Scope

The proposed book will present the state-of-the-art applications of Machine Learning and Deep Learning in the domain of Finance. In particular, we will present a combination of empirical evidence to diverse fields of Finance, Accounting, and Economics. This book will be useful to academics, practitioners, and policy-makers who are looking to train novel and the most advanced machine intelligence and deep learning classifiers. Thus, the purpose of this proposed book is to provide a broad area of applications to different financial assets classes and markets. Furthermore, from an extensive literature assessment, it is evident that there are no existing textbooks that narrate machine learning and deep learning classifiers to unlike areas of finance or to an extensive range of products and markets.

Deep learning is involved in the analysis of large and multiple features instances. It principally refers to acquiring knowledge and intelligence (by a computer program) from processed training examples for generating predictions. It deals with computationally intensive techniques, such as cluster analysis, dimensionality reduction, and support vector analysis. It is principally the area of computer science and is already frequently applied in social sciences, finance and banking, marketing research, operations research, and applied sciences. Moreover, computational finance is a domain of applied computer science that is concerned with practical issues in finance. It may be characterized as the study of features, instances, and learning algorithms applied in finance. It is an interdisciplinary area that integrates computational tools with numerical finance. Furthermore, computational finance applies arithmetical proofs that can be fitted to economic experiments, thereby contributing to the advancement of financial data modeling techniques and systems. These computational techniques are utilized in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. Eventually, this proposed edited book could play a significant role in financial data learning.

TOPICS

• Financial Data Analytics

• Machine Learning and Deep Learning

• Big Data Analytics and Cloud Computing

• Financial Data Mining and Knowledge Discovery

• Financial Expert Systems and Intelligent Systems

• Blockchain and Distributed Finance

• Mobile Technologies

• Big Data and Business Process Mining

• Cybersecurity and Privacy