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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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 © 2018 Wiley © 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 © 2018 Wiley 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 © 2018 Wiley 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 © 2018 Wiley BEHAVIORAL FINANCE AND INVESTMENT PROCESSES • • • focus on how the money will accomplish goals related to children’s education, retirement, family legacy, etc These clients tend to lose interest when presentations involve cognitive details such as Sharpe ratios and covariance data Friendly Followers—These investors may be difficult to advise because their cognitive biases may give them confidence that encourages them to accept greater risk than their regret aversion will actually allow Advisors should encourage these clients to provide solid data in support of their own investment recommendations, and educate them on portfolio diversification Independent Individualists—These investors are most likely to be contrarian, resist following a financial plan, and are comfortable investing and taking risks However, they will listen to advice if presented in a way that respects their intelligence Their predominantly cognitive biases can generally be changed through education, but this should be presented in a general sense rather than in the context of recent failures Active Accumulators—These investors are likely to have concentrated positions and high portfolio turnover rates This BIT may attempt to control the investment process based on overconfidence developed as the result of their former successes Advisors should instead control the process by communicating their ability to make wise decisions based on objective conclusions Otherwise, these clients may become the most difficult to control Limitations of BIT Classifications Classifying investors into different BITs has its limitations Namely: Investors may possess both cognitive and emotional biases Investors may display multiple characteristics of different personality types Investors may exhibit changes of different personality types as they age Investors must be treated as unique individuals even if they are in the same BIT classification Investors may be irrational and unpredictable from time to time In short, it’s up to the advisor to be aware of an investor’s unique complexities and develop an investment program that will achieve goals while maintaining the investor’s compliance LESSON 2: HOW BEHAVIORAL FACTORS AFFECT ADVISOR-CLIENT RELATIONS LOS 7b: Discuss how behavioral factors affect adviser-client interactions Vol 2, pp 117-120 Advisor-Client Relations By adding behavioral finance aspects to the Investor Policy Statement (IPS), an advisor can build a portfolio of investments to which the client and the advisor can adhere Although advisors may have trouble asking behavioral questions during the initial interaction with a client, they should include such information to better reach an outcome similar to that obtained using modem portfolio theory It is generally accepted that incorporating behavioral finance into the investment strategy enhances the advisor-client relationship 168 © W iley BEHAVIORAL FINANCE AND INVESTMENT PROCESSES Successful Investment Relationship Pompian (2006) suggested a few fundamental characteristics of a successful investment relationship Advisors must have a good understanding of their clients’ characteristics and financial goals when developing the investment policy statements It is important that advisors have a good grasp of their clients’ psychology and emotions when setting the financial goals with their clients By doing so, they build stronger bonds to produce results and outcomes that are fulfilling Advisors should use a consistent approach to advising their clients Incorporating behavioral finance as part of the discipline in advising clients provides a systematic way to better understand and serve the clients Advisors should demonstrate a good discipline to invest as expected by their clients They should maintain a fluid and regular communication with clients on results and have their clients’ characteristics in mind This is perhaps the most essential tool to build a successful advisor-client relationship Both the advisor and client acknowledge a beneficial relationship To make an effort to “get inside the head” of the clients, advisors should understand the emotional aspects of their clients’ fears and motives, resulting in a more effective way to explain the design and constmction of a portfolio, which subsequently results in stronger relationships between both parties Limitations of Traditional Risk Tolerance Test In addition to ignoring behavioral issues, traditional risk questionnaires may result in different risk tolerance outcomes when the questions are stated with slight variations or when administered repeatedly to the same individual Carefully interpreting the results can also go a long way toward good advisor-client relationships For example, risk questionnaire results indicating a client’s willingness to accept a maximum loss should probably not result in a portfolio that actually allows the maximum loss In contrast with individual investors, institutional investors often see risk-return optimization as a cognitive process Individuals will often wish to change an optimized portfolio when risk has been determined solely on the basis of risk tolerance because of emotional biases Of course, an investment roadmap designed after considering behavioral factors will be easier for individuals to follow In any case, the investor’s biases should probably be reexamined along with other financial details at least annually LESSON 3: HOW BEHAVIORAL FACTORS AFFECT PORTFOLIO CONSTRUCTION LOS 7c: Discuss how behavioral factors influence portfolio construction Vol 