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首页大数据和AI策略——面向投资的机器学习和另类数据方法
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May 2017
Big Data and AI Strategies
Machine Learning and Alternative Data Approach to Investing
Marko Kolanovic, PhD
(1-212) 272-1438
Global Quantitative & Derivatives Strategy
18 May 2017
marko.kolanovic@jpmorgan.com
May, 2017
Dear Investor,
Over the past few years, we have witnessed profound changes in the marketplace with participants increasingly adopting
quantitative investing techniques. These include Risk Premia investing, algorithmic trading, merging of fundamental and
quantitative investment styles, consumption of increasing amounts and differentiated types of data, and adoption of new
methods of analysis such as those based on Machine Learning and Artificial Intelligence.
In fact, over the past year, the exponential increase of the amount and types of data available to investors prompted some to
completely change their business strategy and adopt a ‘Big Data’ investment framework. Other investors may be unsure on
how to assess the relevance of Big Data and Machine Learning, how much to invest in it, and many are still paralyzed in the
face of what is also called the ‘Fourth Industrial Revolution’.
In this report we aim to provide a framework for Machine Learning and Big Data investing. This includes an
overview of types of alternative data, and Machine Learning methods to analyze them. Datasets are at the core of any
trading strategy. For this reason, we first classify and analyze the types of alternative datasets. We assess the relevance of
various datasets for different types of investors and illustrate the use of Big Data in trading strategies. Datasets covered
include data generated by individuals (e.g. social media), data generated by business processes (e.g. commercial
transactions) and data generated by machines (e.g. satellite image data). After focusing on Datasets, we explain and
evaluate different Machine Learning methods which are necessary tools to analyze Big Data. These methods include
Supervised Machine Learning: regressions, classifications; Unsupervised Machine Learning: clustering, factor analyses;
as well as methods of Deep and Reinforcement Learning. We provide theoretical, practical (e.g. codes) and investment
examples for different Machine Learning methods, and compare their relative performance. The last part of the report is a
handbook of over 500 alternative data and technology providers, which can be used as a rough roadmap to the Big Data
and Artificial Intelligence landscape.
We hope this guide will be educative for investors new to the concept of Big Data and Machine Learning, and provide new
insights and perspectives to those who already practice it.
Marko Kolanovic, PhD
Global Head of Macro Quantitative and Derivatives Strategy
J.P.Morgan Securities LLC
3
This document is being provided for the exclusive use of LOGAN SCOTT at JPMorgan Chase & Co. and clients of J.P. Morgan.
Marko Kolanovic, PhD
(1-212) 272-1438
Global Quantitative & Derivatives Strategy
18 May 2017
marko.kolanovic@jpmorgan.com
4
This document is being provided for the exclusive use of LOGAN SCOTT at JPMorgan Chase & Co. and clients of J.P. Morgan.
Marko Kolanovic, PhD
(1-212) 272-1438
Global Quantitative & Derivatives Strategy
18 May 2017
marko.kolanovic@jpmorgan.com
Table of Contents
I: INTRODUCTION AND OVERVIEW .......................................6
Summary......................................................................................................................7
Introduction to Big Data and Machine Learning .........................................................9
Classification of Alternative Data Sets ......................................................................12
Classification of Machine Learning Techniques........................................................16
Positioning within the Big Data Landscape...............................................................21
II: BIG AND ALTERNATIVE DATA ........................................25
Overview of Alternative Data....................................................................................26
Data from Individual Activity....................................................................................30
Data from Business Processes....................................................................................38
Data from Sensors......................................................................................................42
III: MACHINE LEARNING METHODS....................................51
Overview of Machine Learning Methods ..................................................................52
Supervised Learning: Regressions.............................................................................57
Supervised Learning: Classifications.........................................................................77
Unsupervised Learning: Clustering and Factor Analyses..........................................93
Deep and Reinforcement Learning ..........................................................................102
Comparison of Machine Learning Algorithms ........................................................117
IV: HANDBOOK OF ALTERNATIVE DATA .........................135
Table of contents of data providers..........................................................................136
A. Data from Individual Activity.............................................................................137
B. Data from Business Processes.............................................................................147
C. Data from Sensors...............................................................................................176
D. Data Aggregators ................................................................................................189
E. Technology Solutions..........................................................................................191
APPENDIX.............................................................................214
Techniques for Data Collection from Websites.......................................................215
Packages and Codes for Machine Learning.............................................................226
Mathematical Appendices........................................................................................231
References................................................................................................................254
Glossary ...................................................................................................................270
5
This document is being provided for the exclusive use of LOGAN SCOTT at JPMorgan Chase & Co. and clients of J.P. Morgan.
Marko Kolanovic, PhD
(1-212) 272-1438
Global Quantitative & Derivatives Strategy
18 May 2017
marko.kolanovic@jpmorgan.com
I: INTRODUCTION AND OVERVIEW
6
This document is being provided for the exclusive use of LOGAN SCOTT at JPMorgan Chase & Co. and clients of J.P. Morgan.
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