Artificial Intelligence in Financial Markets

Artificial Intelligence in Financial Markets: Cutting Edge Applications for Risk Management, Portfolio Optimization and Economics (New Developments in Quantitative Trading and Investment)

As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making.

This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization.

This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field.

Table of Contents

Part I: Introduction to Artificial Intelligence
Chapter 1: A Review of Artificially Intelligent Applications in the Financial Domain

Part II: Financial Forecasting and Trading
Chapter 2: Trading the FTSE100 Index: ‘Adaptive’ Modelling and Optimization Techniques
Chapter 3: Modelling, Forecasting and Trading the Crack: A Sliding Window Approach to Training Neural Networks
Chapter 4: GEPTrader: A New Standalone Tool for Constructing Trading Strategies with Gene Expression Programming

Part III: Economics
Chapter 5: Business Intelligence for Decision Making in Economics

Part IV: Credit Risk and Analysis
Chapter 6: An Automated Literature Analysis on Data Mining Applications to Credit Risk Assessment
Chapter 7: Intelligent Credit Risk Decision Support: Architecture and Implementations
Chapter 8: Artificial Intelligence for Islamic Sukuk Rating Predictions

Part V: Portfolio Management, Analysis and Optimisation
Chapter 9: Portfolio Selection as a Multi-period Choice Problem Under Uncertainty: An Interaction-Based Approach
Chapter 10: Handling Model Risk in Portfolio Selection Using Multi-Objective Genetic Algorithm
Chapter 11: Linear Regression Versus Fuzzy Linear Regression: Does it Make a Difference in the Evaluation of the Performance of Mutual Fund Managers?