Thalesians/QFGG (Frankfurt) - Quant Evening

Full title: Thalesians/Quant Finance Group Germany (Frankfurt) - Quant Evening

Schedule

• History, Mission Statement and Future of the Group & Welcome from PPI Host

• Saeed Amen (Thalesians) - Quant trading in FX & PyThalesians
• Jochen Papenbrock (PPI & Firamis) - Correlation Networks
• Miguel Vaz (D-Fine) - Networks with Python/Spark
• Adrian Zymolka (Axioma) - Multi-Period Optimization

Event finish and drinks!

Thanks for Jochen Papenbrock and Adrian Zymolka for organising and for PPI AG for hosting. Tickets will be FREE for this event!

You can access the Thalesians/Quant Finance Germany (Frankfurt) LinkedIn Group page here (https://www.linkedin.com/grp/home?gid=8321682).

Selected talk abstracts

Saeed Amen (Thalesians) - Quant trading in FX & PyThalesians - We shall present how to use the open source PyThalesians Python library to analyse FX markets, plot data and also to create FX trading strategies.

Jochen Papenbrock (PPI & Firamis) - Correlation Networks - On the rise: correlation networks experience a vibrant time. They are currently emerging due to their ability of capturing systemic risk and extrinsic fragility. They help to create antifragile portfolios which actually gain from crisis and also do well in calm market times. This is because of their higher order diversification properties which systematically harvest the risk premiums of multiple assets. Also, risk managers, regulators and auditors appreciate correlation networks due to their simplicity and effectiveness - and their ability to scan portfolios for risk and to visualize portfolio fragility. In the talk I will give an overview on all these aspects of correlation networks.

Adrian Zymolka (Axioma) - Multi-Period Optimization -

Modern optimizers can handle complex portfolio construction problems with many realistic requirements. In practice - particularly in production environments - such tasks usually focus on immediate decisions to take ('the next portfolio/trade list') which makes them myopic by nature. In contrast, multi-period optimization tackles an entire portfolio evolution through time and determines the optimal allocations/trades for the current as well as subsequent rebalancings at once. This allows to exploit better trade-offs between short- and long-term effects as well as between averaging and accumulating measures, leading to better informed decisions in view of expected future developments.

In this talk, I briefly introduce our approach to multi-period optimization, differentiate it against other time-referenced portfolio construction concepts, and present some typical application cases, like trade scheduling, tracking around benchmark reconstitutions, alpha factor selections, or multi-horizon alpha integration.

(A longer presentation on the topic and the application cases is planned for a future Thalesians seminar.)