We study the impacts of business cycles on machine learning (ML) predictions. Using the S&P 500 index, we find that ML models perform worse during most recessions, and the inclusion of recession history or the risk-free rate does not necessarily improve their performance. Investigating...

Artificial intelligence (AI) can revolutionise healthcare but it may also constitute a new paradigm for medical law. This dissertation will focus on medical AI (mAI) that is used for providing personalised treatment recommendations. This dissertation carries four aims: First, to introduce the...

In this study, we explore the impact of COVID-19 pandemic on the default risk of loan portfolios of the Greek banking system, using cutting edge machine learning technologies, like deep learning. Our analysis is based on loan level monthly data, spanning a 42-month period, collected through the...

The FinTech industry, synonymous with the financial technology sector, is undergoing constant evolution and has gained immense popularity due to technological advancements and the widespread adoption of digital tools. This revolution is primarily driven by the convergence of technologies such as...

Accurately modelling and forecasting electricity consumption is a key prerequisite for strategic sustainable energy planning and development. In this study, we use four advanced econometrics time series models and four machine learning (ML) and deep learning models including an AR with...

Supply Chain Financing (SCF) is increasingly utilised as an effective method for optimising cash flows in supply networks. With increased popularity several financial institutions have begun offering SCF to businesses. However various recent scandals have highlighted inefficiencies in the...

Brain-computer interfaces (BCIs) are devices that enable communication and control of external devices by capturing and interpreting neural signals from the brain. Artificial intelligence (AI) has the potential to improve the accuracy and speed of BCIs, thereby enhancing their effectiveness and...

This study extends previous work applying unsupervised machine learning to commodity markets. "Clustering Commodity Markets in Space and Time" [DOI: 10.1016/j.resourpol.2021.102162] examined returns and volatility in commodity markets. That paper supported the conventional ontology of commodity...

In this work we elucidate international trends in the field of quantum technology (QT) by analyzing a global patent database built from an operational definition of QT that was generated through the curated application of artificial intelligence (AI). In doing so we demonstrate how the...

This paper empirically examines the relationship between CO 2 emissions and corporate performance in terms of long-term performance, short-term performance, and cost of capital, using available firm-level data in the First Section of the Tokyo Stock Exchange from FY2011 to FY2019. To address...