Название: Artificial Intelligence for Capital MarketsАвтор: Syed Hasan Jafar, Hemachandran K, Hani El-Chaarani
Издательство: CRC Press
Год: 2023
Страниц: 183
Язык: английский
Формат: pdf (true)
Размер: 11.2 MB
Artificial Intelligence for Capital Market throws light on the application of AI/ML techniques in the financial capital markets. This book discusses the challenges posed by the AI/ML techniques as these are prone to "black box" syndrome. The complexity of understanding the underlying dynamics for results generated by these methods is one of the major concerns which is highlighted in this book.
Artificial Intelligence (AI) systems are machine-based systems with varying levels of autonomy, that are used to make predictions, decisions or recommendations for a given set of human-defined objectives. These systems have gained immense popularity in recent times and almost every sector and industry are exploring its application in some or another way. With the advent of big data and data analytics, there is an increase in the data sources and data volumes which have resulted in search of techniques which can help analyse such huge volumes quickly and efficiently. AI techniques such as Machine Learning (thereafter ML) present themselves as a feasible solution that uses massive amounts of data from alternative data sources to learn and improve predictability and performance through experience. Learning by experience is one characteristic of the AI models which caters to its immense popularity, without being programmed to do so by humans.
AI in Finance presents and explores the most recent advances in the application of innovative and emerging AI/ML models in the financial industry. The main contribution of this book is to provide new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume presents the applicability of various Machine Learning models such as Support Vector Regression, Regression trees and Markov Switching Models in asset pricing and portfolio theory, Artificial Neural Networks (ANNs) in asset pricing, stock market forecasting utilizing a deep learning model, credit and risk analysis of borrowers using Random Forest Algorithms, examining market efficiency using news-driven sentiment, employing LSTM and RNNs for technical analysis, and price forecasting using deep learning and additive models in financial markets.
Features:
Showcases Artificial Intelligence in finance service industry
Explains credit and risk analysis
Elaborates on cryptocurrencies and blockchain technology
Focuses on the optimal choice of asset pricing model
Introduces testing of market efficiency and forecasting in the stock market
This book serves as a reference book for academicians, industry professionals, traders, finance managers and stock brokers. It may also be used as textbook for graduate level courses in financial services and financial analytics.
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