Laboratory for Financial and Risk Analytics seminar
The Laboratory for Financial and Risk Analytics seminar series takes place every other Monday at 16:00, at the Faculty of Electrical Engineering and Computing, in the Black Hall. Attendance is free and open, but registration is necessary.
If you wish to be included in our mailing list for upcoming seminars and other news, please register here: https://forms.gle/83MTej9EAoxfJ55L6.
Monday, November 28, 2022
Title: Technological challenges in high-frequency trading
Abstract: This talk will discuss the organization of HFT systems, from machine learning models to order submission, and comment on points where statistics or machine learning are used.
Presenter: Igor Sočec, Susquehanna International Group
Registration link: https://forms.gle/8nauKVE6SePFtSnB7
Monday, December 12, 2022
Presenter: Jura Jurčević, Faculty of Economics and Business, University of Zagreb
Monday, November 14, 2022
Title: Options market making and why options markets are becoming a major mover of markets
Abstract: Using options an individual investor can achieve leverage and a non-linear payout, but a growing options market can have a great impact on the underlying market as well. Market makers dynamically hedge their positions, which can lead to some extreme situations like the gamma squeezes seen last year or more benign, but more tradable situations around option expirations. With growing option volumes and shrinking tenors, options are becoming an important factor in the market and noone can ignore them any more.
Presenter: Goran Dubček, InterCapital Asset Management
Monday, October 17, 2022
Title: Goal-Based Portfolio Allocation – from analytical solutions to reinforcement learning
Abstract: Goal-based investing is wealth management focused on achieving a predefined goal (amount of wealth) within a predefined timeframe. The main characteristic of a goal-based portfolio is the tendency to be fairly risk-exposed in the beginning and decrease risk as the time limit approaches. The primal challenge is to find an optimal allocation strategy that both accumulates the wanted amount of wealth and secures it as the payoff nears. This talk will cover several traditional approaches to dealing with the issue in question. Moreover, the problem of optimising a goal-based portfolio requires sequential decision making and therefore can be implemented in a reinforcement learning framework. The talk will also discuss this machine learning technique as a potential candidate for finding an optimal allocation policy.
Presenter: Tessa Bauman, Laboratory for Financial and Risk Analytics, UNIZG-FER