Portfolio Construction and Analytics E-Book

Frank J. Fabozzi

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Frank J. Fabozzi Series
• Sprache: Englisch

Seitenzahl: 983

Veröffentlichungsjahr: 2016

Beschreibung

A detailed, multi-disciplinary approach to investment analytics Portfolio Construction and Analytics provides an up-to-date 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 multi-disciplinary 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. Real-world 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 open-source software most commonly used * Gain a multi-angle 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.

Leseprobe

Table of Contents

The Frank J. Fabozzi Series

Title Page

Copyright

Dedication

Preface

Central Themes

Software

Teaching

Disclosure

About the Authors

Acknowledgments

Chapter 1: Introduction to Portfolio Management and Analytics

1.1 Asset Classes and the Asset Allocation Decision

1.2 The Portfolio Management Process

1.3 Traditional versus Quantitative Asset Management

1.4 Overview of Portfolio Analytics

1.5 Outline of Topics Covered in the Book

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

3.1 Examples of Probability Distributions

3.2 Modeling Financial Return Distributions

3.3 Modeling Tails of Financial Return Distributions

Chapter 4: Statistical Estimation Models

4.1 Commonly Used Return Estimation Models

4.2 Regression Analysis

4.3 Factor Analysis

4.4 Principal Components Analysis

4.5 Autoregressive Conditional Heteroscedastic Models

Part Two: Simulation and Optimization Modeling

Chapter 5: Simulation Modeling

5.1 Monte Carlo Simulation: A Simple Example

5.2 Why Use Simulation?

5.3 How Many Scenarios?

5.4 Random Number Generation

Chapter 6: Optimization Modeling

6.1 Optimization Formulations

6.2 Important Types of Optimization Problems

6.3 A Simple Optimization Problem Formulation Example: Portfolio Allocation

6.4 Optimization Algorithms

6.5 Optimization Software

6.6 A Software Implementation Example

Chapter 7: Optimization under Uncertainty

7.1 Dynamic Programming

7.2 Stochastic Programming

7.3 Robust Optimization

Part Three: Three Portfolio Theory

Chapter 8: Asset Diversification

8.1 The Case for Diversification

8.2 The Classical Mean-Variance Optimization Framework

8.3 Efficient Frontiers

8.4 Alternative Formulations of the Classical Mean-Variance Optimization Problem

8.5 The Capital Market Line

8.6 Expected Utility Theory

8.7 Diversification Redefined

Chapter 9: Factor Models

9.1 Factor Models in the Financial Economics Literature

9.2 Mean-Variance 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

10.1 Tracking Error versus Alpha: Calculation and Interpretation

10.2 Forward-Looking versus Backward-Looking Tracking Error

10.3 Tracking Error and Information Ratio

10.4 Predicted Tracking Error Calculation

10.5 Benchmarks and Indexes

10.6 Smart Beta Investing

Part Four: Equity Portfolio Management

Chapter 11: Advances in Quantitative Equity Portfolio Management

11.1 Portfolio Constraints Commonly Used in Practice

11.2 Portfolio Optimization with Tail Risk Measures

11.3 Incorporating Transaction Costs

11.4 Multiaccount Optimization

11.5 Incorporating Taxes

11.6 Robust Parameter Estimation

11.7 Portfolio Resampling

11.8 Robust Portfolio Optimization

Chapter 12: Factor-Based Equity Portfolio Construction and Performance Evaluation

