sample level 3 wiley study guide vol 3

2 18 CFA® EXAM REVIEW W IL E Y Wiley Study Guide for 2018 Level III CFA Exam Review Complete Set Thousands of candidates from more than 100 countries have relied on these Study Guides to pass the CFA® Exam Covering every Learning Outcome Statement (LOS) on the exam, these review materials are an invaluable tool for anyone who wants a deep-dive review of all the concepts, formulas, and topics required to pass Wiley study materials are produced by expert CFA charterholders, CFA Institute members, and investment professionals from around the globe For more information, contact us at info @efficientleaming.com Wiley Study Guide for 2018 Level III CFA Exam Review Wi l ey Copyright © 2018 by John Wiley & Sons, Inc 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, 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http://booksupport.wiley.com For more information about Wiley products, visit www.wiley.com Required CFA® Institute disclaimer: “CFA® and Chartered Financial Analyst® are trademarks owned by CFA Institute CFA Institute (formerly the Association for Investment Management and Research) does not endorse, promote, review or warrant the accuracy of the products or services offered by John Wiley & Sons, Inc.” Certain materials contained within this text are the copyrighted property of CFA Institute The following is the copyright disclosure for these materials: “Copyright 2016, CFA Institute Reproduced and republished with permission from CFA Institute All rights reserved.” These materials may not be copied without written permission from the author The unauthorized duplication of these notes is a violation of global copyright laws and the CFA Institute Code of Ethics Your assistance in pursuing potential violators of this law is greatly appreciated Disclaimer: John Wiley & Sons, Inc.’s study materials should be used in conjunction with the original readings as set forth by CFA Institute in the 2017 CFA Level III Curriculum The information contained in this book covers topics contained in the readings referenced by CFA Institute and is believed to be accurate However, their accuracy cannot be guaranteed ISBN 978-1-119-43611-9 (ePub) ISBN 978-1-119-43610-2 (ePDF) Contents About the Authors xi Wiley Study Guide for 2018 Level III CFA Exam Volume 1: Ethical and Professional Standards & Behavioral Finance Study Session 1: Code of Ethics and Standards of Professional Conduct Reading 1: Code of Ethics and Standards of Professional Conduct Lesson 1: Code of Ethics and Standards of Professional Conduct Reading 2: Guidance for Standards l-VII Lesson 1: Standard I: Professionalism Lesson 2: Standard II: Integrity of Capital Markets Lesson 3: Standard III: Duties to Clients Lesson 4: Standard IV: Duties to Employers Lesson 5: Standard V: Investment Analysis, Recommendations, and Actions Lesson 6: Standard VI: Conflicts of Interest Lesson 7: Standard VII: Responsibilities as a CFA Institute Member or CFA Candidate 3 9 36 46 70 84 97 107 Study Session 2: Ethical and Professional Standards in Practice Reading 3: Application of the Code and Standards Lesson 1: Ethical and Professional Standards in Practice, Part 1: The Consultant Lesson 2: Ethical and Professional Standards in Practice, Part 2: Pearl Investment Management Reading 4: Asset Manager Code of Professional Conduct Lesson 1: Asset Manager Code of Professional Conduct 119 119 120 121 121 Study Session 3: Behavioral Finance Reading 5: The Behavioral Finance Perspective Lesson 1: Behavioral versus Traditional Perspectives Lesson 2: Decision Making Lesson 3: Perspectives on Market Behavior and Portfolio Construction 131 131 136 140 Reading 6: The Behavioral Biases of Individuals Lesson 1: Cognitive Biases Lesson 2: Emotional Biases Lesson 3: Investment Policy and Asset Allocation 147 148 154 159 © W iley © CONTENTS Reading 7: Behavioral Finance and Investment Processes Lesson 1:The Uses and Limitations of Classifying Investors into Types Lesson 2: How Behavioral Factors Affect Advisor-Client Relations Lesson 3: How Behavioral Factors Affect Portfolio Construction Lesson 4: Behavioral Finance and Analyst Forecasts Lesson 5: How Behavioral Factors Affect Committee Decision Making Lesson 6: How Behavioral Finance Influences Market Behavior 165 165 168 169 172 178 179 Wiley Study Guide for 2018 Level III CFA Exam Volume 2: Private Wealth Management & Institutional Investors Study Session 4: Private Wealth Management (1) Reading 8: Managing Individual Investor Portfolios Lesson 1: Investor Characteristics: Situational and Psychological Profiling Lesson 2: Individual IPS: Return Objective Calculation Lesson 3: Individual IPS: Risk Objective Lesson 4: Individual IPS: The Five Constraints Lesson 5: A Complete Individual IPS Lesson 6: Asset Allocation Concepts: The Process of Elimination Lesson 7: Monte Carlo Simulation and Personal Retirement Planning Reading 9: Taxes and Private Wealth Management in a Global Context Lesson 1: Overview of Global Income Tax Structures Lesson 2: After-Tax Accumulations and Returns forTaxable Accounts Lesson 3: Types of Investment Accounts and Taxes and Investment