This book presents a systematic application of recent advances in artificial intelligence (AI) to the problem of asset management. While natural language processing and text mining techniques, such as semantic representation, sentiment analysis, entity extraction, commonsense reasoning, and fact checking have been evolving for decades, finance theories have not yet fully considered and adapted to these ideas.

In this unique, readable volume, the authors discuss integrating textual knowledge and market sentiment step-by-step, offering readers new insights into the most popular portfolio optimization theories: the Markowitz model and the Black-Litterman model. The authors also provide valuable visions of how AI technology-based infrastructures could cut the cost of and automate wealth management procedures.

This inspiring book is a must-read for researchers and bankers interested in cutting-edge AI applications in finance.

Chapter 1. Introduction

Chapter 2
Revisiting the Literature
Chapter 3. Theoretical Underpinnings on Text Mining
Chapter 4. Computational Semantics for Asset Correlations
Chapter 5. Sentiment Analysis for View Modeling
Chapter 6. Storage and Update of Domain Knowledge
Chapter 7. Dialog Systems and Robo-advisory
Chapter 8. Concluding Remarks
Appendix
Index.