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Engineering Investment Process: Making Value Creation Repeatable explores the quantitative steps of a financial investment process.
The authors study how these steps are articulated in order to make any value creation, whatever the asset class, consistent and robust.
The discussion includes factors, portfolio allocation, statistical and economic backtesting, but also the influence of negative rates, dynamical trading, state-space models, stylized facts, liquidity issues, or data biases.
Besides the quantitative concepts detailed here, the reader will find useful references to other works to develop an in-depth understanding of an investment process.
- Blends academic research with practical experience from quants, fund managers, and economists
- Puts financial mathematics and econometrics in their rightful place
- Presents useful information that will increase the reader's understanding of markets
- Clearly provides both the global framework, the investment process, and the useful econometric and financial tools that help in its construction
- Includes efficient tools taken from up-to-date econometric and financial techniques
Professionals that would need guidance in the building of an investment process, both for active and passive fund management. Quantitative and non-quantitative investment managers, emphasizing must-haves and pitfalls that any fund manager should be aware of. Risk managers or any manager needing a reference for assessing the supervision of an investment process (CEOs, regulators). Master class teaching and Academics interested in practical applications of academic research.
- I.1 Making value creation repeatable
- I.2 Why do we allocate?
- I.3 Building a process
- I.4 Further reading
List of Acronyms
1: Understanding the Investment Universe
- 1.1 Introduction
- 1.2 Computing returns
- 1.3 Moment estimation
- 1.4 The time series properties of returns
- 1.5 Modeling financial returns and why it matters to an investment process
- 1.6 Living in a world of factors
2: Dealing with Risk Factors
- 2.1 Dependence among markets
- 2.2 Linear factor models
- 2.3 Risk factor dynamics: the state-space modeling framework
- 2.4 The liquidity risk factor
- 2.5 Implications of low rates on risk modeling in fixed-income markets
3: Active Portfolio Construction
- 3.1 Introduction
- 3.2 A theoretical toolbox for allocation
- 3.3 A focus on mean-variance
- 3.4 Spectral insights for allocation
- 3.5 Allocating using views
- 3.6 Allocating without views
- 3.7 Dynamic trading
4: Backtesting and Statistical Significance of Performance
- 4.1 Introduction
- 4.2 Backtesting
- 4.3 Performance statistics
- 4.4 Statistical significance of performance
5: Gauging Economic Influences on Quantitative Strategies
- 5.1 A tale of three strategies
- 5.2 Building economic condition indices
- 5.3 Relating business conditions to market performances
- 5.4 The impact of economic information on a strategy’s performance
- A.1 Useful formulas
- A.2 Diversification measures
- A.3 A brief introduction to inverse problems
- A.4 Tables from Chapter 5
- No. of pages:
- © ISTE Press - Elsevier 2017
- 17th March 2017
- ISTE Press - Elsevier
- Hardcover ISBN:
- eBook ISBN:
Florian Ielpo is Head of Macro Research at Unigestion and associate researcher at University of Paris 1 in France.
University of Paris 1 and IPAG Business School, Paris, France
Chafic Merhy is Head of Credit Quantitative Research at Natixis Asset Management. He is also a lecturer at University Paris IX Dauphine.
Lecturer, University Paris IX Dauphine.
Guillaume Simon is Research Manager in Equity Statistical Arbitrage for Capital Fund Management.
Research manager, Capital Fund Management, Paris, France