Risk Lesson 4: Implications for Wealth Management Reading 10: Domestic Estate Planning: Some Basic Concepts Lesson 1: Basic Estate Planning Concepts Lesson 2: Core Capital and Excess Capital Lesson 3: Transferring Excess Capital Lesson 4: Estate Planning Tools Lesson 5: Cross-Border Estate Planning 3 10 18 20 21 21 23 31 34 39 39 42 46 51 53 Study Session 5: Private Wealth Management (2) © Reading 11: Concentrated Single-Asset Positions Lesson 1: Concentrated Single-Asset Positions: Overview and Investment Risks Lesson 2: General Principles of Managing Concentrated Single-Asset Positions Lesson 3: Managing the Risk of Concentrated Single-Stock Positions Lesson 4: Managing the Risk of Private Business Equity Lesson 5: Managing the Risk of Investment in Real Estate 59 59 60 66 71 74 Reading 12: Risk Management for Individuals Lesson 1: Human Capital and Financial Capital Lesson 2: Seven Financial Stages of Life Lesson 3: A Framework for Individual Risk Management Lesson 4: Life Insurance Lesson 5: Other Types of Insurance Lesson 6: Annuities Lesson 7: Implementation of Risk Management for Individuals 77 77 78 80 83 88 91 95 © W iley CONTENTS Study Session 6: Portfilio Management for Institutional Investors Reading 13: Managing Institutional Investor Portfolios Lesson 1: Institutional IPS: Defined Benefit (DB) Pension Plans Lesson 2: Institutional IPS: Foundations Lesson 3: Institutional IPS: Endowments Lesson 4: Institutional IPS: Life Insurance and Non-Life Insurance Companies (Property and Casualty) Lesson 5: Institutional IPS: Banks 103 103 111 115 117 120 Wiley Study Guide for 2018 Level III CFA Exam Volume 3: Economic Analysis, Asset Allocation, Equity & Fixed Income Portfolio Management Study Session 7: Applications of Economic Analysis to Portfolio Management Reading 14: Capital Market Expectations Lesson 1: Organizing the Task: Framework and Challenges Lesson 2: Tools for Formulating Capital Market Expectations,Part 1: Formal Tools Lesson 3: Tools for Formulating Capital Market Expectations,Part 2: Survey and Panel Methods and Judgment 13 Lesson 4: Economic Analysis, Part 1: Introduction and Business Cycle Analysis 19 Lesson 5: Economic Analysis, Part 2: Economic Growth Trends, Exogenous Shocks, and International Interactions 27 Lesson 6: Economic Analysis, Part 3: Economic Forecasting 30 Lesson 7: Economic Analysis, Part 4: Asset Class Returns andForeign Exchange Forecasting 33 Reading 15: Equity Market Valuation 39 Lesson 1: Estimating a Justified P/E Ratio and Top-Down and Bottom-Up Forecasting 39 Lesson 2: Relative Value Models 46 Study Session 8: Asset Allocation and Related Decisions in Portfolio Management (1) Reading 16: Introduction to Asset Allocation Lesson 1: Asset Allocation in the Portfolio Construction Process Lesson 2: The Economic Balance Sheet and Asset Allocation Lesson 3: Approaches to Asset Allocation Lesson 4: Strategic Asset Allocation Lesson 5: Implementation Choices Lesson 6: Strategic Considerations for Rebalancing Reading 17: Principles of Asset Allocation Lesson 1: The Traditional Mean-Variance Optimization (MVO) Approach Lesson 2: Monte Carlo Simulation and Risk Budgeting Lesson 3: Factor-Based Asset Allocation Lesson 4: Liability-Relative Asset Allocation Lesson 5: Goal-Based Asset Allocation, Heuristics, Other Approaches to Asset Allocation, and Portfolio Rebalancing 53 53 54 55 57 64 65 67 67 70 71 72 75 Study Session 9: Asset Allocation and Related Decisions in Portfolio Management (2) Reading 18: Asset Allocation with Real-World Constraints Lesson 1: Constraints in Asset Allocation Lesson 2: Asset Allocation for the Taxable Investor © W iley 81 81 84 CONTENTS Lesson 3: Altering or Deviating from the Policy Portfolio Lesson 4: Behavioral Biases in Asset Allocation Reading 19: Currency Management: An Introduction Lesson 1: Review of Foreign Exchange Concepts Lesson 2: Currency Risk and Portfolio Return and Risk Lesson 3: Currency Management: Strategic Decisions Lesson 4: Currency Management: Tactical Decisions Lesson 5: Tools of Currency Management Lesson 6: Currency Management for Emerging Market Currencies Reading 20: Market Indexes and Benchmarks Lesson 1: Distinguishing between a Benchmark and a Market Index and Benchmark Uses and Types Lesson 2: Market Index Uses and Types Lesson 3: Index Weighting Schemes: Advantages and Disadvantages 85 87 89 89 95 98 101 104 112 113 113 117 119 Study Session 10: Fixed-Income Portfolio Management (1) Reading 21: Introduction to Fixed-Income Portfolio Management Lesson 1: Roles of Fixed Income Securities in Portfolios Lesson 2: Fixed Income Mandates Lesson 3: Bond Market Liquidity Lesson 4: Components of Fixed Income Return Lesson 5: Leverage Lesson 6: Fixed Income Portfolio Taxation Reading 22: Liability-Driven and Index-Based Strategies Lesson 1: Liability-driven Investing Lesson 2: Managing Single and Multiple Liabilities Lesson 3: Risks in Managing a Liability Structure Lesson 4: Liability Bond Indexes Lesson 5: Alternative Passive Bond Investing Lesson 6: Liability Benchmarks Lesson 7: Laddered Bond Portfolios 127 127 129 133 135 137 140 143 143 144 147 148 148 149 149 Study Session 