£63.86

Springer Machine Learning in Finance - Theory to Practice Book

Price data updated today

View at Amazon

We'll watch every seller, every day. One email when your price arrives.

This is the usual price. Wait for it to drop, or tell us your number.

£64 today · usual range £58–£74 · best ever £58

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey
↓ Forecast
where the price is heading next
↓ Statistics
all-time high & low, recent range
↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 91 days · 91 data points

Historical
Generating forecast…
£74.14 £56.30 £60.19 £64.08 £67.98 £71.87 £75.76 22 April 2026 14 May 2026 06 June 2026 28 June 2026 21 July 2026

Price Distribution

Price distribution over 91 days • 5 price ranges

Days at Price
Current Price
11 days 11 days · current 42 days 26 days 1 day 0 11 21 32 42 £58-61 £61-64 £64-68 £68-71 £71-74 Days at Price

Price Analysis

Most common range: £64-68 (42 days, 46.2%)

Price range: £58 - £74

Price levels: 5 price ranges over 91 days

Description

Master the integration of machine learning and quantitative finance with this comprehensive guide from Springer. As the finance industry shifts toward larger datasets and increased computational power, machine learning has become an essential skillset for professionals. This book provides a unified treatment of machine learning alongside vital statistical and computational disciplines. Readers will explore the connection between financial econometrics and discrete time stochastic control. The text focuses on how theory and hypothesis tests guide the selection of specific algorithms for financial data modeling and decision making. Designed specifically for advanced graduate students, this resource bridges the gap between complex mathematical theory and practical application in real-world financial environments.

Key Features

Unified approach to machine learning and quantitative finance disciplines like financial econometrics.

Guidance on using theory and hypothesis tests to select the right algorithms for financial data modeling.

Covers discrete time stochastic control to support advanced computational decision making.

Designed for advanced graduate students seeking to master modern financial datasets.

Addresses the growing industry demand for machine learning skills in the finance sector.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
02 July 2020
Listed Since
14 January 2020

Barcode

No barcode data available