{"product_id":"154783","title":"Financial Machine Learning for Asset Management ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYDPGpJnkzeuFFVkmDT9j0tUOIE1Y.png?v=1765081892\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Financial Machine Learning for Asset Management \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/96849122\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e This book is a sequel to Dr. Lopez de Prado's book, \"Complete Analysis of Practical Financial Machine Learning.\" Conceptually, it can be applied to general asset management, and it can serve as a guide for quantitative managers and quantitative analysts. \u003cbr\u003eWe present ideas for financial applications of machine learning, further explaining the fundamental concepts necessary for understanding \"Complete Analysis of Practical Financial Machine Learning\" and adding recent related research.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eChapter 1.\u003cbr\u003e Entering\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 1.1 Motivation\u003cbr\u003e 1.2 Theory is important\u003cbr\u003e 1.2.1 Lesson 1: Theory is necessary.\u003cbr\u003e 1.2.2 Lesson 2: Machine learning helps discover theories.\u003cbr\u003e 1.3 How Scientists Use Machine Learning\u003cbr\u003e 1.4 Two forms of overfitting\u003cbr\u003e 1.4.1 Training set overfitting\u003cbr\u003e 1.4.2 Test set overfitting\u003cbr\u003e 1.5 Overview\u003cbr\u003e 1.6 Audience\u003cbr\u003e 1.7 Five Common Financial Machine Learning Misconceptions\u003cbr\u003e 1.7.1 Machine Learning is the Holy Grail vs. Machine Learning is Useless\u003cbr\u003e 1.7.2 Machine Learning is a Black Box\u003cbr\u003e 1.7.3 Finance has insufficient data to apply machine learning.\u003cbr\u003e 1.7.4 The signal-to-noise ratio in finance is too low. \u003cbr\u003e1.7.5 The risk of overfitting in finance is too great.\u003cbr\u003e 1.8 The Future of Financial Research\u003cbr\u003e 1.9 Frequently Asked Questions\u003cbr\u003e 1.10 Conclusion\u003cbr\u003e 1.11 Practice Problems\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 2.\u003cbr\u003e Noise removal and noise removal\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 2.1 Motivation\u003cbr\u003e 2.2 Marchenko-Pasteur theorem\u003cbr\u003e 2.3 Random matrix with signal\u003cbr\u003e 2.4 Marchenko-Pasteur PDF Adaptation\u003cbr\u003e 2.5 Noise Reduction\u003cbr\u003e 2.5.1 Constant residual eigenvalue method\u003cbr\u003e 2.5.2 Target reduction\u003cbr\u003e 2.6 Removing the main sound\u003cbr\u003e 2.7 Experimental Results\u003cbr\u003e 2.7.1 Minimum Variance Portfolio\u003cbr\u003e 2.7.2 Maximum Sharpe Ratio Portfolio\u003cbr\u003e 2.8 Conclusion\u003cbr\u003e 2.9 Practice Problems\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 3.\u003cbr\u003e distance scale\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 3.1 Motivation\u003cbr\u003e 3.2 Correlation coefficient-based scale\u003cbr\u003e 3.3 Limits and binding entropy\u003cbr\u003e 3.4 Conditional entropy\u003cbr\u003e 3.5 Coolback - Leibler Emission\u003cbr\u003e 3.6 Cross entropy\u003cbr\u003e 3.7 Mutual Information\u003cbr\u003e 3.8 Information Variation\u003cbr\u003e 3.9 Dioxide\u003cbr\u003e 3.10 Distance between two segments\u003cbr\u003e 3.11 Experimental Results\u003cbr\u003e 3.11.1 Unrelated\u003cbr\u003e 3.11.2 Linear relationships\u003cbr\u003e 3.11.3 Nonlinear relationships\u003cbr\u003e 3.12 Conclusion\u003cbr\u003e 3.13 Practice Problems\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 4.\u003cbr\u003e Optimal clustering\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 4.1 Motivation\u003cbr\u003e 4.2 Proximity matrix\u003cbr\u003e 4.3 Clustering Types  \u003cbr\u003e4.4 Number of clusters\u003cbr\u003e 4.4.1 Observation matrix\u003cbr\u003e 4.4.2 Basic clustering\u003cbr\u003e 4.4.3 High-level clustering\u003cbr\u003e 4.5 Experimental Results\u003cbr\u003e 4.5.1 Generating a random block correlation matrix\u003cbr\u003e 4.5.2 Number of clusters\u003cbr\u003e 4.6 Conclusion\u003cbr\u003e 4.7 Practice Problems\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 5.