This study aims to examine the challenges and applications of machine learning for financial research. Machine learning algorithms have been developed for certain data environments which substantially differ from the one we encounter in finance. Not only do difficulties arise due to some of the idiosyncrasies of financial markets, there is a fundamental tension between the underlying paradigm of machine learning and the research philosophy in financial economics. Given the peculiar features of financial markets and the empirical framework within social science, various adjustments have to be made to the conventional machine learning methodology. We discuss some of the main challenges of machine learning in finance and examine how these could be accounted for.

Throughout the past year, the use of artificial intelligence (AI) and other forms of technology within the financial services industry has continued apace. This will increase further as it dovetails with enriched natural language processing (NLP) through 2020 and into the coming decade and lead to more personalisation of services. Indeed, as noted in Crowdfund Insider, the European Union is to invest €100 million in artificial intelligence and blockchain start-ups next year, to boost the EU-wide innovation ecosystem. Globally, the Fintech revolution offers solutions to all manner of issues, and as we see in so many sectors, algorithms and AI can locate data and highlight trends. In doing so, such technologies operate automatically – and therefore can carry out functions much quicker than by human effort – and at a reduced financial cost as technology mitigates the need for large data-crunching teams.

JP Morgan, the world's sixth-largest bank, has reportedly acquired an undisclosed stake in Limeglass, the developer of a set of tools that eliminate information overload in order to help people focus on the "relevant paragraphs" of their financial market research. As one of JP Morgan's in-residence startup graduates, Limeglass' technology has been developed to automatically analyze the important information in research documents in real-time. The analysis tools take into consideration the context and structure of research papers. The firm's analysis tools use proprietary artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) to "smart-tag" individual paragraphs in context. This allows financial institutions to personalize their research for internal and external users.

US investment bank JP Morgan has made a strategic investment in FinTech firm Limeglass, which deploys artificial intelligence (AI), machine learning and natural language processing (NLP) to analyse institutional research. Limeglass stated that it recently completed JP Morgan's In-Residence Program, after being accepted into the scheme for FinTech start-ups in February 2018. The program aims to help emerging technology companies develop products for banking. "The insights our research teams produce daily are a huge source of value to our clients. We are continuously investing in technology to help deliver industry-leading content and to help us and our clients further mine that value," said Hussein Malik, head of transformation and implementation for sales and research at JP Morgan.