11: Fixed-Income Portfolio Management (2) Reading 23: Yield Curve Strategies Lesson 1: Foundational Concepts for Yield Curve Management Lesson 2: Yield Curve Strategies Lesson 3: Formulating a Portfolio Postioning Strategy for a Given Market View Lesson 4: A Framework for Evaluating Yield Curve Trades Reading 24: Fixed-Income Active Management: Credit Strategies Lesson 1: Investment-Grade and High-Yield Corporate Bond Portfolios Lesson 2: Credit Spreads Lesson 3: Credit Strategy Approaches Lesson 4: Liquidity Risk and Tail Risk in Credit Portfolios Lesson 5: International Credit Portfolios Lesson 6: Structured Financial Instruments 153 153 155 161 167 169 169 172 175 185 189 191 © W iley CAPITAL MARKET EXPECTATIONS Overconfidence bias is common among highly trained and specially skilled individuals who tend to overestimate the precision of their forecasts Analysts should consider a wider range of possible outcomes as a means to avoid this trap Prudence trap is the temptation to moderate conclusions so as to appear more conventional than the research itself indicates Unorthodox forecasts that are far outside consensus opinion can prove brilliant if they turn out to be accurate but humiliating if they fail to materialize Analysts can defend against this bias with rigorous research and allowing for a wider range of possible outcomes Recallability bias occurs when the research is heavily influenced by events that have left a lasting impression on the analyst, particularly catastrophic or dramatic events such as a market crash Grounding conclusions on objective data rather than on personal emotion minimizes the distortion and addresses this trap Example 1-1 Amy Cobourg is the fund manager of a small emerging market fund that invests in both large-cap and mid-cap stocks Cobourg has seen large gains in her personal portfolio from investments in Poland and is keen to take advantage of her knowledge of the region Currently, 25 percent of the portfolio is invested in the mining industry in Poland, which saw great returns the previous year due to a global boom in commodity prices Cobourg is forecasting an average 12 percent (±10 bps) return on investment for commodity stocks for the coming year Cobourg recently read an article from a highly regarded mining industry analyst who specializes in Polish companies The article suggests doubt and concern for the three largest businesses in the sector due to the author’s forecast of economic recession and lower prices Cobourg’s supervisor, John Curran, disagrees with the report and says that the three companies are in a good position to handle a downturn, which he believes will profit by a reduction in the supply of gold from South Africa due to a miner’s strike there Cobourg revises her forecast to an expected return of 11 percent, only slightly lower than her existing 12 percent expectation, believing that Poland will gain extra market share at South Africa’s expense Identify three psychological traps in forecasting, justifying your answer with one reason for each trap identified Solution: Overconfidence—Overestimating own knowledge of Polish stocks based on her own portfolio Possibly used too narrow of range (±10 bps) for her forecast Anchoring trap—Cobourg only slightly revised expectations to 11% from 12% despite the negative outlook in the article Confirming evidence trap—Ignores the negative outlook in the article but agrees with the effect of potential profit from Poland’s gain in market share at South Africa’s expense © W iley © CAPITAL MARKET EXPECTATIONS Model Risk Financial and economic models are abstract representations of markets They try to uncover the factors that influence the behavior of the variable being forecast However, as abstract representations, they are incomplete, providing only estimates of dependent variables A model used to forecast economic variables or asset returns has two sources of error First is the accuracy of the model inputs Data is often imperfect, or model inputs themselves must be estimated from other models The second source of error is the model itself To the extent that the abstract representation departs from reality, the model’s forecast will also deviate from the dependent variables’ actual value LESSON 2: TOOLS FOR FORMULATING CAPITAL MARKET EXPECTATIONS, PART 1: FORMAL TOOLS LOS 14c: Demonstrate the application of formal tools for setting capital market expectations, including statistical tools, discounted cash flow models, the risk premium approach, and financial equilibrium models Vol 3, pp 23^40 ANALYTICAL METHODS AND TOOLS Financial theory and practice provide a variety of tools and techniques for analysis and forecasting asset returns Like more menial tasks, the analyst must select the right tool for the right job In this section, we consider formal tools, which include statistical models, discounted cash flow models, and other quantitative techniques We also consider survey and consensus approaches Quantitative Tools: Statistical Methods Recall that there are two types of statistics: descriptive and inferential Descriptive statistics seek to organize and present data in meaningful ways