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Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory

"Financial Markets in Practice: From Post-Crisis Intermediation to FinTechs delivers an overview of risk transformations operated by the financial industries from the perspective of quantitative finance. It gives a pedagogical and comprehensive understanding of the structure of the financial system as a network of risk suppliers and risk consumers, where different categories of market participants buy, transform, net and re-sell different kinds of risks. This risk-transformation oriented view is supported by the evolutions that followed the last global financial crisis: consumers of financial products asked for less complex risk transformations, regulators demanded to keep as few risks as possible inside financial institutions, and market participants turned to run mass market-like businesses rather than "haute couture"-like businesses. This book portrays the network of intermediaries composing the financial system, describes their most common business models, explains the exact role of each kind of market participant and underlines the interaction between them. It seeks to reveal the potential disintermediation that could happen inside the financial sector led by FinTechs and Artificial Intelligence-based innovations. Readers are invited to rethink the role of market participants in the post-crisis world, and are prepared for the next wave of changes that are driven by data sciences, AI and blockchain. Amid these transformations, quantitative finance will be increasingly involved in all aspects of the financial system. This handy resource helps practitioners from the buy-side and sell-side to gain insights to an overview of business models in the financial system from an intermediation perspective, and guides students to comprehensively understand the complex ecosystem they will evolve into."

Market fragmentation : monitoring and history -- Smart order routing -- Tick size; -- Information seeking and price discovery -- Dark pools and broker crossing networks -- Liquidity : the viewpoint of trading venues -- The agenda of high frequency traders -- The link between fragmentation and systemic risk -- The flash crash -- The signature plot -- The epps effect -- Optimal organization for optimal trading -- Market impact at different time scales -- Optimal trading quantitative approaches : optimal trade scheduling and optimal order routing -- Orderbook dynamics