12.1 Equity Factors Used in Practice

12.2 Stock Screens

12.3 Portfolio Selection

12.4 Risk Decomposition

12.5 Stress Testing

12.6 Portfolio Performance Evaluation

12.7 Risk Forecasts and Simulation

Part Five: Fixed Income Portfolio Management

Chapter 13: Fundamentals of Fixed Income Portfolio Management

13.1 Fixed Income Instruments and Major Sectors of the Bond Market

13.2 Features of Fixed Income Securities

13.3 Major Risks Associated with Investing in Bonds

13.4 Fixed Income Analytics

13.5 The Spectrum of Fixed Income Portfolio Strategies

13.6 Value-Added Fixed Income Strategies

Chapter 14: Factor-Based Fixed Income Portfolio Construction and Evaluation

14.1 Fixed Income Factors Used in Practice

14.2 Portfolio Selection

14.3 Risk Decomposition

Chapter 15: Constructing Liability-Driven Portfolios

15.1 Risks Associated with Liabilities

15.2 Liability-Driven Strategies of Life Insurance Companies

15.3 Liability-Driven Strategies of Defined Benefit Pension Funds

Part Six: Derivatives and Their Application to Portfolio Management

Chapter 16: Basics of Financial Derivatives

16.1 Overview of the Use of Derivatives in Portfolio Management

16.2 Forward and Futures Contracts

16.3 Options

16.4 Swaps

Chapter 17: Using Derivatives in Equity Portfolio Management

17.1 Stock Index Futures and Portfolio Management Applications

17.2 Equity Options and Portfolio Management Applications

17.3 Equity Swaps

Chapter 18: Using Derivatives in Fixed Income Portfolio Management

18.1 Controlling Interest Rate Risk Using Treasury Futures

18.2 Controlling Interest Rate Risk Using Treasury Futures Options

18.3 Controlling Interest Rate Risk Using Interest Rate Swaps

18.4 Controlling Credit Risk with Credit Default Swaps

Appendix: Basic Linear Algebra Concepts

A.1 Systems of Equations

A.2 Vectors and Matrices

A.3 Matrix Algebra

A.4 Important Definitions

References

Index

End User License Agreement

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Guide

Table of Contents

Begin Reading

List of Exhibits

Chapter 1: Introduction to Portfolio Management and Analytics

Exhibit 1.1 A typical system for quantitative investment management.

Chapter 2: Random Variables, Probability Distributions, and Important Statistical Concepts

Exhibit 2.1 Bernoulli distribution with

p

= 0.3.

Exhibit 2.2 Movement of stock's value over three years. “1” = “success” (stock's value goes up). “0” = “failure” (stock's value goes down). Note: At the end of three years, we can have three successes in a row, two successes out of three, one success out of three, or no successes. The (0,1) combinations in the tree show the order of the successes or the failures up to that point in time.

Exhibit 2.3 Binomial distribution with

n

= 3 trials and

p

= 0.30 probability of success.

Exhibit 2.4 Shape of binomial distributions with the same number of trials and different values for the probability of success

p

: (a) ; (b) ; (c) .

Exhibit 2.5 Shape of binomial distributions with the same probability of success

p

= 0.3 and an increasing number of trials

n

: (a) ; (b) ; (c) .

Exhibit 2.6 Standard normal distribution.

Exhibit 2.7 The CDF of (a) a continuous random variable (lognormal), and (b) a discrete random variable (binomial with 10 trials and probability of success 0.30). Note: The values on the horizontal axis are the values the random variable takes.

Exhibit 2.8 Calculation of the probability that the random variable falls between 20 and 60 as a difference of cumulative probabilities up to 20 and 60.

Exhibit 2.9 Comparison of two probability distributions in terms of risk and central tendency.

Exhibit 2.10 (a) 95% VaR computed from P/L data; (b) 95% VaR computed from L/P data.

Exhibit 2.11 A connected graph of the realizations of versus the realizations of .

Exhibit 2.12 Sum (convolution) of two uniform random variables.

Exhibit 2.13 Joint (scatterplot) and marginal (histograms) distributions obtained from 1,000 realizations of normal random variable with mean 100 and standard deviation 30 and normal variable with mean 50 and standard deviation 10.

Exhibit 2.14 PDFs of multivariate probability distributions obtained from two standard normal marginals and four different types of copula functions: (a) Normal copula, (b) Clayton copula, (c) Frank copula, and (d) Gumbel copula.