\u003cbr\u003e Financial Label\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 5.1 Motivation\u003cbr\u003e 5.2 Fixed-period method\u003cbr\u003e 5.3 Triple Barrier Method\u003cbr\u003e 5.4 Trend Search Methods\u003cbr\u003e 5.5 Meta-labeling\u003cbr\u003e 5.5.1 Bet size based on expected Sharpe ratio\u003cbr\u003e 5.5.2 Ensemble Bet Size\u003cbr\u003e 5.6 Experimental Results\u003cbr\u003e 5.7 Conclusion\u003cbr\u003e 5.8 Practice Problems\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 6.\u003cbr\u003e Feature Importance Analysis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 6.1 Motivation\u003cbr\u003e 6.2 p-value\u003cbr\u003e 6.2.1 Some flaws with p values\u003cbr\u003e 6.2.2 Numerical Examples\u003cbr\u003e 6.3 Feature Importance\u003cbr\u003e 6.3.1 Average Decrease Impurity\u003cbr\u003e 6.3.2 Average Decrease Accuracy\u003cbr\u003e 6.4 Probability-weighted accuracy\u003cbr\u003e 6.5 Replacement Effect\u003cbr\u003e 6.5.1 Orthogonalization\u003cbr\u003e 6.5.2 Cluster Feature Importance\u003cbr\u003e 6.6 Experimental Results\u003cbr\u003e 6.7 Conclusion\u003cbr\u003e 6.8 Practice Problems\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 7.\u003cbr\u003e Building a portfolio\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 7.1 Motivation\u003cbr\u003e 7.2 Convex Portfolio Optimization\u003cbr\u003e 7.3 Condition number\u003cbr\u003e 7.4 The Curse of Markowitz\u003cbr\u003e 7.5 Signals as a Source of Covariance Instability  \u003cbr\u003e7.6 Overlapping Cluster Optimization Algorithm\u003cbr\u003e 7.6.1 Correlation Clustering\u003cbr\u003e 7.6.2 Proportion within cluster\u003cbr\u003e 7.6.3 Distribution between clusters\u003cbr\u003e 7.7 Experimental Results\u003cbr\u003e 7.7.1 Minimum Variance Portfolio\u003cbr\u003e 7.7.2 Maximum Sharpe Ratio Portfolio\u003cbr\u003e 7.8 Conclusion\u003cbr\u003e 7.9 Practice Problems\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 8.\u003cbr\u003e Test set overfitting\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 8.1 Motivation\u003cbr\u003e 8.2 Precision and Recall\u003cbr\u003e 8.3 Precision and recall under multiple testing\u003cbr\u003e 8.4 Sharpe ratio\u003cbr\u003e 8.5 Summary of 'False Strategies'\u003cbr\u003e 8.6 Experimental Results\u003cbr\u003e 8.7 reduced sharp ratio\u003cbr\u003e 8.7.1 Valid number of trials\u003cbr\u003e 8.7.2 Dispersion between trials\u003cbr\u003e 8.8 Error rate by group\u003cbr\u003e 8.8.1 Siddharth adjustments\u003cbr\u003e 8.8.2 Type 1 error under multiple testing\u003cbr\u003e 8.8.3 Type 2 error under multiple testing\u003cbr\u003e 8.8.4 Interaction between Type 1 and Type 2 errors\u003cbr\u003e 8.9 Conclusion\u003cbr\u003e 8.10 Practice Problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eAppendix A.\u003cbr\u003e Synthetic data testing\u003cbr\u003e Appendix B.\u003cbr\u003e Proof of the 'False Strategy' Theorem\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate3303\/MidCate007\/330263464.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e★ Structure of this book ★\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eWe will learn that financial covariance matrices are noisy and must be cleaned before performing regression analysis or calculating optimal portfolios (Chapter 2).\u003cbr\u003e We will learn that correlation is a very narrow definition of interrelationship, and that various information-theoretic measures are more insightful (Chapter 3).\u003cbr\u003e We will learn an intuitive way to reduce the dimensionality of a space without changing the basis.\u003cbr\u003e Unlike principal component analysis (PCA), machine learning-based dimensionality reduction methods provide intuitive results (Chapter 4).\u003cbr\u003e Rather than aiming for impossible fixed-horizon predictions, we will learn alternative methods that propose financial forecasting problems that can be solved with high accuracy (Chapter 5).\u003cbr\u003e You will learn modern alternatives to the classical p-value (Chapter 6) and how to address the instability problem that plagues mean-variance investment portfolios (Chapter 7). \u003cbr\u003eAnd you will learn how to assess the probability that a researcher's findings are false as a result of multiple testing (Chapter 8).\u003cbr\u003e If you work in the asset management industry or financial academia, this book is for you.