Abstract: Economic forecasting has benefited from long established and well-tested methods and techniques. However, the toolkit that ML&AI offers can greatly augment our understanding of the field and improve forecasts. In fact, the potential of 'alternative data' is finally unlocked in our age thanks to the ability of ML&AI to extract novel features - something impossible a decade ago. This talk will focus on forecasting inflation with ML and 'alternative data'. It will show the steps of building such models, the improvements over the traditional econometric models, and will describe the many hurdles a practical implementation of such an approach entails.
Bio: Alexander is a Quant & Data Scientist with 20 years of accumulated experience both in specialist and leadership positions in global financial institutions.. Mastering the main AI/ML techniques, he is also strictly specialized and personally contributed to the field of Probabilistic Graphical Models, Causal AI and Alternative Data. Alexander has authored/co-authored 10+ papers and 3 books on these topics. He holds a degree in Mathematical Finance from University of Oxford where he is a Visiting Lecturer on Bayesian Risk Management and Alternative Data. Currently he is CEO of Turnleaf Analytics.