Exhibit 2.15 Contours of multivariate probability distributions obtained from two standard normal marginals and four different types of copula functions: (a) Normal copula, (b) Clayton copula, (c) Frank copula, and (d) Gumbel copula.

Exhibit 2.16 PDFs of (a) the Normal copula and (b) the Clayton copula.

Chapter 3: Important Probability Distributions

Exhibit 3.1 PMF of a discrete uniform distribution.

Exhibit 3.2 (a) Continuous uniform distribution on [3,3.5] and (b) Standard continuous uniform distribution.

Exhibit 3.3 Examples of PDFs of

t

distributions for different degrees of freedom.

Exhibit 3.4 PDF of a lognormal distribution.

Exhibit 3.5 PMF of a Poisson distribution (with λ = 5).

Exhibit 3.6 PDF of an exponential distribution (with λ = 0.2).

Exhibit 3.7 PDF of a chi-square distribution (with 5 degrees of freedom).

Exhibit 3.8 The PDF of the gamma distribution for different values of the parameters

k

and

θ

.

Exhibit 3.9 PDFs of examples of beta distributions with values for shape parameters equal to (a) 4 and 6 and (b) 6 and 4.

Exhibit 3.10 An example of a bivariate elliptic distribution: simulated values for a bivariate normal distribution (in the scatterplot), a level curve at 0 (appears as an ellipse), and the marginal distributions for the two random variables (in this example, standard normal distributions).

Exhibit 3.11 Comparison of the PDF of the standard Cauchy distribution and the PDF of the standard normal distribution.

Exhibit 3.12 The PDFs of the standard forms (shape parameter

δ

= 0, scale parameter

θ

= 1) of the three types of extreme value distributions.

Exhibit 3.13 PDFs for Generalized Pareto distribution.

Chapter 4: Statistical Estimation Models

Exhibit 4.1 Determining a linear relationship between returns on a market index (the S&P 500) and returns on P&G stock.

Exhibit 4.2 Excel regression output for the P&G example.

Exhibit 4.3 Ten stock return processes.

Exhibit 4.4 Principal components.

Exhibit 4.5 Standard deviations of the 10 principal components.

Exhibit 4.6 Scree plot.

Exhibit 4.7 Representation of the original data for the 10 stocks (78 data points, one for each time period) in terms of the first two principal components.

Chapter 5: Simulation Modeling

Exhibit 5.1 A typical Monte Carlo simulation system.

Exhibit 5.2 Histogram and summary statistics for the end-of-year distribution of 100 simulated values for $1,000 invested at the beginning of the year.

Exhibit 5.3 Output distribution for amount of capital after 30 years.

Exhibit 5.4 Histogram and summary statistics of the capital after 30 years from investing in the S&P 500 and Treasury bonds, taking into account the correlation between the returns on stocks and bonds.

Exhibit 5.5 Comparison of Strategy A (equal allocation to stocks and bonds, in dark gray) and Strategy B (allocation of 30% to stocks and 70% to bonds, in light gray).

Exhibit 5.6 Histogram and summary statistics for the difference between the capital at the end of 30 years with Strategy A and with Strategy B.

Chapter 6: Optimization Modeling

Exhibit 6.1 An example of the optimal objective function values for and for a quadratic objective function of a single decision variable

x

.

Exhibit 6.2 Global (point A) versus local (point B) minimum for a function of two variables

x

1

and

x

2

.

Exhibit 6.3 Examples of (a) a convex function; (b) a concave function.

Exhibit 6.4 Data for the portfolio manager's problem.

Exhibit 6.5 Excel Solver dialog box.

Exhibit 6.6 Solver constraint dialog box.

Exhibit 6.7 Excel Solver Options dialog box.

Exhibit 6.8 Snapshot of an Excel model of the portfolio allocation example from Section 6.3.

Exhibit 6.9 Excel Solver inputs for the portfolio allocation problem.

