11/07/2023
Two Master in Finance students develop an ingenious predictive financial model for their final project, going beyond the theory and exploring an industry of potential.
IE Business School’s Master in Finance is designed to give students practical, hands-on experience with the modern financial landscape, priming them for success in the real world. At the end of the program, they complete a final project based on their personal and professional interests to demonstrate their learnings and upskill their expertise in their chosen path.
While students are exposed to the inner workings of the financial sector throughout the program, the final project goes one step further; it puts the student in the driver’s seat, tasking them with turning their ideas into reality. In this way, students step closer to their goals and make progress toward unlocking the next best version of themselves.
Harnessing tech in finance
Master in Finance students Gian Gael Gonzalez Büchsenschütz and Aleksei Fedotenkov based their project on innovations born from artificial intelligence and machine learning technologies.
They specifically used recurrent neural networks, which they fed with massive amounts of economic, financial and market data. As a result, these students built a custom algorithm that would allow them to predict the probability of default by any firm in one year.
Gael and Aleksei were also able to compile a list of unique parameters that can be employed to determine the weight of different data points in predicting defaults. What’s more, they managed to prove their algorithmic model’s utility in a wide range of applications.
Guided by their mentor
This year, adjunct professor Antonio Rivela led Gael and Aleksei through their final project, helping to break down complex mathematical concepts into easily understood pieces. Thanks to his support, the pair were able to dive straight into the testing phase of the project as they already had a strong grasp of the relevant theory from the beginning.
Both are particularly thankful for Antonio’s Financial Programming class—they say it was the perfect introduction to Python programming knowledge for the finance industry. Gael and Aleksei also credit their chosen specialization track, Financial Analytics & Digital Finance, with providing them a firm foundation in machine learning algorithms through the AI and Deep Learning in Python course.
Having this strong programming background played a big role in Gael and Aleksei successfully executing their final project. However, what really set them apart was their drive to go beyond course content and dig deeper into the latest developments in the field.
Further, their thirst for knowledge empowered them to withstand failures and remain levelheaded under stress. They were consequently well-equipped to adapt to risk and pursue constant improvement of their design. “By the time you’re starting the final project, you are already resilient,” Master in Finance student Gael says, adding, “If your algorithm suddenly stops working, as ours did, you know what is going on and that it’s somehow going to work out.”
A network of support
This project wouldn’t have been possible without the comprehensive datasets collected and compiled by past and current students in the Bloomberg Terminal. Gael and Aleksei had access to the latest data, going back five years or so, to train their default probability prediction model.
Departing from traditional research methods, they took a more hands-on approach when deciding what to do with the results of their final project.
In their own words, they “shopped around for investors or professionals who consult for debts and SMEs” who could use the algorithm to assess interest rates or debt maturities. The model can also be adapted to evaluate personal loans.
Gael explained that, far from being merely theoretical, their model “opens up a lot of doors to the practical world.” Its flexibility means that it has many applications in the financial and commercial sectors. Of course, pitching their algorithm to real companies and investors gave these students the opportunity to represent IE Business School while also networking and raising their professional profiles—Gael and Aleksei received a warm welcome from the local financial community thanks to the school’s stellar reputation.
Overcoming unique challenges
That said, this effort also posed some issues for the innovative students. Gael and Aleksei aimed to build upon the success of a previous project—value added that would make it more accurate in predicting how many companies would default. But given the number of similar initiatives around, the pair had their work cut out for them from the start, having to negotiate a competitive and challenging financial market. They were trying to establish a business in a difficult field, surrounded by some of the biggest players in the industry. Aleksei explains that by targeting smaller or emerging markets or focusing on personal loans—something big banks don’t oversee directly—their project stood out.
And it’s clear that they were successful in their final project: Gael and Aleksei’s algorithm managed to achieve an 81% success rate in predicting debt default and bankruptcy. Plans are already in the works to tweak the algorithm to further boost this figure.
If you want to launch a successful career in finance, it’s essential to start gaining real-world experience as early as possible. The Master in Finance immerses students in a wealth of opportunities to help kickstart their academic and professional careers and give them that well-honed edge required to excel in the field.
