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Machine Learning and the Optimal Choice of Asset Pricing Model

Bielinski, Aleksadner; Broby, Daniel

Authors

Daniel Broby



Contributors

Syed Hasan Jafar
Editor

Hemachandran K
Editor

Hani El-Chaarani
Editor

Sairam Moturi
Editor

Neha Gupta
Editor

Abstract

This chapter evaluates the traditional methods for price prediction and examines what we believe are the most promising machine learning techniques for that task. Asset price forecasting is one of the fundamental problems in the financial field. Traditional forecasting methods include Capital Asset Pricing Model (CAPM) or Factor Models to estimate stocks’ excess returns. More recently, an increasing number of researchers and financial practitioners began to explore the role of machine learning in asset pricing. We show how these methods have been already applied in practice and discuss their results. We also explore the potential use of neural networks in asset pricing as we believe that their capacity to process large amounts of data together with the ability to accurately capture non-linear relationships among the variables makes them a great tool for price prediction.

Acceptance Date Jun 27, 2022
Online Publication Date May 15, 2023
Publication Date 2023
Deposit Date Jun 30, 2022
Publicly Available Date May 16, 2025
Publisher Taylor & Francis
Pages 91-127
Edition 1st
Book Title Artificial Intelligence for Capital Markets
Chapter Number 7
ISBN 9781032353937
DOI https://doi.org/10.1201/9781003327745-7
Public URL http://researchrepository.napier.ac.uk/Output/2883653

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