Chapter 7: Optimization under Uncertainty

Exhibit 7.1 A simplified example of a scenario tree.

Exhibit 7.2 Scenarios for the returns of the four funds.

Exhibit 7.3 (a) A “box” uncertainty set in three dimensions; (b) an ellipsoidal uncertainty set in three dimensions.

Chapter 8: Asset Diversification

Exhibit 8.1 (a) Change in portfolio expected return as the fraction invested in Stock 1 increases from 0 to 1; (b) change in portfolio standard deviation as the fraction invested in Stock 1 increases from 0 to 1.

Exhibit 8.2 Mean and standard deviation for the returns of equally weighted portfolios of S&P 500 stocks picked at random. Note: The random selection started with a portfolio of a single stock (Caterpillar) followed by a portfolio of three stocks (Caterpillar, Boeing, and AT&T) and ended with a portfolio of 25 stocks. The portfolio mean and standard deviation were computed based on 12 monthly returns for each stock included in the portfolios between January 2013 and December 2013. The graph illustrates the decrease in the realized standard deviation of an equally weighted portfolio as the number of stocks in the portfolio increases.

Exhibit 8.3 (a) Possible pairings of portfolio expected return and standard deviation as the weights of the two stocks vary between 0 and 1; (b) portfolio efficient frontier.

Exhibit 8.4 Feasible and mean-variance efficient portfolios.

Exhibit 8.5 Capital market line.

Exhibit 8.6 Examples of different utility functions.

Chapter 9: Factor Models

Exhibit 9.1 An example of breaking down asset classes into factors.

Exhibit 9.2 Correlations of major factors that explain asset class returns and co-movements. Correlations measured over the period August 1988 through September 2014.

Chapter 10: Benchmarks and the Use of Tracking Error in Portfolio Construction

Exhibit 10.1 Data and calculation of tracking error.

Exhibit 10.2 Illustration of the effect of leverage on investment risk and return.

Chapter 11: Advances in Quantitative Equity Portfolio Management

Exhibit 11.1 An example of modeling transaction costs (TC) as a piecewise-linear function of trade size

t

.

Chapter 12: Factor-Based Equity Portfolio Construction and Performance Evaluation

Exhibit 12.1 Example of stratification with two factors (industry and P/E ratio).

Exhibit 12.2 Portfolio active risk decomposition.

Exhibit 12.3 Risk decomposition, style factors.

Exhibit 12.4 Exposure analysis of style factors.

Exhibit 12.5 Risk decomposition, sector by market cap (in billions of dollars).

Exhibit 12.6 Risk decomposition, sector, and industry detail.

Exhibit 12.7 Risk decomposition, asset detail.

Exhibit 12.8 Raw factor exposures.

Exhibit 12.9 Firm-wide security exposure.

Exhibit 12.10 Stress testing.

Exhibit 12.11 Risk-based performance attribution (total).

Exhibit 12.12 Risk performance attribution (sectors).

Exhibit 12.13 Factor attribution—Style factors.

Exhibit 12.14 Factor attribution—Growth.

Exhibit 12.15 Factor returns (historical performance).

Exhibit 12.16 Simulated value-at-risk for the portfolio example from Section 12.4.

Exhibit 12.17 Simulated value-at-risk under current and “extreme” (October 2008) market conditions for the portfolio example from Section 12.4.

Chapter 13: Fundamentals of Fixed Income Portfolio Management

Exhibit 13.1 An upward-sloping yield curve.

Exhibit 13.2 Relationship between bond price and interest rate.

Exhibit 13.3 Spectrum of bond portfolio management strategies.

Chapter 14: Factor-Based Fixed Income Portfolio Construction and Evaluation

Exhibit 14.1 Example of stratification with two factors (credit quality and effective duration).

Exhibit 14.2 Example portfolio as of April 24, 2015.

Exhibit 14.3 Portfolio and benchmark sector allocation.

