000			a
999			_c33074 _d33074
008			240319b xxu||||| |||| 00| 0 eng d
020			_a9783030410704
082			_a332.0285554 _bDIX
100			_aDixon, Matthew F.
245			_aMachine learning in finance : from theory to practice
260			_bSpringer, _c2020 _aCham :
300			_axxv, 548 p. ; _bill., _c24 cm.
365			_b79.99 _c€ _d93.50
504			_aIncludes bibliographical references and index.
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.
650			_aInsurance
650			_aStatistics
650			_aApplications of mathematics
650			_aInterpretability
650			_aGaussian processes
650			_aProbabilistic sequence
650			_aInverse reinforcement
650			_aMachine learning and finance
650			_aBayesian regression
700			_aHalperin, Igor
700			_aBilokon, Paul A.
942			_2ddc _cBK