Description

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This book presents artificial intelligence methods in money. It provides a unified treatment of artificial intelligence as well as various analytical as well as computational techniques in quantitative financing, such as economic econometrics as well as discrete time stochastic control, with a focus on just how theory and also hypothesis tests educate the selection of algorithm for financial data modeling as well as decision making.

This book is written for innovative college students as well as academics in economic econometrics, mathematical money as well as applied statistics, along with quants and information researchers in the field of quantitative financing.

This book is separated right into three components, each part covering theory as well as applications:

- The initial presents monitored finding out for cross-sectional information from both a Bayesian as well as frequentist viewpoint.

- The second part provides supervised understanding for time series data, arguably the most common data type used in money with examples in trading, stochastic volatility and fixed income modeling.

- Finally, the 3rd part offers reinforcement knowing and also its applications in trading, investment and also wide range management. As a bridge to research in this emerging field, the final chapter presents the frontiers of artificial intelligence in finance from a researcher's perspective, highlighting how many widely known ideas in analytical physics are most likely to become important methodologies for machine learning in money.