Exhibit 14.4 Optimal set of 15 trades for portfolio rebalancing.

Exhibit 14.5 Summary report for the 50-security portfolio.

Exhibit 14.6 Systematic and idiosyncratic monthly tracking error for the 50-securities portfolio by asset class.

Exhibit 14.7 Isolated monthly tracking error and liquidation effect for the 50-securities portfolio by sector.

Exhibit 14.8 Comparison of contributions to duration by asset class for the 50-securities portfolio and the benchmark.

Exhibit 14.9 Monthly tracking errors for risk factors.

Exhibit 14.10 Treasury curve risk for the 50-securities portfolio.

Exhibit 14.11 Swap spread risk for the 50-securities portfolio.

Chapter 15: Constructing Liability-Driven Portfolios

Exhibit 15.1 Current yield curve, discount factors, cash flows from bonds 1 and 2, as well as present values of cash flows from the two bonds.

Exhibit 15.2 Effect of shift of Δ

y

= 25 bp on bond prices.

Exhibit 15.3 Immunization results.

Exhibit 15.4 Immunization risk.

Exhibit 15.5 Cash-flow matching example data.

Exhibit 15.6 Excel spreadsheet set up for the cash-flow matching problem.

Exhibit 15.7 Excel Solver dialog box for the cash-flow matching problem.

Chapter 16: Basics of Financial Derivatives

Exhibit 16.1 Profit/loss profile at expiration of a long call position and a long position in asset U.

Exhibit 16.2 Profit/loss profile at expiration for a short call position and a long call position.

Exhibit 16.3 Profit/loss profile at expiration for a long put position and a short position in asset U.

Exhibit 16.4 Profit/loss profile at expiration for a short put position and a long put position.

Exhibit 16.5 Weekly return distribution of the S&P 500 (March 2009 through March 2014) with a normal distribution with the same mean and standard deviation superimposed for comparison.

Exhibit 16.6 Probability distribution of (a) the price of a stock at expiration, and (b) the payoff of a call option on the stock at expiration.

Exhibit 16.7 (a) Simulated profit/loss distribution for a portfolio containing one share of Microsoft stock assuming the current stock price is $47.29; (b) profit/loss distribution for a portfolio containing one share of Microsoft stock and one put with strike price of $45 and price of $1.07 assuming the current stock price is $47.29.

Exhibit 16.8 Summary of factors that affect the price of a European option.

Exhibit 16.9 One-period binomial tree with values for an asset, payoffs for the call option, and the value of a portfolio consisting of a long position in Δ units of an asset and a short position in the option.

Exhibit 16.10 Pricing of a European call option using a two-period binomial tree.

Exhibit 16.11 Pricing a European put option with a two-period binomial tree.

Exhibit 16.12 Prices of European call and put options for different values of the time to maturity

T

and the volatility

σ

.

Exhibit 16.13 Volatility smile for in-the-money call options.

Chapter 17: Using Derivatives in Equity Portfolio Management

Exhibit 17.1 Comparison of portfolio value from purchasing stocks to replicate an index and a futures/Treasury bill strategy when the futures contract is fairly priced.

Assumptions

: (1) amount to be invested = $90 million; (2) current value of S&P 500 = 1200; (3) current value of S&P futures contract = 1212; (4) expected dividend yield = 2%; (5) yield on Treasury bills = 3%; (6) number of S&P 500 contracts to be purchased = 300

Exhibit 17.2 Payoff at expiration of a protective put strategy.

Exhibit 17.3 Payoff at expiration of a collar strategy.

Exhibit 17.4 Payoff at expiration of a covered call strategy.

