Nestoras Chalkidis

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Nestoras Chalkidis gratuated in 2014 from the Department of Mathematics of the Aristotle University of Thessaloniki, with a bachelor degree in Mathematics. In 2016 he obtained a Master of Science degree in Complex Systems and Networks, from the Department of Mathematics of the Aristotle University of Thessaloniki. His thesis with the title ”Statistical Analysis of epigenetic data for CLL patients” held of in the Centre for Research and Technology Hellas (CERTH), in Thessaloniki. In September 2017 he joined the University of Liverpool, and the Institute for Risk and Uncertainty as an MRes student.

Research Interests: Advances in Financial Risk Analysis, Modelling, and Management

Research project title: Developing a decision support system to enable sustainable growth of small-scale vertical farms through environmental impact and financial risk modelling.

Project description: This project is a multidisciplinary work which combines three major fields Mathematics, Machine Learning (ML) and Finance. The purpose of this project is to apply Artificial Intelligence (AI) and ML algorithms to Finance and in particular to Trading.
In fact, within the context of the research project with the title "Advances in Financial Risk Analysis, Modelling, and Management" the plan is to explore new and advanced ways of modelling financial systems. ML algorithms, AI and/or data analysis will be applied in a try to build a trading system application. A trading system is a system of rules or parameters, which determines when to buy or sell an equity in the stock market. To this end, a predictive model will be built as the foundation of a trading strategy.
For my PhD work, I will continue in the direction of using ML algorithms fed with market data. I will use richer raw financial data such as intra-day and order book data. Furthermore, I will explore a range of ML models, including Deep Learning, developing my own where appropriate. Finally, I will evaluate not just ML models, but trading strategies build on top of these models.

Supervisory team: Professor Rahul Savani, Mr. Jason Laws

ORCID: 0000-0003-1936-9986