Applications Of Reinforcement Learning In Finance
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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 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.
Recent Advances of Neural Network Models and Applications
Author: Simone Bassis
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-12-19
This volume collects a selection of contributions which has been presented at the 23rd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Vietri sul Mare, Salerno, Italy during May 23-24, 2013. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop- is organized in two main components, a special session and a group of regular sessions featuring different aspects and point of views of artificial neural networks, artificial and natural intelligence, as well as psychological and cognitive theories for modeling human behaviors and human machine interactions, including Information Communication applications of compelling interest.
Applications of Reinforcement Learning in Finance
Author: Rubi Pence
language: en
Publisher: Independently Published
Release Date: 2023-12-02
Dive into the dynamic intersection of finance and cutting-edge technology with "Applications of Reinforcement Learning in Finance." This comprehensive guide explores the transformative impact of reinforcement learning on various facets of the financial industry, offering readers a profound understanding of how this revolutionary approach is reshaping decision-making processes. In this insightful book, you'll embark on a journey through the intricate applications of reinforcement learning, unraveling its potential to optimize trading strategies, risk management, portfolio construction, and more. With real-world examples and practical insights, this book equips both finance professionals and tech enthusiasts with the knowledge needed to leverage reinforcement learning algorithms effectively.