Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage

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Together with providing the seven tenets for QEPM, the authors explain in great detail how the tenets apply to their thought processes. In addition, in his role working for corporate equity derivatives businesses, he has been involved in structuring marketing and executing very large and strategic transactions for large companies and institutions, mainly in Europe, but also in Emerging Markets. Each quantitative investment strategy has its own risk-reward profile and is suitable for different market conditions.

Quantitative Equity Portfolio Management - The Theory and Quantitative Equity Portfolio Management - The Theory and

All the results, tested with Python programs, are demonstrated rigorously, often using geometric approaches for optimization problems and intrinsic approaches for statistical methods, leading to unusually short and elegant proofs. You will start by learning about the various behavioral biases – mistakes that investors make and understand their reasons. Overfitting is a common problem where the model performs well on past data but poorly when presented with new events. On the technical side, there are a number of approaches and measures to help discern the risks and opportunities.

The book’s final part addresses such practical applications as backtesting, analyzing model performance, and real-life portfolio management issues, such as taxes and transaction costs. For example, a portfolio that starts out with a 70% equity and 30% fixed-income allocation could, after an extended market rally, shift to an 80/20 allocation.

Quantitative Portfolio Manager | CQF A Day in the Life of a Quantitative Portfolio Manager | CQF

Previously he spent 19 years working in investment banking in London, in international banks, and 4 years in Paris in an arbitrage bank. According to the poll conducted by the CQF Institute, 68% of the respondents strongly agreed that the portfolio manager of the future will require more technical skills than they do today; an additional 15% generally agreed with the statement. Potential pitfalls to watch for in quantitative investing include model risk, overfitting, and not accounting for the effects of a rapidly evolving market.Each course in the Specialization has learning objectives that together make up the overarching goal for this Specialization.