Building a Trading Model That Works for You Through Executive Education in Algorithmic Finance

Using sophisticated quantitative methods to operate on financial markets is a fascinating topic that Webster Vienna Private University, in partnership with Ronald Hochreiter, has decided to offer through Executive Education modules. Understanding in-depth computational methods is key to mastering a trading model that works for each individual, as there is never a one-size-fits-all approach.

A constant recalibration of these efforts is required as markets continue to change. Without updated knowledge and technology, it becomes easy to fall behind. To get a better look at what this new executive education program entails, we were able to speak with program director Ronald Hochreiter. Continue reading to learn more about it.

Understanding Different Quantitative Model Families

The importance of understanding the different quantitative model families in algorithmic finance courses cannot be understated. Ronald clarified why, saying “As we see that there is not a single one of the large Quant Hedge Funds which is able to outperform the market at any time, it is clear that there is not the one holy grail, but rather a combination of existing models, or creatively applying the board knowledge of what is available, to create a new method which might work well for a certain regime.”

Being able to understand different models allows professionals to assess and estimate which combination of methods will work best for them at any given time. Ronald is an expert on this. He is the Vice President of the Austrian Society for Operations Research as well as the President of the Academy of Data Science in Finance, and he has conducted research on everything from algorithmic finance to AI, data science, machine intelligence, and much more.

The Two-Module Approach Used in Our Executive Education in Algorithmic Finance

Ronald has designed the Webster Executive Education in Algorithmic Finance program to deliver a comprehensive overview of three different methodological approaches: Simulation, Optimization, and Machine Learning. This is done over the course of two modules and covers techniques such as Monte-Carlo Simulation for Pricing and Portfolio Decisions, Decision Optimization under Uncertainty, Contemporary Portfolio Optimization, and more.

Ronald further explained the modules, saying, “One important point in these two modules is to get an overview of the different types of models that can be estimated, i.e. from single-asset standard methods (such as classical Technical Trading) and single-asset Machine Learning and AI methods in the first module, to multi-asset methods in the second module. The important focus is on seeing that there are (too) many heterogeneous methods to reach a possible investment decision.”

Practical Hands-On Experience

We wanted to learn a bit more about who this program is designed for. “Every person that is interested to see the variety of models and methods to quantitatively compute investment decisions, both Executives and decision-makers as well as prospective Quants,” Ronald said, adding that, “The focus is on understanding differences and shortcomings of the available methods.”

Roleplaying simulations are implemented in both modules to help professionals gain practical experience. Additionally, Ronald told us that “The participants receive the program code (in the programming language R) of all presented models, which they can use to recap the models and content hands-on after the lectures, as well as to use it as a blueprint for their own model generation.” He noted that this code can be used in other ways, saying, “It is known that Python is the programming language currently favoured by Quants worldwide, but the code from the course is not meant to be restricted to one programming language, but rather meta code which can be translated to any favoured language.”

Interested in programmatic business practice?

Contact WVPU about our Executive Education programs for more information!