Title: Multi-objective reward generalization: improving performance of Deep Reinforcement Learning for applications in single-asset trading   
Speaker: Federico Cornalba, Department of Mathematical Sciences, University of Bath, UK

Abstract: In this talk, we investigate the potential of Multi-Objective, Deep Reinforcement Learning for stock and cryptocurrency single-asset trading: in particular, we consider a Multi-Objective algorithm which generalizes the reward functions and discount factor (i.e., these components are not specified a priori, but incorporated in the learning process). Using several assets (BTCUSD, ETHUSDT, XRPUSDT, AAPL, SPY, NIFTY50), we discuss the reward generalization property of the proposed Multi-Objective algorithm, preliminary statistical evidence for its predictive stability over the corresponding Single-Objective strategy, and its performance for sparse reward mechanisms.

Joint work with C. Disselkamp (pagent.ai), D. Scassola (aindo, and Università degli Studi di Trieste), and C. Helf (pagent.ai).

- date: 18 July 2024

- time: 2.00 PM

- room: 205, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale.

- on-line: meet.google.com/mbu-duor-fva

Title: ESG Data Imputation and Greenwashing
Speaker: Giulia Crippa,Operations Research and Financial Engineering, Princeton University & The Aggregate Confusion Project at Sloan School of Management, MIT 

Abstract: In recent years, there has been a notable surge of Environmental, Social, and Governance (ESG) investing. This paper provides a simple and comprehensive tool to tackle the issue of missing ESG data. Firstly, it allows to shed light on the failure of ESG ratings due to data sparsity. Exploiting machine learning techniques, we find that the most significant metrics are promises, targets and incentives, rather than realized variables. Then, data incompleteness is addressed, which affects about 50% of the overall dataset. Via a new methodology, imputation accuracy is improved with respect to traditional median-driven techniques. Lastly, exploiting the newly imputed data, a quantitative dimension of greenwashing is introduced. We show that when rating agencies do not efficiently impute missing metrics, ESG scores carry a quantitative bias that should be accounted by market players. 

- date: 18 July 2024

- time: 3.00 PM

- room: 205, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale.

- on-line: meet.google.com/mbu-duor-fva