We wrote this book to help financial experts and investors to understand the state of the art of artificial intelligence and machine learning in finance. But first, what is artificial intelligence? The foundations of artificial intelligence lie in the human desire to automate. Often this desire has had foundations in grand civilization-defining visions or economic needs, such as the Antikythera mechanism, circa 200 BCE. Considered to be the oldest known example of an analog computer, it is thought that the mechanism automated the prediction of the positions of the sun, the moon, and the planets to assist in navigation.
No matter the specific industry or application, AI has become a new engine of growth. Both finance and banking have been leveraging AI technologies and algorithms, applying them to automate routine tasks, procedures and forecasting, thereby improving overall customer experience.
The topics covered in this book make it an invaluable resource for academics, researchers, policymakers, and practitioners alike who want to understand how AI has affected the banking and financial industries and how it will continue to change them in the years to come.
Contents:
- Machine Learning in Portfolio Decisions (Massimo Guidolin)
- Natural Language Processing and Stock Returns (Manuela Pedio)
- Portfolio Allocation and Reinforcement Learning (René Garcia and Alissa Marinenko)
- Explainable Artificial Intelligence in Risk Management: A Framework (Silvio Andrae)
- How Can Sentiment Analysis Contribute to Financial Markets and Services? (Abraham Itzhak Weinberg)
- Quantum Fintech (Alessio Faccia)
- Tail Dependence of Eurozone Bond Yields and Sovereign CDS Spreads (Veni Arakelian, Roberto Savona and Marika Vezzoli)
- Stylized Facts of Decentralized Finance (De Fi) (Yuanyuan Zhang, Stephen Chan, Jeffrey Chu, Xin Liao and Helu Min)
- Effective Systems for Bot Detection and Real-Time Stock Market Predictions (Reem Abdulla Alkhalifa, Riadh Ksantini and Khaoula Tbarki)
- Reinforcement Machine Learning Optimization Algorithms for the Computation of Downside Risk and Investable Portfolios in Post 2007–2009 Financial Meltdown (Mazin A M Al Janabi)
- Deep Learning in Insurance: An Incremental Deep Learning Approach for Pricing Prediction Strategy in the Insurance Industry (Alaa Tareq Mohamed, Riadh Ksantini and Jihene Kaabi)
Readership: Academics, practitioners, financial investors.
Key Features:
- This book focuses on recent applications of AI to finance, in particular:
- how ML algorithms can forecast signals in the modern dynamic financial world, enabling investors to make data-driven decisions in this rapidly evolving market
- how AI can be leveraged to create sophisticated quantitative tools for comprehensive financial analysis, providing insights that empower informed decision-making and enhance investment strategies
- how to decode the intricacies of market sentiments to uncover valuable insights and gain a deeper understanding of market dynamics
- how to use AI algorithms to optimize risk-return profiles and maximizing investment performance
- how to develop transparent and interpretable models that shed light on risk factors and support effective risk management strategies
- how to deploy reinforcement learning techniques, enabling portfolios to adapt and optimize strategies in response to dynamic market conditions
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