The Frank J. Fabozzi Series

Fixed Income Securities, Second Edition by Frank J. Fabozzi

Focus on Value: A Corporate and Investor Guide to Wealth Creation by James L. Grant and James A. Abate

Handbook of Global Fixed Income Calculations by Dragomir Krgin

Managing a Corporate Bond Portfolio by Leland E. Crabbe and Frank J. Fabozzi

Real Options and Option-Embedded Securities by William T. Moore

Capital Budgeting: Theory and Practice by Pamela P. Peterson and Frank J. Fabozzi

The Exchange-Traded Funds Manual by Gary L. Gastineau

Professional Perspectives on Fixed Income Portfolio Management, Volume 3 edited by Frank J. Fabozzi

Investing in Emerging Fixed Income Markets edited by Frank J. Fabozzi and Efstathia Pilarinu

Handbook of Alternative Assets by Mark J. P. Anson

The Global Money Markets by Frank J. Fabozzi, Steven V. Mann, and Moorad Choudhry

The Handbook of Financial Instruments edited by Frank J. Fabozzi

Interest Rate, Term Structure, and Valuation Modeling edited by Frank J. Fabozzi

Investment Performance Measurement by Bruce J. Feibel

The Handbook of Equity Style Management edited by T. Daniel Coggin and Frank J. Fabozzi

The Theory and Practice of Investment Management edited by Frank J. Fabozzi and Harry M. Markowitz

Foundations of Economic Value Added, Second Edition by James L. Grant

Financial Management and Analysis, Second Edition by Frank J. Fabozzi and Pamela P. Peterson

Measuring and Controlling Interest Rate and Credit Risk, Second Edition by Frank J. Fabozzi, Steven V. Mann, and Moorad Choudhry

Professional Perspectives on Fixed Income Portfolio Management, Volume 4 edited by Frank J. Fabozzi

The Handbook of European Fixed Income Securities edited by Frank J. Fabozzi and Moorad Choudhry

The Handbook of European Structured Financial Products edited by Frank J. Fabozzi and Moorad Choudhry

The Mathematics of Financial Modeling and Investment Management by Sergio M. Focardi and Frank J. Fabozzi

Short Selling: Strategies, Risks, and Rewards edited by Frank J. Fabozzi

The Real Estate Investment Handbook by G. Timothy Haight and Daniel Singer

Market Neutral Strategies edited by Bruce I. Jacobs and Kenneth N. Levy

Securities Finance: Securities Lending and Repurchase Agreements edited by Frank J. Fabozzi and Steven V. Mann

Fat-Tailed and Skewed Asset Return Distributions by Svetlozar T. Rachev, Christian Menn, and Frank J. Fabozzi

Financial Modeling of the Equity Market: From CAPM to Cointegration by Frank J. Fabozzi, Sergio M. Focardi, and Petter N. Kolm

Advanced Bond Portfolio Management: Best Practices in Modeling and Strategies edited by Frank J. Fabozzi, Lionel Martellini, and Philippe Priaulet

Analysis of Financial Statements, Second Edition by Pamela P. Peterson and Frank J. Fabozzi

Collateralized Debt Obligations: Structures and Analysis, Second Edition by Douglas J. Lucas, Laurie S. Goodman, and Frank J. Fabozzi

Handbook of Alternative Assets, Second Edition by Mark J. P. Anson

Introduction to Structured Finance by Frank J. Fabozzi, Henry A. Davis, and Moorad Choudhry

Financial Econometrics by Svetlozar T. Rachev, Stefan Mittnik, Frank J. Fabozzi, Sergio M. Focardi, and Teo Jasic

Developments in Collateralized Debt Obligations: New Products and Insights by Douglas J. Lucas, Laurie S. Goodman, Frank J. Fabozzi, and Rebecca J. Manning

Robust Portfolio Optimization and Management by Frank J. Fabozzi, Peter N. Kolm, Dessislava A. Pachamanova, and Sergio M. Focardi

Advanced Stochastic Models, Risk Assessment, and Portfolio Optimizations by Svetlozar T. Rachev, Stogan V. Stoyanov, and Frank J. Fabozzi

How to Select Investment Managers and Evaluate Performance by G. Timothy Haight, Stephen O. Morrell, and Glenn E. Ross

