Python for Asset Management
Series: Chapman and Hall/CRC Financial Mathematics Series;
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Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 31 August 2026
- ISBN 9781041308324
- Binding Hardback
- No. of pages334 pages
- Size 234x156 mm
- Language English
- Illustrations 58 Illustrations, color; 58 Line drawings, color; 40 Tables, black & white 700
Categories
Short description:
The book empowers non-programmers?portfolio managers, risk analysts, and students?to implement advanced models themselves. It responds to the growing demand for quantitative literacy in finance, especially in sustainable investing and smart beta strategies, areas of active research for both of the authors.
MoreLong description:
The asset management industry is undergoing a paradigm shift toward automation, transparency, and data-driven decision-making. Traditional tools (Excel, Bloomberg) are being replaced by programmable, scalable solutions. Yet most finance professionals lack accessible, practical training in applying Python to real portfolio problems.
Python for Asset Management fills that gap. The book empowers non-programmers ? portfolio managers, risk analysts, and students ? to implement advanced models themselves. It responds to the growing demand for quantitative literacy in finance, especially in sustainable investing and smart beta strategies, areas of active research for both of the authors.
Features
- 31 hands-on Python exercises with real data and executable code.
- Complete GitHub repository (MIT License) with all scripts, data pipelines, and results.
- Step-by-step implementation of VaR (historical, parametric, Monte Carlo), bond immunization, and factor models.
- Real-world decision tools ? e.g., build a bullet/barbell/ladder bond portfolio, run Brinson?Fachler attribution, or backtest smart beta vs. index.
- Immediate applicability ? every exercise produces a deliverable (e.g., optimal weights, risk report, attribution table) ready for client meetings.
- Focus on practical asset management workflows, not just theory.
Table of Contents:
Chapter One: Python Libraries. Chapter Two: Python Applied to Market Index Analysis. Chapter Three: Python Applied to Equity Management. Chapter Four: Python Applied to Bond Management. Chapter Five: Python Applied to Return Attribution. Chapter Six: Python Applied to Investment Funds. Chapter Seven: Python Applied to Factor Investing. Chapter Eight: Python Applied to ESG Investment. Bibliography.
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