Abstract: 

My presentation will cover applications of ML in various areas of interest to investment management professionals and investment bankers. From portfolio construction and risk management to trade idea selection and bond price prediction. The objective of the presentation is to discuss the methodologies that we found more compelling and applicable, the challenges, as well as the tangible, actionable outcomes. What we learned and what we can possibly do better.

Bio: 

Daniel Giamouridis is the Global Head of Scientific Implementation (Scientific Implementation, SI), Data and Innovation Group, at Bank of America Merrill Lynch. He heads a Team of PhD-trained scientists, delivering quantitative client solutions and data driven products. In addition, the SI Team publishes quantitative thematic pieces, interacts with academia and facilitates interaction between academia and practice for idea generation/exchange and talent sourcing.
Daniel joined Bank of America Merrill Lynch in January 2016. Prior to joining Bank of America Merrill Lynch Daniel was an Associate Professor of Finance at the Athens University of Economics and Business and had worked closely for over 10 years with institutional investors, investments banks and asset management organizations globally in areas covering quantitative equity research, factor investing and hedge fund replication, pension asset management, and derivatives valuation. Daniel’s research has appeared in leading academic and practitioner journals and has received grants from professional and academic organizations.
Daniel holds a PhD in Finance from Cass Business School and a MEng in Mechanical Engineering from NTUA. He is currently affiliated as a Visiting/Associate member of staff with Cass Business School (City University) and EDHEC-Risk Institute (EDHEC Business School). Daniel is a member of the Governing Board of the Institute for Quantitative Investment Research (INQUIRE) UK and also a Co-Editor of the Financial Analysts Journal, the Journal of the CFA Institute.