goals at once: users find it easier to understand math theories/concepts and become more employable as they pick up real-world financial skills.

Based on my experience, there will be great demand for Quants as the financial world looks for people who “can do the job”. This will further strengthen the role of computational finance in teaching business students. To overcome the problem of programming, the instructor in advance can choose the functions that will be used to teach the lecture. This will allow the school to deliver creative solutions for the Financial Engineering class, and prepare the students to deliver new valuation models based on loss aversion and extreme value.

Do not destroy the essential catalyst of risk. By Lloyd Blankfein.