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Machine Learning for Finance

You're reading from Machine Learning for Finance Principles and practice for financial insiders

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781789136364

Length 456 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Authors (2):

Jannes Klaas

James Le

View More author details

Table of Contents (15) Chapters

Machine Learning for Finance

Contributors

Preface

Other Books You May Enjoy

1. Neural Networks and Gradient-Based Optimization

2. Applying Machine Learning to Structured Data FREE CHAPTER

3. Utilizing Computer Vision

4. Understanding Time Series

5. Parsing Textual Data with Natural Language Processing

6. Using Generative Models

7. Reinforcement Learning for Financial Markets

8. Privacy, Debugging, and Launching Your Products

9. Fighting Bias

10. Bayesian Inference and Probabilistic Programming

Index

Chapter 4. Understanding Time Series

A time series is a form of data that has a temporal dimension and is easily the most iconic form of financial data out there. While a single stock quote is not a time series, take the quotes you get every day and line them up, and you get a much more interesting time series. Virtually all media materials related to finance sooner or later show a stock price gap; not a list of prices at a given moment, but a development of prices over time.

You'll often hear financial commenters discussing the movement of prices: "Apple Inc. is up 5%." But what does that mean? You'll hear absolute values a lot less, such as, "A share of Apple Inc. is $137.74." Again, what does that mean? This occurs because market participants are interested in how things will develop in the future and they try to extrapolate these forecasts from how things developed in the past:

Multiple time series graphs as seen on Bloomberg TV

Most forecasting that is done involves looking at past developments...

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Authors (2)

James Le

James Le

James Le is currently a master's student studying Computer Science at Rochester Institute of Technology. He is pursuing a research path in Computer Vision using Deep Learning. Moreover, he is an active freelance data scientist and data journalist, with specialties including machine/deep learning, recommendation systems, and data analysis/visualization.

See other products by James Le

Jannes Klaas

Jannes Klaas

Jannes Klaas is a quantitative researcher with a background in economics and finance. He taught machine learning for finance as lead developer for machine learning at the Turing Society, Rotterdam. He has led machine learning bootcamps and worked with financial companies on data-driven applications and trading strategies. Jannes is currently a graduate student at Oxford University with active research interests including systemic risk and large-scale automated knowledge discovery.

See other products by Jannes Klaas

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