Bayesian Methods in Finance by Svetlozar T. Rachev, John S. J. Hsu, Biliana S. Bagasheva, and Frank J. Fabozzi

Simulation and Optimization in Finance: Modeling with MATLAB, @RISK, or VBA + Website by Dessislava A. Pachamanova and Frank J. Fabozzi

The Handbook of Municipal Bonds edited by Sylvan G. Feldstein and Frank J. Fabozzi

Subprime Mortgage Credit Derivatives by Laurie S. Goodman, Shumin Li, Douglas J. Lucas, Thomas A Zimmerman, and Frank J. Fabozzi

Introduction to Securitization by Frank J. Fabozzi and Vinod Kothari

Structured Products and Related Credit Derivatives edited by Brian P. Lancaster, Glenn M. Schultz, and Frank J. Fabozzi

Handbook of Finance: Volume I: Financial Markets and Instruments edited by Frank J. Fabozzi

Handbook of Finance: Volume II: Financial Management and Asset Management edited by Frank J. Fabozzi

Handbook of Finance: Volume III: Valuation, Financial Modeling, and Quantitative Tools edited by Frank J. Fabozzi

Finance: Capital Markets, Financial Management, and Investment Management by Frank J. Fabozzi and Pamela Peterson-Drake

Active Private Equity Real Estate Strategy edited by David J. Lynn

Foundations and Applications of the Time Value of Money by Pamela Peterson-Drake and Frank J. Fabozzi

Leveraged Finance: Concepts, Methods, and Trading of High-Yield Bonds, Loans, and Derivatives by Stephen Antczak, Douglas Lucas, and Frank J. Fabozzi

Modern Financial Systems: Theory and Applications by Edwin Neave

Institutional Investment Management: Equity and Bond Portfolio Strategies and Applications by Frank J. Fabozzi

Robust Equity Portfolio Management + Website by Woo Chang Kim, Jang Ho Kim, and Frank J. Fabozzi

PortfolioConstructionand Analytics

DESSISLAVA A. PACHAMANOVAFRANK J. FABOZZI

Copyright © 2016 by Dessislava A. Pachamanova and Frank J. Fabozzi. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, [phone], fax [phone], or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, [phone], fax [phone], or online at http://www.wiley.com/go/permissions.

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Library of Congress Cataloging-in-Publication Data:

Names: Fabozzi, Frank J., author. | Pachamanova, Dessislava A., author.

Title: Portfolio construction and analytics / Frank J. Fabozzi, Dessislava Pachamanova.

Description: Hoboken, New Jersey : John Wiley & Sons, Inc., 2016 | Series: Frank J. Fabozzi series | Includes bibliographical references and index.

Identifiers: LCCN 2015040278 (print) | LCCN 2016003023 (ebook) | ISBN 9781118445594 (hardback) | ISBN 9781119238140 (ePub) | ISBN 9781119238164 (Adobe PDF)

Subjects: LCSH: Portfolio management. | BISAC: BUSINESS & ECONOMICS / Finance.

Classification: LCC HG4529.5 .F33456 2016 (print) | LCC HG4529.5 (ebook) | DDC 332.6—dc23

LC record available at http://lccn.loc.gov/2015040278

Cover Design: Wiley

Cover Image: © kentoh/Shutterstock

Dessislava A. PachamanovaTo my parents, Rositsa and AngelFrank J. FabozziTo my wife, Donna, and my children, Karly, Patricia, and Francesco

Preface

“Analytics” and “Big Data” have become buzzwords in many industries, and have dominated the news over the past few years. In finance, analytics and big data have been around for a long time, even if they were described with different terms. As J.R. Lowry, chief operating officer of State Street Global Exchange, stated in a 2014 interview published in the MIT Sloan Management Review, “In general, data and analytics have pervaded our business for many, many years, but it wasn't something that we were focused on in any kind of coherent way.”