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2020 | OriginalPaper | Chapter

1. Introduction

Authors : Matthew F. Dixon, Igor Halperin, Paul Bilokon

Published in: Machine Learning in Finance

Publisher: Springer International Publishing

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Abstract

This chapter introduces the industry context for machine learning in finance, discussing the critical events that have shaped the finance industry’s need for machine learning and the unique barriers to adoption. The finance industry has adopted machine learning to varying degrees of sophistication. How it has been adopted is heavily fragmented by the academic disciplines underpinning the applications. We view some key mathematical examples that demonstrate the nature of machine learning and how it is used in practice, with the focus on building intuition for more technical expositions in later chapters. In particular, we begin to address many finance practitioner’s concerns that neural networks are a “black-box” by showing how they are related to existing well-established techniques such as linear regression, logistic regression, and autoregressive time series models. Such arguments are developed further in later chapters. This chapter also introduces reinforcement learning for finance and is followed by more in-depth case studies highlighting the design concepts and practical challenges of applying machine learning in practice.

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next chapter Probabilistic Modeling
Appendix
Available only for authorised users
Footnotes
1
The model is referred to as non-parametric if the parameter space is infinite dimensional and parametric if the parameter space is finite dimensional.
 
3
C. Shannon, A Mathematical Theory of Communication, The Bell System Technical Journal, Vol. 27, pp. 379-423, 623-656, July, October, 1948.
 
4
Note that we do not treat the input as a layer. So there are L − 1 hidden layers and an output layer.
 
5
While the functional form of the map is the same as linear regression, neural networks do not assume a data generation process and hence inference is not identical to ordinary least squares regression.
 
6
A wealth process is self-financing if, at each time step, any purchase of an additional quantity of the risky asset is funded from the bank account. Vice versa, any proceeds from a sale of some quantity of the asset go to the bank account.
 
7
Note, for avoidance of doubt, that the risk-aversion parameter must be scaled by a factor of \(\frac {1}{2}\) to ensure consistency with the finance literature.
 
8
The question of how much data is needed to train a neural network is a central one, with the immediate concern being insufficient data to avoid over-fitting. The amount of data needed is complex to assess; however, it is partly dependent on the number of edges in the network and can be assessed through bias–variance analysis, as described in Chap. D.
 
9
Note that the composition of the S&P 500 changes over time and so we should interpret a feature as a fixed symbol.
 
10
The strategy refers the choice of weight if Player 2 is to choose a payoff V = wV 1 + (1 − w)V 2, i.e. a weighted combination of payoffs V 1 and V 2.
 
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Metadata
Title
Introduction
Authors
Matthew F. Dixon
Igor Halperin
Paul Bilokon
Copyright Year
2020
Book
Machine Learning in Finance

Print ISBN: 978-3-030-41067-4

Electronic ISBN: 978-3-030-41068-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-41068-1_1