Machine Learning for Asset Management

New Developments and Financial Applications

Machine Learning for Asset Management

Edited by

Emmanuel Jurczenko, Glion Institute of Higher Education, Switzerland

ISBN : 9781786305442

Publication Date : September 2020

Hardcover 460 pp

165.00 USD



This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management.

The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing.

Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.


1. Time-series and Cross-sectional Stock Return Forecasting: New Machine Learning Methods, David E. Rapach and Guofu Zhou.
2. In Search of Return Predictability: Application of Machine Learning Algorithms in Tactical Allocation, Kris Boudt, Muzafer Cela and Majeed Simaan.
3. Sparse Predictive Regressions: Statistical Performance and Economic Significance, Daniele Bianchi and Andrea Tamoni.
4. The Artificial Intelligence Approach to Picking Stocks, Riccardo Borghi and Giuliano De Rossi.
5. Enhancing Alpha Signals from Trade Ideas Data Using Supervised Learning, Georgios V. Papaioannou and Daniel Giamouridis.
6. Natural Language Process and Machine Learning in Global Stock Selection, Yin Luo.
7. Forecasting Beta Using Machine Learning and Equity Sentiment Variables, Alexei Jourovski, Vladyslav Dubikovskyy, Pere Adell, Ravi Ramakrishnan and Robert Kosowski.
8. Machine Learning Optimization Algorithms & Portfolio Allocation, Sarah Perrin and Thierry Roncalli.
9. Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-asset Multi-factor Allocations, Harald Lohre, Carsten Rother and Kilian Axel Schäefer.
10. Portfolio Performance Attribution: A Machine Learning-Based Approach, Ryan Brown, Harindra De Silva and Patrick D. Neal.
11. Modeling Transaction Costs When Trades May Be Crowded: A Bayesian Network Using Partially Observable Orders Imbalance, Marie Brière Charles-Albert Lehalle, Tamara Nefedova and Amine Raboun.

About the authors

Emmanuel Jurczenko is Director of Graduate Studies and Professor of Finance at Glion Institute of Higher Education, Switzerland. Prior to this, he spent 13 years as Associate Professor of Finance at ESCP-Europe and worked for ABN-AMRO as Head of Quantitative Analysts where he was in charge of quantitative fund selection. His research focuses on portfolio construction in particular on risk budgeting, factor investing and machine learning estimation techniques.