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A machine learning approach to portfolio pricing and risk management for high-dimensional problems

Publications associées (45)

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Portfolio Construction with Hierarchical Momentum

Urban Ulrych

This article presents a portfolio construction approach that combines the hierarchical clustering of a large asset universe with the stock price momentum. On one hand, investing in high-momentum stocks enhances returns by capturing the momentum premium. On ...

Pageant Media Ltd2024

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Alfio Quarteroni, Francesco Regazzoni, Stefano Pagani

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equ ...

Nature Portfolio2024

Demand-based Asset Pricing: Theory, Estimation and Applications

Philippe van der Beck

This thesis investigates the relationship between investors' demand shocks and asset pricesthrough the use of data on portfolio holdings. In three chapters, I study the theory, estimation,and application of demand-based asset pricing models, which incorpor ...

EPFL2023

Asset Pricing and Monetary Policy

Nicolas Gauderon

Classical theory asserts that the formation of prices is the result of aggregated decisions ofeconomics agent such as households or corporation. However central banks are very importantagents that have often been neglected in asset pricing models. Central ...

EPFL2022

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

Andreas Loukas, Nikolaos Karalias

Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects. But, discrete domains are (I) not naturally amenable to gradient-based optimization, and (II) incompatible with deep lear ...

2022

Principal Portfolios

Semyon Malamud

We propose a new asset pricing framework in which all securities' signals predict each individual return. While the literature focuses on securities' own-signal predictability, assuming equal strength across securities, our framework includes cross-predict ...

WILEY2022

Information-driven transitions in projections of underdamped dynamics

Daniel Maria Busiello, Giorgio Nicoletti, Amos Maritan

Low-dimensional representations of underdamped systems often provide useful insights and analytical tractability. Here, we build such representations via information projections, obtaining an optimal model that captures the most information on observed spa ...

AMER PHYSICAL SOC2022

COVID-19: data-driven dynamic asset allocation in times of pandemic

Anna Timonina-Farkas

The COVID-19 pandemic has demonstrated the importance and value of multi-period asset allocation strategies responding to rapid changes in market behavior. In this article, we formulate and solve a multi-stage stochastic optimization problem, choosing the ...

AMER INST MATHEMATICAL SCIENCES-AIMS2021

Chemiscope: interactive structure-property explorer for materials and molecules

Michele Ceriotti, Guillaume André Jean Fraux

The number of materials or molecules that can be created by combining different chemical elements in various proportions and spatial arrangements is enormous. Computational chemistry can be used to generate databases containing billions of potential struct ...

2020

Non-myopic betas

Semyon Malamud

An overlapping generations model with investors having heterogeneous investment horizons leads to a two-factor asset pricing model. The risk premiums are determined by the exposure to the market (myopic betas) and the future return on the efficient portfol ...

2018