| 000 | 00000nam u2200205 a 4500 | |
| 001 | 000045941431 | |
| 005 | 20180515104211 | |
| 008 | 180514s2018 nju 000 0 eng d | |
| 010 | ▼a 2017049249 | |
| 020 | ▼a 9781119482086 (hardback) | |
| 035 | ▼a (KERIS)REF000018606925 | |
| 040 | ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d 211009 | |
| 050 | 0 0 | ▼a HG104 ▼b .L67 2018 |
| 082 | 0 0 | ▼a 332.0285/631 ▼2 23 |
| 084 | ▼a 332.0285 ▼2 DDCK | |
| 090 | ▼a 332.0285 ▼b L864a | |
| 100 | 1 | ▼a López de Prado, Marcos Mailoc. |
| 245 | 1 0 | ▼a Advances in financial machine learning / ▼c Marcos López de Prado. |
| 260 | ▼a New Jersey : ▼b Wiley, ▼c c2018. | |
| 300 | ▼a xxi, 366 p. ; ▼c 24 cm. | |
| 500 | ▼a Includes index. | |
| 505 | 8 | ▼a Machine generated contents note: About the Author Preamble 1. Financial Machine Learning as a Distinct Subject Part 1: Data Analyssis 2. Financial Data Structures 3. Labeling 4. Sample Weights 5. Fractionally Differentiated Features Part 2: Modelling 6. Ensemble Methods 7. Cross-validation in Finance 8. Feature Importance 9. Hyper-parameter Tuning with Cross-Validation Part 3: Backtesting 10. Bet Sizing 11. The Dangers of Backtesting 12. Backtesting through Cross-Validation 13. Backtesting on Synthetic Data 14. Backtest Statistics 15. Understanding Strategy Risk 16. Machine Learning Asset Allocation Part 4: Useful Financial Features 17. Structural Breaks 18. Entropy Features 19. Microstructural Features Part 5: High-Performance Computing Recipes 20. Multiprocessing and Vectorization 21. Brute Force and Quantum Computers 22. High-Performance Computational Intelligence and Forecasting Technologies Dr. Kesheng Wu and Dr. Horst Simon Index. |
| 520 | ▼a "Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance"-- ▼c Provided by publisher. | |
| 520 | ▼a "This book begins by structuring financial data in a way that is amenable to machine learning (ML) algorithms. Then, the author discusses how to conduct research with ML algorithms on that data and how to backtest your discoveries. Most of the problems and solutions are explained using math, supported by code. This makes the book very practical and hands-on. Readers become active users who can test the solutions proposed in their work. Readers will learn how to structure, label, weight, and backtest data. Machine learning is the future, and this book will equip investment professionals with the tools to utilize it moving forward"-- ▼c Provided by publisher. | |
| 650 | 0 | ▼a Finance ▼x Data processing. |
| 650 | 0 | ▼a Finance ▼x Mathematical models. |
| 650 | 0 | ▼a Machine learning. |
| 945 | ▼a KLPA |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Science & Engineering Library/Sci-Info(Stacks2)/ | Call Number 332.0285 L864a | Accession No. 121244511 | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Author Introduction
마르코스 로페즈 데 프라도(지은이)
머신러닝과 슈퍼컴퓨팅을 이용해 수십억 달러의 기금을 운용하고 있다. 구겐하임 파트너의 정량 금융 투자 전략(QIS, Quantitative Investment Strategies) 사업을 설립해 뛰어난 리스크-조정 수익률을 지속적으로 달성한 고용량 전략을 개발했다. 130억 달러의 자산을 운용한 후 QIS를 인수하고 2018년 구겐하임에서 스핀아웃(spin out)했다. 2010년부터 로렌스 버클리 국립 연구소(Lawrence Berkeley National Laboratory, 미국 에너지부, 과학국)의 연구원으로 일하고 있다. 금융에서 가장 많이 읽힌 10대 도서의 저자(SSRN 순위 기준)로, 머신러닝과 슈퍼컴퓨팅에 관련된 수십 편의 논문을 썼고, 알고리즘 거래에 대한 다수의 국제 특허를 갖고 있다. 1999년에 스페인 국립 학문상을 수상했고, 2003년에는 금융 경제학으로 박사학위를 받았으며, 2011년에는 마드리드 대학교에서 수학 금융으로 두 번째 박사학위를 받았다. 박사 후 과정을 하버드와 코넬 대학교에서 마쳤으며, 공학부에서 금융 머신러닝 과정을 가르쳤다. 미국 수학 학회에 따른 에르도스 #2Erdos #2와 아인슈타인 #4Einstein #4를 갖고 있다.
Table of Contents
Preamble
Foreword by Paul Tudor Jones, II,. Tudor Investment Corporation.
Foreword by Professor. Horst Simon,. Lawrence Berkeley National Laboratory.
1. Financial ML as a distinct subject.
Section 1: Data Analysis
2. Financial data structures.
3. Labeling.
4. Sample weights.
5. Fractional differentiation.
CE: Continue deleting punctuation from TOC
Section 2: Modelling
6. Ensemble methods.
7. Cross validation in finance.
8. Feature importance.
9. Hyper-parameter tuning with CV.
Section 3: Backtesting
10. Bet sizing.
11. The dangers of backtesting.
12. Backtesting through CV.
13. Backtesting on synthetic data.
14. Backtest statistics.
15. Understanding strategy risk.
16. ML asset allocation.
Section 4: Useful Financial Features
17. Structural breaks.
18. Entropy features.
19. Microstructural features.
Section 5: HPC Recipes
20. Multiprocessing and vectorization.
21. Brute force and quantum computers.
Epilogue
22. Partnering with Berkeley Lab (by Dr. John Wu).
23. ML at University of California, Berkeley (by TBD).
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