MARC Record from marc_columbia

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001 13632362
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008 180224s2018 njuab ob 001 0 eng
010 $a 2018009027
035 $a(OCoLC)on1021096780
035 $a(NNC)13632362
040 $aDLC$beng$erda$epn$cDLC$dN$T$dEBLCP$dRECBK$dYDX$dOCLCO$dYDX$dOCLCF$dOCLCO$dOCLCQ$dIDB$dTEU$dCOO$dTEFOD$dNRC$dCOF$dYOU$dOCLCQ$dUAB$dWYU$dLVT$dZCU$dK6U$dD6H$dUKAHL$dUKMGB$dYT1$dOCLCO
015 $aGBB810928$2bnb
016 7 $a018674674$2Uk
019 $a1020790036$a1021299458$a1136610024
020 $a9781119482116$q(electronic bk.)
020 $a1119482119$q(electronic bk.)
020 $a9781119482109$q(electronic bk.)
020 $a1119482100$q(electronic bk.)
020 $z9781119482086$q(hardcover)
020 $z1119482089$q(hardcover)
035 $a(OCoLC)1021096780$z(OCoLC)1020790036$z(OCoLC)1021299458$z(OCoLC)1136610024
037 $a9177A981-A064-432A-968E-EBE7AC756451$bOverDrive, Inc.$nhttp://www.overdrive.com
042 $apcc
050 14 $aHG104$b.L67 2018
072 7 $aBUS$x027000$2bisacsh
082 00 $a332.0285/631$223
084 $aBUS036000$2bisacsh
049 $aZCUA
100 1 $aLópez de Prado, Marcos Mailoc,$eauthor.
245 10 $aAdvances in financial machine learning /$cMarcos López de Prado.
264 1 $aHoboken, New Jersey :$bJohn Wiley & Sons, Inc.,$c[2018]
300 $a1 online resource (xxi, 366 pages)
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
520 $a"Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance"--$cProvided by publisher.
520 $a"This book begins by structuring financial data in a way that is amenable to machine learning (ML) algorithms. Then, the author discusses how to conduct research with ML algorithms on that data and how to backtest your discoveries. Most of the problems and solutions are explained using math, supported by code. This makes the book very practical and hands-on. Readers become active users who can test the solutions proposed in their work. Readers will learn how to structure, label, weight, and backtest data. Machine learning is the future, and this book will equip investment professionals with the tools to utilize it moving forward"--$cProvided by publisher.
504 $aIncludes bibliographical references and index.
505 0 $aIntro; Advances in Financial Machine Learning; Contents; About the Author; Preamble; 1 Financial Machine Learning as a Distinct Subject; 1.1 Motivation; 1.2 The Main Reason Financial Machine Learning Projects Usually Fail; 1.2.1 The Sisyphus Paradigm; 1.2.2 The Meta-Strategy Paradigm; 1.3 Book Structure; 1.3.1 Structure by Production Chain; 1.3.2 Structure by Strategy Component; 1.3.3 Structure by Common Pitfall; 1.4 Target Audience; 1.5 Requisites; 1.6 FAQs; 1.7 Acknowledgments; Exercises; References; Bibliography; PART 1 Data Analysis; 2 Financial Data Structures; 2.1 Motivation.
505 8 $a2.2 Essential Types of Financial Data2.2.1 Fundamental Data; 2.2.2 Market Data; 2.2.3 Analytics; 2.2.4 Alternative Data; 2.3 Bars; 2.3.1 Standard Bars; 2.3.2 Information-Driven Bars; 2.4 Dealing with Multi-Product Series; 2.4.1 The ETF Trick; 2.4.2 PCA Weights; 2.4.3 Single Future Roll; 2.5 Sampling Features; 2.5.1 Sampling for Reduction; 2.5.2 Event-Based Sampling; Exercises; References; 3 Labeling; 3.1 Motivation; 3.2 The Fixed-Time Horizon Method; 3.3 Computing Dynamic Thresholds; 3.4 The Triple-Barrier Method; 3.5 Learning Side and Size; 3.6 Meta-Labeling; 3.7 How to Use Meta-Labeling.
505 8 $a3.8 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 Review.
505 8 $a5.4 The Method5.4.1 Long Memory; 5.4.2 Iterative Estimation; 5.4.3 Convergence; 5.5 Implementation; 5.5.1 Expanding Window; 5.5.2 Fixed-Width Window Fracdiff; 5.6 Stationarity with Maximum Memory Preservation; 5.7 Conclusion; Exercises; References; Bibliography; PART 2 Modelling; 6 Ensemble Methods; 6.1 Motivation; 6.2 The Three Sources of Errors; 6.3 Bootstrap Aggregation; 6.3.1 Variance Reduction; 6.3.2 Improved Accuracy; 6.3.3 Observation Redundancy; 6.4 Random Forest; 6.5 Boosting; 6.6 Bagging vs. Boosting in Finance; 6.7 Bagging for Scalability; Exercises; References; Bibliography.
505 8 $a7 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 Features.
588 0 $aOnline resource; title from digital title page (viewed on March 02, 2018).
650 0 $aFinance$xData processing.
650 0 $aFinance$xMathematical models.
650 0 $aMachine learning.
650 6 $aFinances$xInformatique.
650 6 $aFinances$xModèles mathématiques.
650 6 $aApprentissage automatique.
650 7 $aBUSINESS & ECONOMICS$xInvestments & Securities.$2bisacsh
650 7 $aFinance$xData processing.$2fast$0(OCoLC)fst00924370
650 7 $aFinance$xMathematical models.$2fast$0(OCoLC)fst00924398
650 7 $aMachine learning.$2fast$0(OCoLC)fst01004795
655 0 $aElectronic books.
655 4 $aElectronic books.
776 08 $iPrint version:$aLópez de Prado, Marcos Mailoc.$tAdvances in financial machine learning.$dNew Jersey : Wiley, [2018]$z9781119482086$w(DLC) 2017049249
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio13632362$zAll EBSCO eBooks
852 8 $blweb$hEBOOKS