At the Applied Machine Learning Days 2022 at EPFL Lausanne, aisot co-presented the track “Advances of ML Approaches for Financial Decision Making & Time Series Analysis”.
aisot team members and advisors Petter Kolm, Fabrizio Lillo, Nino Antulov-Fantulin & Thomas Asikis discussed in fully booked sessions “Advances of Machine Learning Approaches for Financial Decision Making & Time Series Analysis” and related topics. The track was dedicated to the recent advancement in deep and machine learning and its profound influence on many fields, including finance. From the program: Mathematical and quantitative finance provide a plentitude of challenging prediction problems that can be used as benchmarks for deep and reinforcement learning algorithms. Specifically, financial markets represent a complex interplay of agents interacting through auction-market mechanisms at different time scales and with different objectives. Therefore, it is not surprising that it continues to receive attention from computer scientists, physicists, social scientists, and others interested in addressing a multitude of challenging prediction and decision problems.
The sessions enabled the exchange of recent research and insights amongst aisot team members and advisors with researchers interested in machine learning approaches for decision making and times series analysis of financial markets.
aisot advisor Fabrizio Lillo about applications of time varying interactions modeling, e.g. in connection with stock market volatility and flash-crashes.
aisot advisor Petter Kolm and aisot Head of Research Nino Antulov-Fantulin about challenges in financial markets, especially in investment management and trading.
aisot team members and advisors in the panel discussion of the track.
