Book description
A detailed, multidisciplinary approach to investment analyticsPortfolio Construction and Analytics provides an uptodate understanding of the analytic investment process for students and professionals alike. With complete and detailed coverage of portfolio analytics and modeling methods, this book is unique in its multidisciplinary approach. Investment analytics involves the input of a variety of areas, and this guide provides the perspective of data management, modeling, software resources, and investment strategy to give you a truly comprehensive understanding of how today's firms approach the process. Realworld examples provide insight into analytics performed with vendor software, and references to analytics performed with open source software will prove useful to both students and practitioners.
Portfolio analytics refers to all of the methods used to screen, model, track, and evaluate investments. Big data, regulatory change, and increasing risk is forcing a need for a more coherent approach to all aspects of investment analytics, and this book provides the strong foundation and critical skills you need.
 Master the fundamental modeling concepts and widely used analytics
 Learn the latest trends in risk metrics, modeling, and investment strategies
 Get up to speed on the vendor and opensource software most commonly used
 Gain a multiangle perspective on portfolio analytics at today's firms
Identifying investment opportunities, keeping portfolios aligned with investment objectives, and monitoring risk and performance are all major functions of an investment firm that relies heavily on analytics output. This reliance will only increase in the face of market changes and increased regulatory pressure, and practitioners need a deep understanding of the latest methods and models used to build a robust investment strategy. Portfolio Construction and Analytics is an invaluable resource for portfolio management in any capacity.
Table of contents
 The Frank J. Fabozzi Series
 Title Page
 Copyright
 Dedication
 Preface
 About the Authors
 Acknowledgments

Part One: Statistical Models of Risk and Uncertainty

Chapter 2: Random Variables, Probability Distributions, and Important Statistical Concepts
 2.1 What Is a Probability Distribution?
 2.2 The Bernoulli Probability Distribution and Probability Mass Functions
 2.3 The Binomial Probability Distribution and Discrete Distributions
 2.4 The Normal Distribution and Probability Density Functions
 2.5 The Concept of Cumulative Probability
 2.6 Describing Distributions
 2.7 Dependence between Two Random Variables: Covariance and Correlation
 2.8 Sums of Random Variables
 2.9 Joint Probability Distributions and Conditional Probability
 2.10 Copulas
 2.11 From Probability Theory to Statistical Measurement: Probability Distributions and Sampling
 Chapter 3: Important Probability Distributions
 Chapter 4: Statistical Estimation Models

Chapter 2: Random Variables, Probability Distributions, and Important Statistical Concepts
 Part Two: Simulation and Optimization Modeling

Part Three: Three Portfolio Theory
 Chapter 8: Asset Diversification

Chapter 9: Factor Models
 9.1 Factor Models in the Financial Economics Literature
 9.2 MeanVariance Optimization with Factor Models
 9.3 Factor Selection in Practice
 9.4 Factor Models for Alpha Construction
 9.5 Factor Models for Risk Estimation
 9.6 Data Management and Quality Issues
 9.7 Risk Decomposition, Risk Attribution, and Performance Attribution
 9.8 Factor Investing
 Chapter 10: Benchmarks and the Use of Tracking Error in Portfolio Construction
 Part Four: Equity Portfolio Management
 Part Five: Fixed Income Portfolio Management
 Part Six: Derivatives and Their Application to Portfolio Management
 Appendix: Basic Linear Algebra Concepts
 References
 End User License Agreement
Product information
 Title: Portfolio Construction and Analytics
 Author(s):
 Release date: April 2016
 Publisher(s): Wiley
 ISBN: 9781118445594
You might also like
book
Options, Futures, and Other Derivatives, Ninth Edition
For graduate courses in business, economics, financial mathematics, and financial engineering; for advanced undergraduate courses with …
book
Software Engineering at Google
Today, software engineers need to know not only how to program effectively but also how to …
book
Artificial Intelligence in Finance
Many industries have been revolutionized by the widespread adoption of AI and machine learning. The programmatic …
book
Python for Finance, 2nd Edition
The financial industry has recently adopted Python at a tremendous rate, with some of the largest …