This book is intended to cover some advanced modelling techniques applied to equity investment strategies that are built on firm characteristics . The content is threefold. First, we try to simply explain the ideas behind most mainstream machine learning algorithms that are used in equity asset allocation. Second, we mention a wide range of academic references for the readers who wish to push a little further. Finally, we provide hands-on R code samples that show how to apply the concepts and tools on a rea
The printed R version can be bought on the publisher's website or on Amazon (US) , on Amazon (France) and Amazon (UK) .
This book deals with machine learning (ML) tools and their applications in factor investing. Factor investing is a subfield of a large discipline that encompasses asset allocation, quantitative trading and wealth management. Its premise is that differences in the returns of firms can be explained by the characteristics of these firms. Thus, it departs from traditional analyses which rely on price and volume data only, like classical portfolio theory à la Markowitz ( 1952 ) , or high frequency trading. For a