AI for Finance (Paperback, 1)

1. AI-Finance Synergy. 1.1. Speed matters. 1.2. The race is on seeking, not running. 1.3. Pattern recognition. 1.4. Data mining. 1.5. Forecasting. 1.6. Concluding Summary: Synergy between AI and finance. 2. Machine Learning knows no boundaries? 2.1. AlphaGo: the success. 2.2. General AI: the rose garden. 2.3. Complication: the reality. 2.4. Combinatorial explosion, the curse of computation. 2.5. A missing ingredient in classical economics. 2.6. Neither can live while the other survives. 2.7. Summary: powerful but not magical. 3.Machine Learning in Finance. 3.1. Machine Learning for Forecasting. 3.2. Supervised learning. 3.3. Know your data. 3.4. A glimpse of game theory. 3.5. ‘Unsupervised learning’ for bargaining. 3.6. Summary: machine learning is a game-changer 4. Modelling, Simulation and machine learning 4.1. Modelling. 4.2. Modelling: imperfect but useful. 4.3. Simulation: beyond mathematical analysis. 4.4. Case study: Risk Analysis. 4.5. Adding machine learning to modelling and simulation. 4.6. Mechanism Design. 4.7. Conclusion: model-simulate-learn, a powerful combination. 5. Portfolio Optimization 5.1. Maximising profit, minimising risk. 5.2. The Markowitz Model for portfolio optimization. 5.3. Constrained optimization. 5.4. Two-objective optimization. 5.5. The reality is much more complex. 5.6. Economics vs Computer Science. 5.7. Summary. 6. Financial Data: beyond time series 6.1. What is Time exactly? 6.2. Event-based time representation. 6.3. Measuring market volatility under DC. 6.4. Two eyes are better than one. 6.5. Striking discoveries under DC. 6.6. Research in DC. 6.7. Conclusion: new representation, new frontier. 7. Over the Horizon 7.1. Algorithmic Trading drones. 7.2. High-Frequency Finance. 7.3. Blockchain. 7.4. Information extraction from news. 7.5. Finance as a hard science.