Название: Artificial Intelligence and Beyond for FinanceАвтор: Marco Corazza, René Garcia, Faisal Shah Khan, Davide La Torre, Hatem Masri
Издательство: World Scientific Publishing
Год: 2024
Страниц: 429
Язык: английский
Формат: pdf (true)
Размер: 13.6 MB
Why did we write this book? We wanted to help financial experts and investors to understand the state of the art of Artificial Intelligence and Machine Learning in finance. And so, what is Artificial Intelligence?
Deep Learning (DL) is a type of Machine Learning (ML) model that is typically used in supervised learning. The main feature of DL is the use of artificial neurons that model neurons of the human brain. Arranging many artificial neurons as nodes in vertical layers from left to right produces a neural network consisting of interconnected nodes. Each vertical collection of nodes is called a layer of the neural network. The layers in between the first and the last layers are called hidden layers. If there are one or two hidden layers in a neural network, it is called shallow ; otherwise, a network is called deep. Therefore, DL refers to learning that takes place with respect to a deep neural network. For context, we note that a linear regression can be implemented on a shallow neural network. No matter the specific industry or application, AI has become a new engine of growth. Both finance and banking have been leveraging AI technologies and algorithms and applying them to automate routine tasks, procedures, forecasting, and improving the overall customer experience. This book focuses on recent applications of AI to finance, in particular, the following:
(a) how ML algorithms can forecast signals in the modern dynamic financial world, enabling investors to make data-driven decisionsin this rapidly evolving market,
(b) how AI can be leveraged to create sophisticated quantitative tools for comprehensive financial analysis, providing informed decision-making and enhancing investment strategies,
(c) how to decode the intricacies of market sentiments to uncover valuable insights and gain a deeper understanding of market dynamics,
(d) how to use AI algorithms to optimize risk–return profiles and maximizing investment performance,
(e) how to develop transparent and interpretable models that shed light on risk factors and support effective risk management strategies,
(f) how to deploy reinforcement learning techniques, enabling portfolios to adapt and optimize strategies in response to dynamic market conditions.
The topics covered by this book make it an invaluable resource for academics, researchers, policymakers, and practitioners alike who want to understand how AI has affected the banking and financial industries and how it will continue to change them in the years to come.
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