Co-developing Funding Bids with the Smart Data Foundry (SDF)

ECFI group member participation: Prof. Gbenga Ibikunle, Prof. Tiejun Ma, Dr. Miguel de Carvalho

Collaborative partners: Smart Data Foundry

This project aims to induce a sustained and high level of active academic engagement and grant applications from researchers working on areas relevant to the SDF’s thematic areas of focus and can exploit SDF’s data assets and resources. To achieve this goal, the current project will deliver a workshop and three sandpits to stimulate a progression from isolated research interests around the SDF’s area of thematic focus and data assets into multidisciplinary funding bid teams pitching translational ideas. We anticipate submitting at least two major grant bids (with a combined worth of ~£3M) over the course of the 2023/24 academic year.

Green house Pricing Index

ECFI group member participation: Prof. Gbenga Ibikunle, Dr Ben Sila

Collaborative partners: NatWest

The main goal of the Green House Price Index project is to understand the impact that climate change has on the value of a property and to be able to better understand how ongoing climate change will impact house prices in the future. Further to this, what impact does improving the energy efficiency of a home, in response to rising temperatures and energy prices have on the value of a home? We look to create a house price index that measures this change in a robust way and be used both internally within NatWest Group and externally to help improve the overall energy efficiency of NatWest Group’s current book as well as understand how the value of the book could change as result of climate-related factors, e.g. sea level rises, more intense storms in certain areas.

ESG Factors and the Expected Return

ECFI group member participation: Dr Yi Cao (PI), Dr Yizhe Dong, Dr Adam Ntakaris, Prof. Gbenga Ibikunle

Collaborative partners: Abrdn

This project embarks on an exploration of the relationship between asset returns and ESG factors, shedding light on their effectiveness in investment decision-making. We delve into three perspectives to uncover valuable insights. Firstly, we conduct an extensive examination of companies’ voluntary ESG reports using textual analysis, aiming to ascertain the extent to which these reports encompass accurate and up-to-date information regarding their ESG performance. Secondly, we assess the comprehensiveness of ESG ratings provided by data providers such as MSCI, addressing missing items by data-driven methods. Lastly, we delve into the relationship between the identified ESG information and the corresponding stock returns. To accomplish these, we leverage techniques including textual analysis, deep learning models, and statistical approaches with the objective of providing a comprehensive analysis, illuminating the association between ESG information and future asset returns. By doing so, we aim to make a significant contribution to the investment decision-making.

Transition Risk Modelling

ECFI group member participation: Prof. Luca Taschini

Collaborative partners: NatWest

The shift in the economic landscape needed to achieve net-zero targets is expected to be profound and may result in substantial expenses for unprepared industries and businesses. Undoubtedly, these expenses could have a significant impact on the cash flows and valuations of firms, potentially undermining their capacity to manage debt effectively and ultimately leading to increased default probabilities and heightened credit risks. There is already some evidence suggesting that transition risk, as quantified by a company's current carbon emissions data, has an influence on credit risk. UEBS and NWG will work together to attain insights into how the scale and pace of the economic transformation, as well as the correlated credit risk, exhibit variations across diverse sectors, regions, and throughout time.