2, pp 120-124 Portfolio Construction A number of studies examined investor behavior as participants in defined contribution (DC) plans such as 401(k) plans in the United States to see how investor portfolios deviate from portfolios corresponding to MPT © W iley 169 BEHAVIORAL FINANCE AND INVESTMENT PROCESSES Inertia Most investors exhibit status quo bias by maintaining an asset allocation through various life stages, even though theory would suggest their allocations would change over time This often occurs in spite of no cost associated with transactions resulting from allocation change If no allocation was selected, plan participants also neglected to change from the default option (usually money market or other very low-risk option) To counteract such inertia, plans with an “autopilot” strategy, such as target date fu n d s , allow asset selection and allocation to change automatically based upon the time to stated retirement The drawback of these kind of funds is that they not meet the needs of individual investors Example 3-1 Dana Peterson participates in a target date fund in her company’s defined contribution (DC) pension plan The asset allocation in the target date fund reflects very low-risk investments based on her stated retirement date five years from now Peterson has also made several successful investments in equity securities over the years and has very few emotional biases Peterson’s investments would suggest she has little chance of failing to meet her retirement goals Is Peterson’s low-risk asset allocation optimal for her personal situation? A No, because her target date fund does not account for the individual circumstance of Peterson and therefore does not consider her other investments B Yes, because it recognizes the short time horizon until retirement C Yes, because it offsets the higher risk of her self-directed personal investments Solution: A Her more aggressive, higher-returning investments would be more tax efficient in the retirement account with her lower returning, lower risk investments held outside the retirement account, assuming she has limits in investments that can be maintained in her 401(k) Naive Diversification DC plan investors with active allocations often exhibit framing bias and use of simple heuristics Investors allocating between an equity fund and a balanced fund (50% equity and 50% debt) used 1/n diversification (i.e., 50% to each fund) in spite of this, resulting in an over-allocation to equities (75% rather than 50%) Huberman and Liang (2006) argue, however, that investors use a conditional 1/n diversification when a plan offers multiple funds For example, if the fund offers seven funds, investors may choose some subset of those funds and then invest equally in that subset of the available funds Anecdotal evidence seems to indicate this is a type of regret aversion bias in which the investor does not want to invest in one or the other asset class entirely in case the other asset class should outperform © W iley BEHAVIORAL FINANCE AND INVESTMENT PROCESSES Concentration in Company Stock Many DC plans offer employees the option to invest in company stock in addition to other funds One study indicates that as much as one-third of 401(k) plan assets are in the employee’s company stock, while another one has up to 90 percent in employer’s stock Several reasons are offered to explain this phenomenon • • • • • Familiarity and overconfidence bias—Employees place too much confidence in their company and underestimate risk based on their familiarity Representativeness—Employees may believe that their company’s past success will continue indefinitely at the historical rate they use for their basis Framing and status quo effects—In plans where the employer matches with company stock (rather than cash), employers allocate nearly 10 percent more of their own funds This may result from the view that the company has a great deal of confidence in their stock (framing) Employees often not change their allocations, regardless of the performance prospects for their company stock, due to inertia (status quo bias) Loyalty to the employer—Employee ownership of company stock may present as a takeover defense because employees with voting power will be unlikely to accept acquisition by another company Financial incentives—Financial related possibilities, such as discounts to market share price, beneficial tax treatment, and restriction to sell, may contribute to employees’ preference for holding their company’s own shares Excessive Trading Investments in discount brokerage accounts trade at a much greater rate than the DC plans, which often suffer from inertia Investors in discount brokerage accounts often show disposition effects in which they sell winners too soon and hold on to losers, thus decreasing returns Holding losers may be evidence of regret aversion while excessive trading may result from overconfidence that they have the right answers on which stocks to trade Young males have been identified as trading the most and earning the lowest returns after trading expenses To some extent, traders may self-select out of DC accounts (which often limit trading) and into individual brokerage accounts, thus increasing the percentage of individual account trading in such studies Home Bias Empirical findings indicate that investors tend to hold a high proportion in assets from their home country even though MPT would suggest a much higher percentage should be invested in international securities This could result from familiarity similar to the company stock problem, but may also be related to availability, confirmation, endowment, status quo, and illusion of control biases It could also be related to information costs (more of a cognitive issue) © W iley BEHAVIORAL FINANCE AND INVESTMENT PROCESSES LOS 7d: Explain how behavioral finance can be applied to the process of portfolio construction Vol 2, pp 