Dissecting Characteristics via Machine Learning for Stock Selection

Academic Paper, 2019

97 Pages

Abstract or Introduction

We conduct a comparative analysis of methods in the machine learning repertoire, including penalized linear models, generalized linear models, boosted regression trees, random forests, and neural networks, that investors can deploy to forecast the cross-section of stock returns.

Gaining more widespread use in economics, machine learning algorithms have demonstrated the ability to reveal complex, nonlinear patterns that are difficult or largely impossible to detect with conventional statistical methods and are often more robust to the effects of multi-collinearity among predictors. We provide new evidence that machine learning techniques can improve the economic value of cross-sectional return forecasts.

The implications of machine learning for quantitative finance are becoming both increasingly apparent and controversial. There is a growing discussion over whether machine learning tools can and should be applied to predict stock returns with greater precision. Broadly speaking, models that can be used to explain the returns of individual stocks draw on stock and firm characteristics, such as the market price of financial instruments and companies' accounting data. These characteristics can also be used to predict expected returns out-of-sample.


Dissecting Characteristics via Machine Learning for Stock Selection
Catalog Number
ISBN (eBook)
dissecting, characteristics, machine, learning, stock, selection
Quote paper
David Dümig (Author), 2019, Dissecting Characteristics via Machine Learning for Stock Selection, Munich, GRIN Verlag, https://www.grin.com/document/502999


  • No comments yet.
Read the ebook
Title: Dissecting Characteristics via Machine Learning for Stock Selection

Upload papers

Your term paper / thesis:

- Publication as eBook and book
- High royalties for the sales
- Completely free - with ISBN
- It only takes five minutes
- Every paper finds readers

Publish now - it's free