Machine learning models are increasingly used in a wide variety of financial settings. The difficulty of understanding the inner workings of these systems, combined with their wide applicability, has the potential to lead to significant new risks for users; these risks need to be understood and quantified. In this sub-chapter, we will focus on a well studied application of machine learning techniques, to pricing and hedging of financial options. Our aim will be to highlight the various sources of risk that the introduction of machine learning emphasises or de-emphasises, and the possible risk mitigation and management strategies that are available
These slides present selected challenges that arise in the financial industry and how these challeng...
This master's thesis is divided into three parts. In the first part I described P2P lending, its cha...
Machine learning's prowess for automatic pattern recognition at scale is meaningfully reshaping ever...
This book introduces machine learning in finance and illustrates how we can use computational tools ...
For decades, there have been developments of computer software to support human decision making. Alo...
It is now widely acknowledged that machine learning plays an essential role in a variety of financia...
There is an increasing influence of machine learning in business applications, with many solutions a...
This thesis applies machine learning (ML) techniques to re-evaluate longstanding problems in financi...
This article focuses on supervised learning and reinforcement learning. These areas overlap most wit...
Machine Learning (ML) for finance is a fruitful approach to detect patterns in data. However, when i...
This study investigates how modern machine learning (ML) techniques can be used to advance the field...
The recent fast development of machine learning provides new tools to solve challenges in many areas...
We perform a comparative analysis of machine learning methods for the canonical problemof empirical ...
Financial researchers, who often work with large volumes of financial data, need efficient tools to ...
Machine learning methods penetrate to applications in the analysis of financial data, particularly t...
These slides present selected challenges that arise in the financial industry and how these challeng...
This master's thesis is divided into three parts. In the first part I described P2P lending, its cha...
Machine learning's prowess for automatic pattern recognition at scale is meaningfully reshaping ever...
This book introduces machine learning in finance and illustrates how we can use computational tools ...
For decades, there have been developments of computer software to support human decision making. Alo...
It is now widely acknowledged that machine learning plays an essential role in a variety of financia...
There is an increasing influence of machine learning in business applications, with many solutions a...
This thesis applies machine learning (ML) techniques to re-evaluate longstanding problems in financi...
This article focuses on supervised learning and reinforcement learning. These areas overlap most wit...
Machine Learning (ML) for finance is a fruitful approach to detect patterns in data. However, when i...
This study investigates how modern machine learning (ML) techniques can be used to advance the field...
The recent fast development of machine learning provides new tools to solve challenges in many areas...
We perform a comparative analysis of machine learning methods for the canonical problemof empirical ...
Financial researchers, who often work with large volumes of financial data, need efficient tools to ...
Machine learning methods penetrate to applications in the analysis of financial data, particularly t...
These slides present selected challenges that arise in the financial industry and how these challeng...
This master's thesis is divided into three parts. In the first part I described P2P lending, its cha...
Machine learning's prowess for automatic pattern recognition at scale is meaningfully reshaping ever...