AI in Quantitative Analysis
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AI in Quantitative Analysis explores the intersection of artificial intelligence and modern financial modeling. Structured into four comprehensive parts, the book guides readers from foundational concepts to advanced applications and ethical considerations in AI-driven quantitative finance.
Part I lays the groundwork, detailing the evolution of quantitative analysis and the integration of AI into financial systems. It covers essential mathematical and statistical principles, creating a solid base for understanding how AI models function in financial contexts.
Part II dives into core machine learning techniques, including supervised and unsupervised learning, time series modeling, and reinforcement learning. It explains how regression, classification, clustering, ARIMA, LSTM, Transformers, and policy gradient methods are used for price prediction, anomaly detection, and portfolio optimization.
Part III expands into sophisticated applications such as Natural Language Processing (NLP) for extracting sentiment and events from news and social media, Generative AI for simulating market scenarios and augmenting data, and Explainable AI tools like SHAP and LIME. It also discusses how AI enhances risk management, from fraud detection to credit scoring and stress testing.
Part IV focuses on practical implementation—highlighting programming languages (Python, R, Julia), machine learning libraries, backtesting tools, real-time data handling, deployment strategies, and MLOps in finance. The final chapter addresses critical ethical challenges, including bias, transparency, AI governance, and emerging technologies like quantum computing and neuromorphic architectures.
This book offers a detailed, application-rich guide for finance professionals, data scientists, and academics seeking to master the use of AI in quantitative financial research and decision-making.
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AI in Quantitative Analysis explores the intersection of artificial intelligence and modern financial modeling. Structured into four comprehensive parts, the book guides readers from foundational concepts to advanced applications and ethical considerations in AI-driven quantitative finance.
Part I lays the groundwork, detailing the evolution of quantitative analysis and the integration of AI into financial systems. It covers essential mathematical and statistical principles, creating a solid base for understanding how AI models function in financial contexts.
Part II dives into core machine learning techniques, including supervised and unsupervised learning, time series modeling, and reinforcement learning. It explains how regression, classification, clustering, ARIMA, LSTM, Transformers, and policy gradient methods are used for price prediction, anomaly detection, and portfolio optimization.
Part III expands into sophisticated applications such as Natural Language Processing (NLP) for extracting sentiment and events from news and social media, Generative AI for simulating market scenarios and augmenting data, and Explainable AI tools like SHAP and LIME. It also discusses how AI enhances risk management, from fraud detection to credit scoring and stress testing.
Part IV focuses on practical implementation—highlighting programming languages (Python, R, Julia), machine learning libraries, backtesting tools, real-time data handling, deployment strategies, and MLOps in finance. The final chapter addresses critical ethical challenges, including bias, transparency, AI governance, and emerging technologies like quantum computing and neuromorphic architectures.
This book offers a detailed, application-rich guide for finance professionals, data scientists, and academics seeking to master the use of AI in quantitative financial research and decision-making.