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Full Description
This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data.
Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
Contents
1. Machine Learning in Finance and Accounting 2. Decision Trees and Random Forests 3. Improving Longevity Risk Management through Machine Learning 4. Kernel Switching Ridge Regression in Business Intelligence 5. Predicting Stock Return Volatility using Sentiment Analysis of Corporate Annual Reports 6. Random Projection Methods in Economics and Finance 7. The Future of Cloud Computing in Financial Services: A Machine Learning and Artificial Intelligence Perspective 8. Prospects and Challenges of Using Artificial Intelligence in Audit Process 9. Web Usage Analysis: Pillar 3 Information Assessment in Turbulent Times 10. Machine Learning in the Fields of Accounting, Economics and Finance: The Emergence of New Strategies 11. Handling Class Imbalance Data in Business Domain 12. Artificial Intelligence (AI) in Recruiting Talents Recruiters' Intention and Actual Use of AI
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