LEADER 03473uam 2200553 a 450 001 9910957713703321 005 20240506114551.0 010 $a9781119482116 010 $a1119482119 035 $a(CKB)3840000000340227 035 $a(CaSebORM)9781119482086 035 $a(MiAaPQ)EBC5240570 035 $a(BIP)060890092 035 $a(OCoLC)1031706579 035 $a(OCoLC)on1031706579 035 $a(FR-PaCSA)88944366 035 $a(FRCYB88944366)88944366 035 $a(EXLCZ)993840000000340227 100 $a20230318d2018 uy 101 0 $aeng 135 $au||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Financial Machine Learning /$fde Prado, Marcos 205 $a1st edition 210 1$cWiley,$d2018. 215 $a1 online resource (400 pages) 311 08$a9781119482086 311 08$a1119482089 320 $aIncludes bibliographical references and index. 327 $aMachine generated contents note: About the Author Preamble 1. Financial Machine Learning as a Distinct Subject Part 1: Data Analysis 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. 330 $aMachine 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. 606 $aFinance$xData processing 606 $aFinance$xMathematical models 606 $aMachine learning 615 0$aFinance$xData processing. 615 0$aFinance$xMathematical models. 615 0$aMachine learning. 676 $a332.0285/631 686 $aBUS036000$2bisacsh 700 $ade Prado$b Marcos$01796215 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910957713703321 996 $aAdvances in Financial Machine Learning$94337890 997 $aUNINA