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000938191 019__ $$a1178999231$$a1182447174$$a1182917486$$a1183933914
000938191 020__ $$a9783030410681$$q(electronic book)
000938191 020__ $$a3030410684$$q(electronic book)
000938191 020__ $$z9783030410674
000938191 0247_ $$a10.1007/978-3-030-41
000938191 035__ $$aSP(OCoLC)on1164491558
000938191 035__ $$aSP(OCoLC)1164491558$$z(OCoLC)1178999231$$z(OCoLC)1182447174$$z(OCoLC)1182917486$$z(OCoLC)1183933914
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000938191 049__ $$aISEA
000938191 050_4 $$aHG173
000938191 08204 $$a330.0285/631$$223
000938191 1001_ $$aDixon, Matthew F.
000938191 24510 $$aMachine learning in finance :$$bfrom theory to practice /$$cMatthew F. Dixon, Igor Halperin, Paul Bilokon.
000938191 260__ $$aCham :$$bSpringer,$$c2020.
000938191 300__ $$a1 online resource (565 pages)
000938191 336__ $$atext$$btxt$$2rdacontent
000938191 337__ $$acomputer$$bc$$2rdamedia
000938191 338__ $$aonline resource$$bcr$$2rdacarrier
000938191 504__ $$aIncludes bibliographical references index.
000938191 5050_ $$aChapter 1. Introduction -- Chapter 2. Probabilistic Modeling -- Chapter 3. Bayesian Regression & Gaussian Processes -- Chapter 4. Feed Forward Neural Networks -- Chapter 5. Interpretability -- Chapter 6. Sequence Modeling -- Chapter 7. Probabilistic Sequence Modeling -- Chapter 8. Advanced Neural Networks -- Chapter 9. Introduction to Reinforcement learning -- Chapter 10. Applications of Reinforcement Learning -- Chapter 11. Inverse Reinforcement Learning and Imitation Learning -- Chapter 12. Frontiers of Machine Learning and Finance.
000938191 506__ $$aAccess limited to authorized users.
000938191 520__ $$aThis book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
000938191 588__ $$aDescription based on print version record.
000938191 650_0 $$aFinance$$xData processing.
000938191 650_0 $$aMachine learning.
000938191 7001_ $$aHalperin, Igor.
000938191 7001_ $$aBilokon, Paul A.,$$d1982-
000938191 77608 $$iPrint version:$$aDixon, Matthew F.$$tMachine Learning in Finance : From Theory to Practice$$dCham : Springer International Publishing AG,c2020$$z9783030410674
000938191 852__ $$bebk
000938191 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-41068-1$$zOnline Access$$91397441.1
000938191 909CO $$ooai:library.usi.edu:938191$$pGLOBAL_SET
000938191 980__ $$aEBOOK
000938191 980__ $$aBIB
000938191 982__ $$aEbook
000938191 983__ $$aOnline
000938191 994__ $$a92$$bISE