Franklin

Big data and machine learning in quantitative investment / Tony Guida.

Author/Creator:
Guida, Tony, 1979- author.
Publication:
Chichester : Wiley, [2019]
Format/Description:
Book
1 online resource (299 pages).
Edition:
1st edition
Series:
Wiley finance series.
Wiley finance
Status/Location:
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Details

Subjects:
Investments -- Study and teaching.
Machine learning.
Big data.
System Details:
text file
Summary:
Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. •    Gain a solid reason to use machine learning •    Frame your question using financial markets laws •    Know your data •    Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.
Contents:
Machine generated contents note: Chapter 1: Do algorithms dream about artificial alphas? Chapter 2: Taming Big data Chapter 3: State of machine learning applications in investment management Chapter 4: Implementing alternative data in an investment Process Chapter 5: Using alternative and Big Data to trade macro assets Chapter 6: Big is beautiful: How email receipt data can help predict company sales Chapter 7: Ensemble learning applied to quant equity: gradient boosting in a multi-factor framework Chapter 8: A social media analysis of corporate culture Chapter 9: Machine Learning & Event Detection for Trading Energy Futures Chapter 10: Natural language processing of financial news Chapter 11: Support-Vector-Machine Based Global Tactical Asset Allocation Chapter 12: Reinforcement learning in finance Chapter 13: Deep learning in Finance: Prediction of stock returns with long short term memory networks Biography of contributors.
Notes:
Includes bibliographical references and index.
Description based on print version record.
ISBN:
1-119-52222-6
1-119-52208-0
OCLC:
1080079483