Engineering Investment Process

Making Value Creation Repeatable

Florian Ielpo, Chafic Merhy, Guillaume Simon

• Publisher: ISTE Press - Elsevier
• Year: 2017
• Print ISBN: 9781785481628
• eBook ISBN: 9780081011485
• Topic: Business
• Subtopic: Investments & Securities
• Index: Business

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

1

Understanding the Investment Universe

Abstract

With a variety of asset classes with different liquidities, constraints and characteristics, and a large number of investors with different investment horizons, speaking in general of financial returns is rather difficult. Finance is not a hard science and no statistical pattern appears with certainty and perfect regularity in the data. However, it has been known for a long time that financial returns show some stylized facts. Stylized facts are statistical patterns that tend to repeat in the data, for different financial instruments (stocks, indices, etc.) and markets, frequently but without certainty as they may be unobserved in some periods or under some extreme market conditions. Were the first to empirically question the Gaussian random walk hypothesis for prices, bringing to light various statistical properties of asset returns. Their studies paved the way to intensive empirical works trying to exhibit statistical regularities common across a wide range of financial data sets, as presented in clearly stating that there is a trade-off between the potential universality of the qualitative qualification and the quantitative precision when characterizing stylized facts).

Keywords

Computing returns; Discrete-time modeling; Expected return and covariance; Investment Universe; Modeling returns; Moment estimation; Return moments; Risk factors; Stylized facts; Time series properties

1.1 Introduction

With a variety of asset classes with different liquidities, constraints and characteristics, and a large number of investors with different investment horizons, speaking in general of financial returns is rather difficult. Finance is not a hard science and no statistical pattern appears with certainty and perfect regularity in the data. However, it has been known for a long time that financial returns show some stylized facts. Stylized facts are statistical patterns that tend to repeat in the data, for different financial instruments (stocks, indices, etc.) and markets, frequently but without certainty as they may be unobserved in some periods or under some extreme market conditions. [MAN 63] and [FAM 65] were the first to empirically question the Gaussian random walk hypothesis for prices, bringing to light various statistical properties of asset returns. Their studies paved the way to intensive empirical works trying to exhibit statistical regularities common across a wide range of financial data sets, as presented in [CON 01] and [TER 11] ([CON 01] clearly stating that there is a trade-off between the potential universality of the qualitative qualification and the quantitative precision when characterizing stylized facts).

1.1.1 On the importance of stylized facts

A first stylized fact is that financial returns are known to be non-Gaussian. The main drawback of the Gaussian assumption for returns is that it does not, in general, give a precise description of the tails of the return distribution. The non-Gaussianity of returns is a widely and deeply studied topic, in various aspects and details, for several asset classes. For instance, it is the main object of the book of [JON 07a], but other stylized facts are particularly striking. We recall here some of t...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Foreword
• Preface
• Introduction
• List of Acronyms
• 1: Understanding the Investment Universe
• 2: Dealing with Risk Factors
• 3: Active Portfolio Construction
• 4: Backtesting and Statistical Significance of Performance
• 5: Gauging Economic Influences on Quantitative Strategies
• Appendix
• Conclusion
• Bibliography
• Index