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عنوان
Big Data and Machine Learning in Quantitative Investment

پدید آورنده

موضوع
Big data.,Investments-- Study and teaching.,Machine learning.,BUSINESS & ECONOMICS-- Finance-- General.

رده
HG4521
.
G853
2019

کتابخانه
Center and Library of Islamic Studies in European Languages

محل استقرار
استان: Qom ـ شهر: Qom

Center and Library of Islamic Studies in European Languages

تماس با کتابخانه : 32910706-025

INTERNATIONAL STANDARD BOOK NUMBER

(Number (ISBN
1119522080
(Number (ISBN
1119522218
(Number (ISBN
9781119522089
(Number (ISBN
9781119522218
Erroneous ISBN
1119522196
Erroneous ISBN
9781119522195

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Big Data and Machine Learning in Quantitative Investment
General Material Designation
[Book]

.PUBLICATION, DISTRIBUTION, ETC

Place of Publication, Distribution, etc.
Newark :
Name of Publisher, Distributor, etc.
John Wiley & Sons, Incorporated,
Date of Publication, Distribution, etc.
2018.

PHYSICAL DESCRIPTION

Specific Material Designation and Extent of Item
1 online resource (299 pages)

GENERAL NOTES

Text of Note
Chapter 5 Using Alternative and Big Data to Trade Macro Assets

INTERNAL BIBLIOGRAPHIES/INDEXES NOTE

Text of Note
ReferencesChapter 4 Implementing Alternative Data in an Investment Process; 4.1 Introduction; 4.2 The Quake: Motivating the Search for Alternative Data; 4.2.1 What happened?; 4.2.2 The next quake?; 4.3 Taking Advantage of the Alternative Data Explosion; 4.4 Selecting A Data Source for evaluation; 4.5 Techniques for Evaluation; 4.6 Alternative Data for Fundamental Managers; 4.7 Some Examples; 4.7.1 Example 1: Blogger sentiment; 4.7.2 Example 2: Online consumer demand; 4.7.3 Example 3: Transactional data; 4.7.4 Example 4: ESG; 4.8 Conclusions; References

CONTENTS NOTE

Text of Note
Cover; Title Page; Copyright; Contents; Chapter 1 Do Algorithms Dream About Artificial Alphas?; 1.1 Introduction; 1.2 Replication or Reinvention; 1.3 Reinvention with Machine Learning; 1.4 A Matter of Trust; 1.5 Economic Existentialism: A Grand Design or an Accident?; 1.6 What is this System Anyway?; 1.7 Dynamic Forecasting and New Methodologies; 1.8 Fundamental Factors, Forecasting and Machine Learning; 1.9 Conclusion: Looking for Nails; Chapter 2 Taming Big Data; 2.1 Introduction: Alternative Data -- an Overview; 2.1.1 Definition: Why 'alternative'? Opposition with conventional
Text of Note
2.1.2 Alternative is not always big and big is not always alternative2.2 Drivers of Adoption; 2.2.1 Diffusion of innovations: Where are we now?; 2.3 Alternative Data Types, Formats and Universe; 2.3.1 Alternative data categorization and definitions; 2.3.2 How many alternative datasets are there?; 2.4 How to Know What Alternative Data is Useful (And What isn't); 2.5 How Much Does Alternative Data Cost?; 2.6 Case Studies; 2.6.1 US medical records; 2.6.2 Indian power generation data; 2.6.3 US earnings performance forecasts; 2.6.4 China manufacturing data; 2.6.5 Short position data
Text of Note
2.6.6 The collapse of carillion -- a use case example for alt data2.7 The Biggest Alternative Data Trends; 2.7.1 Is alternative data for equities only?; 2.7.2 Supply-Side: Dataset Launches; 2.7.3 Most common queries; 2.8 Conclusion; Reference; Chapter 3 State of Machine Learning Applications in Investment Management; 3.1 Introduction; 3.2 Data, Data, Data Everywhere; 3.3 Spectrum of Artificial Intelligence Applications; 3.3.1 AI applications classification; 3.3.2 Financial analyst or competitive data scientist?; 3.3.3 Investment process change: An 'Autonomous Trading' case
Text of Note
3.3.4 Artificial intelligence and strategies development3.4 Interconnectedness of Industries and Enablers of Artificial Intelligence; 3.4.1 Investments in development of AI; 3.4.2 Hardware and software development; 3.4.3 Regulation; 3.4.4 Internet of things; 3.4.5 Drones; 3.4.6 Digital transformation in steps -- case study; 3.5 Scenarios for Industry Developments; 3.5.1 Lessons from autonomous driving technology; 3.5.2 New technologies -- new threats; 3.5.3 Place for discretionary management; 3.6 For the Future; 3.6.1 Changing economic relationships; 3.6.2 Future education focus; 3.7 Conclusion
0
8
8
8

SUMMARY OR ABSTRACT

Text of Note
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.

ACQUISITION INFORMATION NOTE

Source for Acquisition/Subscription Address
Wiley
Stock Number
9781119522218

OTHER EDITION IN ANOTHER MEDIUM

Title
Big Data and Machine Learning in Quantitative Investment.
International Standard Book Number
9781119522195

TOPICAL NAME USED AS SUBJECT

Big data.
Investments-- Study and teaching.
Machine learning.
BUSINESS & ECONOMICS-- Finance-- General.

DEWEY DECIMAL CLASSIFICATION

Number
332
.
60285631
Edition
23

LIBRARY OF CONGRESS CLASSIFICATION

Class number
HG4521
Book number
.
G853
2019

PERSONAL NAME - PRIMARY RESPONSIBILITY

Guida, Tony.

ORIGINATING SOURCE

Date of Transaction
20200822154529.0
Cataloguing Rules (Descriptive Conventions))
pn

ELECTRONIC LOCATION AND ACCESS

Electronic name
 مطالعه متن کتاب 

[Book]

Y

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