Inferential statistics attempt to estimate or predict the characteristics of a population by looking at smaller samples Historical Averages and Estimators The simplest forecast looks solely at past data An analyst can compute the average return and variance of a sampled time series over a specific period If the distribution of the data is stable, the sample statistics might be good estimates of their future values There are, however, different methods of computing an average In finance, the most commonly used methods are the arithmetic average, which is best for an estimate at a single point in time, and the geometric average, which is best for averaging compound returns over time Shrinkage Estimation Shrinkage estimation is the weighted average of two estimates of a parameter based on the relative confidence the analyst has in using two methods For example, an analyst might use sample historical data to estimate a covariance matrix and an alternative method such as a factor model to estimate a second covariance matrix, called a target covariance matrix © W iley CAPITAL MARKET EXPECTATIONS Let’s consider that the estimated covariance between stocks and bonds is 24 using a factor model and 40 using a historical estimate, and assume that the optimal weights on the model and historical estimates are 0.80 and 0.20, respectively The shrinkage estimate of the covariance would be 0.80(24) + 0.20(40) = 27.2 In all cases, a shrinkage estimate using any target covariance matrix is a more efficient (or at least not less efficient) estimate than the historical average Example 2-1 Richard Ayoade is using a shrinkage estimator approach to estimating covariances between Mexican and Colombian equities He estimates that the covariance between Mexican and Colombian equities is 76 using historical data He also estimates the covariance as 64 using a factor model approach A Determine the shrinkage estimate of the covariance between Mexican and Colombian equities if the analyst has 80 percent confidence in the factor model approach B Contrast the quality of the shrinkage estimate of covariance versus the historical average alone Solutions: A 0.20(76) + 0.80(64) = 66.4 B The shrinkage estimator approach will lead to an increase in the efficiency of the covariance estimates versus the historical estimate We can also determine a shrinkage estimate for mean returns by taking a weighted average of historical mean return and some other target estimate, like the average mean of a group of assets For example, given five assets with sample mean returns of percent, 11 percent, 13 percent, 15 percent, and 19 percent, respectively, and a weight of 70 percent on the sample mean, we would calculate the grand mean return as 13 percent and the shrinkage estimate of the first asset’s return as 0.3(9%) + 0.7(13%) = 11.2% Time-Series Analysis Time-series estimators are based on regression using lagged variables, which are past values of the dependent variable For example, a model used to determine the short-term volatility in a variety of asset markets was developed at JPMorgan The model shows that the variance (a 2) in time t is dependent upon its value in the preceding period t — and the square of a random error term ef a? = p a ^ + (1 - p)e2 The larger the coefficient term, P, the greater influence the past variance (c^r_1) has on the forecasted variance (of ) This variance “memory” from one period to the next is called volatility clustering ã â W iley ã ã đ CAPITAL MARKET EXPECTATIONS Multifactor Regression Models Multifactor models provide asset return forecasts (R,) based on risk factors (Fk) that are thought to drive returns The risk factors represent the required return for assuming that particular source of risk The factor sensitivities (bik) are the regression coefficients that measure the degree to which the return is affected by a particular risk factor, or the asset’s exposure to that risk Multifactor models are also well suited for estimating covariances between asset class returns Quantitative Methods: Discounted Cash Flow Models Discounted cash flow (DCF) models are based on the fundamental premise that the value of any asset is the present value of its future cash flows DCF models estimate the intrinsic value of an asset The expected return on the asset is embedded in the relation between the asset’s intrinsic value and its current market price However, an expected return based on this intrinsic valuation approach is realized only when the market price converges to the intrinsic value, which can take a long time to happen For that reason, intrinsic value approaches are generally regarded as useful in setting long-term, strategic expectations as opposed to short-term, tactical expectations Dividend Discount Model (DDM) The Gordon (constant) growth dividend discount model is a widely recognized DCF model for estimating a stock’s intrinsic value The current price (P q) is determined by the next dividend [D\ = D q(1 + g)], discounted at the required return on common equity (re) adjusted for the estimated growth rate of dividends (g), which are assumed to grow at the same rate as earnings A ^ Do