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Advances in financial machine learning / Marcos López de Prado

By: Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: Hoboken, New Jersey : Wiley, 2018Description: 1 Online-RessourceISBN:
  • 1119482119
  • 9781119482116
Subject(s): Additional physical formats: 1119482089 | 9781119482086 | Erscheint auch als: Advances in financial machine learning. Druck-Ausgabe Hoboken, New Jersey : Wiley, 2018. XIX, 366 SeitenDDC classification:
  • 332.028563
RVK: RVK: ST 302 | QK 305LOC classification:
  • Q325.5
Online resources: Summary: 3.8 The Quantamental Way3.9 Dropping Unnecessary Labels; Exercises; Bibliography; 4 Sample Weights; 4.1 Motivation; 4.2 Overlapping Outcomes; 4.3 Number of Concurrent Labels; 4.4 Average Uniqueness of a Label; 4.5 Bagging Classifiers and Uniqueness; 4.5.1 Sequential Bootstrap; 4.5.2 Implementation of Sequential Bootstrap; 4.5.3 A Numerical Example; 4.5.4 Monte Carlo Experiments; 4.6 Return Attribution; 4.7 Time Decay; 4.8 Class Weights; Exercises; References; Bibliography; 5 Fractionally Differentiated Features; 5.1 Motivation; 5.2 The Stationarity vs. Memory Dilemma; 5.3 Literature ReviewSummary: 7 Cross-Validation in Finance7.1 Motivation; 7.2 The Goal of Cross-Validation; 7.3 Why K-Fold CV Fails in Finance; 7.4 A Solution: Purged K-Fold CV; 7.4.1 Purging the Training Set; 7.4.2 Embargo; 7.4.3 The Purged K-Fold Class; 7.5 Bugs in Sklearns Cross-Validation; Exercises; Bibliography; 8 Feature Importance; 8.1 Motivation; 8.2 The Importance of Feature Importance; 8.3 Feature Importance with Substitution Effects; 8.3.1 Mean Decrease Impurity; 8.3.2 Mean Decrease Accuracy; 8.4 Feature Importance without Substitution Effects; 8.4.1 Single Feature Importance; 8.4.2 Orthogonal FeaturesSummary: 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 financePPN: PPN: 1018804366Package identifier: Produktsigel: ZDB-4-NLEBK
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