QFRG and DSLab Seminar

This time Adam Korniejczuk and Robert Ślepaczuk will present the results of their study “Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market”

Abstract: 
The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. The amalgamation of quantitative and machine learning methods, including the Kelly criterion, and an ensemble of machine learning classifiers have improved risk-adjusted returns and increased the immunity to transaction costs over existing approaches. The study seeks to provide an integrated approach to optimal signal detection and risk management. As a part of this approach, innovative ways of optimizing take profit and stop loss functions for daily frequency trading strategies have been proposed and tested. All of the tested approaches outperformed appropriate benchmarks. The best combinations of the techniques and parameters demonstrated significantly better performance metrics than the relevant benchmarks. The results have been obtained under the assumption of realistic transaction costs, but are sensitive to changes in some key parameters. In AI, this paper contributes by integrating machine learning classifiers with graph clustering techniques to improve predictive models for asset price movements in algorithmic trading. The approach offers a robust application for financial systems in engineering, optimizing trade execution and risk management strategies through advanced algorithmic methods.

The meeting will take place on October 7, 2024 (Monday) at 6:40 pm at the Faculty of Economic Sciences in room B002 (ul. Długa 44/50, Warsaw) and also simultaneously on Zoom. Meeting ID: 989 8885 9470, Passcode: 204578, the direct link: 
https://uw-edu-pl.zoom.us/j/98988859470?pwd=ADa21ixtr3pYxrUs3PJ5BkjUOuT9QO.1 
Please log in at 6:30 pm CEST. The presentation is scheduled for about 45 minutes, and after that, we invite you to a discussion.