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Machine Learning in Finance
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Description
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 Bayesianand 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 likelyto emerge as important methodologies for machine learning in finance.
Product details
Edition:
1
Number of Pages:
576
Release Date:
2020-07-02
Publication Date:
2020-07-02
Publisher:
Springer
Languages:
Original:
English
ISBN10:
3030410676
ISBN13:
9783030410674
GPSR Manufacturer Reference:
Weight:
1021 g
Height:
160 cm
Width:
241 cm
Thickness:
37 cm
Condition
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Very good
Almost no signs of wear. Book pages have no markings, accessories are intact and all other media are in good condition.
€69,49