Video Description
A Talk from Machine Learning in Quant Finance Conference 2024
The advent of massive amounts of textual data has spurred the development of econometric methodologies to transform qualitative sentiment data into quantitative sentiment variables and use those variables in an econometric analysis of the relationships between sentiment and other variables. This talk will briefly introduce this research field and illustrate possible applications in finance.
Speaker Bio
David Ardia
David is an IVADO professor in the Department of Decision Sciences at HEC Montréal. Trained in quantitative methods for finance, he has a keen interest in ML/NLP methods for asset allocation, risk management, and economic forecasting. In 2018, the Swiss Risk Association awarded him “Swiss risk manager of the year.” He is a regular member at GERAD, Quantact, and Fin-ML, an associate researcher at OBVIA, and an instructor at DataCamp.
Here’s the Annual Quant Insights Conference 2024 with a brand new line-up of talks from leading quant finance experts.
Video Description
A Talk from Machine Learning in Quant Finance Conference 2024
The ability to anticipate banking crises is vital for ensuring global financial stability. This talk will examine how machine learning methods, such as clustering, sequential feature selection, and ridge regression, can significantly enhance the identification and prediction of banking crises. I will share insights from a recent study that applied these techniques to predict the 2023 regional banking crisis in the United States, highlighting how data-driven approaches can detect patterns and anomalies in financial data to foresee crises. Additionally, the discussion will explore the advantages of machine learning models in minimizing risk and enhancing decision-making for investors.
Speaker Bio
Sebastian Petric
Sebastian Petric began his career in emerging market asset management, where he gained a deep understanding of the diverse factors influencing returns and risks. This foundation led him to specialize in multi-asset class investing and capital market research, where he developed a strong passion for data science and machine learning. At the University of Liechtenstein, he focuses on these areas and co-founded the Crisis Research Group, which leverages advanced analytics to identify and predict financial crises. As a future-focused leader set to begin his fellowship at the Oxford Internet Institute in October, he brings extensive expertise in leveraging innovative technologies. Committed to making an impact, he actively contributes through advisory roles and advocacy, aiming to shape the field through his thought leadership and publications.
Video Description
A Talk from Machine Learning in Quant Finance Conference 2024
In this paper, a shorter and more publication focused version of our recent article “A Bottom-Up Approach to the financial Markets” (Mahdavi-Damghani & Roberts, S. 2019 .) is presented. More specifically we propose a new approach to studying the financial markets using the Bottom-Up approach instead of the traditional Top-Down. We achieve this shift in perspective, by re-introducing the High Frequency Trading Ecosystem (HFTE) model Mahdavi-Damghani, B. 2017 . More specifically we specify an approach in which agents in Neural Network format designed to address the complexity demands of most common financial strategies interact through an Order-Book. We introduce in that context concepts such as the Path of Interaction in order to study our Ecosystem of strategies through time. We show how a Particle Filter methodology can then be used in order to track the market ecosystem through time. Finally, we take this opportunity to explore how to build a realistic market simulator which objective would be to test real market impact without incurring any research costs.
Speaker Bio
Dr. Babak Mahdavi-Damghani
Dr. Babak Mahdavi-Damghani (BMD) completed his PhD in Machine Learning for Quantitative Finance at the University of Oxford. He has a broad range of work experiences in the financial industry, notably having worked for Citigroup, Socgen, GAM Systematic, Credit Suisse, LSEG, the Oxford Algorithmic Trading Programme and Andurand Capital Management.
He has experience through all the major asset classes in both the buy and sell sides. He is the founder of EQRC and an alumni of the Oxford Man Institute of Quantitative Finance. He is also the author of numerous publications, including cover stories of Wilmott magazine and mathematical models currently taught at the CQF.
