Författare

Matthew F. Dixon

Bästsäljande2 verkEngelska

Matthew F. Dixon är en uppskattad författare inom Ekonomi och Ledarskap med totalt 2 böcker tillgängliga på Bokkollen, utgivna hos Springer Nature Switzerland AG.

Bland verken finns Machine Learning in Finance, som toppar listan över Matthew F. Dixons populäraste böcker. Verken spänner över ekonomi & ledarskap och tilltalar läsare som uppskattar genren.

Letar du efter något nytt att läsa? Prova Machine Learning in Finance – ett annat uppskattat verk av Matthew F. Dixon.

På Bokkollen gör vi det enkelt att navigera i Matthew F. Dixons författarskap. Vår databas uppdateras ständigt med nya släpp och format, så oavsett om du söker efter en lättläst pocket för semestern, en lyxig inbunden presentutgåva eller en digital ljudbok för pendlingen, har vi rätt utgåva för dig.

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Machine Learning in Finance
Mest populär

Machine Learning in Finance

This 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 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.