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003e★ Author's Note ★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e The purpose of this book is to introduce machine learning tools useful for developing economic and financial theory.\u003cbr\u003e A successful investment strategy is a specific implementation of a general theory.\u003cbr\u003e An investment strategy that lacks theoretical justification is likely to be false.\u003cbr\u003e Therefore, researchers should focus on developing theories rather than backtesting potential strategies.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e Machine learning is not a black box, nor does it necessarily overfit.\u003cbr\u003e Machine learning tools complement rather than replace classical methods. \u003cbr\u003eThe strengths of machine learning include (1) its focus on out-of-sample predictive power prior to variance assessment, (2) its use of computational methods that avoid relying on (potentially unrealistic) assumptions, (3) its ability to learn complex settings that include nonlinear, hierarchical, and discontinuous interaction effects in high-dimensional spaces, and (4) its ability to separate variable exploration from setting exploration to ensure robustness to multicollinearity and other surrogate effects.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003e★ Translator's Note ★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e This book complements Dr. Lopez de Prado's previous book, \"Advances in Financial Machine Learning,\" through the Elements of Quantitative Finance series, and is conceptually applicable to general asset management.\u003cbr\u003e This book can be a guide, especially for quantitative managers and quantitative analysts. \u003cbr\u003eAlthough short, it presents the author's thoughts on the financial applications of machine learning. It also explains the basic concepts necessary for understanding the contents of \"Complete Analysis of Practical Financial Machine Learning\" in more detail and adds recent research related to it, so it is highly recommended as a companion book to \"Complete Analysis of Practical Financial Machine Learning.\"\u003cbr\u003e\u003cbr\u003e After reading this book, readers will be familiar with information theory-based distance concepts, particularly mutual information and information variation, optimal number of clusters (ONC), blocking of correlation coefficient matrices using hierarchical clustering, trend-based labeling, mean decay impurity (MDI), mean decay accuracy, probability-weighted accuracy, portfolio construction using hierarchical risk parity, and overfitting and false strategy cleanup on both the training and test sets. \u003cbr\u003eThese concepts will serve as an important foundation not only for machine learning but also for future financial research and financial strategy development.\u003cbr\u003e Additionally, if you reread \"Complete Analysis of Practical Financial Machine Learning\" based on the concepts in this book, you will find that many parts are connected and converge toward completion.\u003cbr\u003e\u003cbr\u003e The translator had a lot of personal contact with AQR, the quant fund where Dr. Lopez de Prado once worked, and was impressed by how, despite being one of the most street-smart and for-profit investment firms, it always actively recruits and collaborates with new research and figures from the academic world.\u003cbr\u003e In this culture and environment, it may be natural that a scholar with both financial practice and theory, like Dr. Lopez de Prado, would emerge. \u003cbr\u003eI conclude with the hope that in our country, too, active industry-academia cooperation will be promoted, producing many people with this kind of personality. \u003cbr\u003e\u003cbr\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePublication date:\u003c\/strong\u003e January 26, 2021\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 208 pages | 152*228mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791161754918\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1161754911 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893449621546,"sku":"154783","price":32.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/baf28acfb3a0c4edc4d3f0de010f9871.jpg?v=1765402676","url":"https:\/\/librairie.coreenne.fr\/en\/products\/154783","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}