Isbn: 9798896652311 - causal inference for traders: moving beyond correlation in financial markets (5 risultati)

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Paperback. Condizione: new. Paperback. "Causal Inference for Traders: Moving Beyond Correlation in Financial Markets"In modern markets, most trading models are still built on fragile correlations that unravel the moment regimes shift. This book is for systematic traders, quantitative researchers, data scientists, and risk managers who want to move beyond black-box prediction and towards principled decision-making. It bridges the gap between academic causal inference and the messy realities of financial data, showing how to reason about "what would have happened" under alternative trading rules, signals, or policies.The book develops a complete toolkit for causal analysis in markets: from returns, microstructure, and backtest hygiene, through probability, estimation, and machine learning foundations, to formal causal frameworks with DAGs, potential outcomes, and identification rules. Readers learn how to define estimands like ATE and CATE in P&L terms; deploy matching, weighting, and doubly robust methods; and exploit quasi-experiments, DiD, RDD, IV, and synthetic control in time-series and panels. The final chapters convert effects into tradable policies via offline evaluation, policy learning, causal reinforcement learning, and robust, governed deployment.The text assumes comfort with basic statistics, linear algebra, and programming, but it is self-contained in its treatment of causal concepts. Throughout, financial examples and implementation-oriented discussions emphasize realistic workflows and failure modes, making this a practical field guide rather than a purely theoretical monograph. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'Causal Inference for Traders: Moving Beyond Correlation in Financial Markets'In modern markets, most trading models are still built on fragile correlations that unravel the moment regimes shift. This book is for systematic traders, quantitative researchers, data scientists, and risk managers who want to move beyond black-box prediction and towards principled decision-making. It bridges the gap between academic causal inference and the messy realities of financial data, showing how to reason about 'what would have happened' under alternative trading rules, signals, or policies.The book develops a complete toolkit for causal analysis in markets: from returns, microstructure, and backtest hygiene, through probability, estimation, and machine learning foundations, to formal causal frameworks with DAGs, potential outcomes, and identification rules. Readers learn how to define estimands like ATE and CATE in P&L terms; deploy matching, weighting, and doubly robust methods; and exploit quasi-experiments, DiD, RDD, IV, and synthetic control in time-series and panels. The final chapters convert effects into tradable policies via offline evaluation, policy learning, causal reinforcement learning, and robust, governed deployment.The text assumes comfort with basic statistics, linear algebra, and programming, but it is self-contained in its treatment of causal concepts. Throughout, financial examples and implementation-oriented discussions emphasize realistic workflows and failure modes, making this a practical field guide rather than a purely theoretical monograph.…

Taschenbuch. Condizione: Neu. Causal Inference for Traders | Moving Beyond Correlation in Financial Markets | Victor Trex | Taschenbuch | Englisch | 2025 | NobleTrex Press | EAN 9798896652311 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…