124-125 Behavioral Portfolios Behavioral portfolio theory describes how investors build portfolios in layered pyramids of investments corresponding to goals (a mental accounting bias), rather than as one great portfolio of all their assets They form these layers without regard to correlations among asset classes as suggested by modem portfolio theory This explains why investors fail to diversify internationally despite the benefits Thus, investors not have an integrated risk target, but have a different risk-return opportunity for each layer It makes sense to them that they can accept a greater risk for certain assets (e.g., in the “aspirational layer”) versus their emergency fund (e.g., savings account assets) Investment advisors must identify each of these mental accounts and determine a mean-variance optimized portfolio appropriate for each layer Exhibit 3-1: Structures of Mean-Variance and Behavioral Portfolios Mean-Variance Portfolio M ean-variance portfolios are constructed as a whole, and only the expected return and the variance of the entire portfolio matter, Covariance between assets is crucial in determination of the variance of the portfolio Behavioral Portfolio Behavioral portfolios are constructed not as a whole but layer by layer, where each layer is associated with a goal and is filed with securities that correspond to that goal Covariance between assets is overlooked Upside-Potential Layer (contains, for example, foreign stocks, aggressive growth funds, IPOs, lottery tickets) Downside-Potential Layer (contains, for example, T-bills, CDs, money market funds) Source: Statman (1999) LESSON 4: BEHAVIORAL FINANCE AND ANALYST FORECASTS LOS 7e: Discuss how behavioral factors affect analyst forecasts and recommend remedial actions for analyst biases Vol 2, pp 125-136 ANALYST FORECASTS IN THE CONTEXT OF BEHAVIORAL FINANCE Investment analysts—-just as analysts in any field—have the potential to make biased forecasts in spite of or, in some cases, because of their superior analytical abilities Analysts can improve their decisions and conclusions when they understand the limits of their knowledge 172 © W iley BEHAVIORAL FINANCE AND INVESTMENT PROCESSES Overconfidence Overconfidence bias is a manifestation of illusion o f knowledge (i.e., the analysts believe they know more than they do) and may be intensified with self-attribution bias (i.e., the analysts attribute successes to their own abilities and fault others for failures) Some studies find that analyst forecasts with 90 percent confidence intervals should only be incorrect 10 percent of the time, but turn out to be wrong closer to 40 percent of the time Causes Errors in analyst forecasts and overconfidence regarding their validity could result from focusing on company-specific factors without regard for the general economy and other information Stock analysts exhibit overconfidence regarding earnings estimates rather than price targets while strategists tend to be more overconfident than stock or industry analysts, especially when making contrarian pronouncements Usually the illusion of control bias stems from the fact that analysts believe more information can provide them an edge For instance, analysts may attribute the accuracy of their forecasts solely on additional information they acquire and that the outcome resembles the overall collected data This may be a type of representativeness bias, in which their conclusion is reflected in multiple data sources and an availability bias, in which analysts give more weight to readily available information Collecting too much information contributes to the illusion o f control and can lead to overconfidence in forecasting complex combinations of earnings forecasts, price targets, and recommendations Models that fit a particular data set may not perform well under different assumptions, and mathematical rigor can conceal the underlying assumption errors from the analyst The illusion of knowledge driving this overconfidence may result from the false sense of control after gathering a tremendous amount of information and failure to recognize the inherent risk in the market and the economy that can’t be removed Analysts should instead focus on more robust models rather than models that very closely replicate a particular data outcome Analysts and others may appear to exhibit self-attribution bias when they respond to financial incentives (i.e., appear to claim success for themselves to gain a reward), although this is not truly the emotional bias itself creating the appearance Self-attribution bias actually occurs as people try to preserve self-esteem while they protect themselves from and attempt to comprehend their failures Forecasts are evaluated in hindsight, and analysts and others may evaluate their work with hindsight bias, in which they see past events as having been predictable Analysts and strategists often selectively recall their own predictions as being more accurate Hindsight bias could be the result of both cognitive error and emotional bias as individuals combine both information with prior beliefs In addition, it tends to be more prevalent with ambiguous forecasts © W iley 173 BEHAVIORAL FINANCE AND INVESTMENT PROCESSES Example 4-1 Synergy Technologies Co., a public company, recently made a high profile acquisition with a stock swap Based on the company’s recent earnings an analyst has concluded that Synergy will continue its high rate of growth Before the analyst submits her report, she also reads an article from a small investment magazine that Synergy might be in talks with another company to work on a new technology venture The analyst concludes that the additional information adds validity to her report and feels more confident with her forecasted growth rate on Synergy Hence, she includes the additional information in her report The analyst’s forecast was most likely influenced by overconfidence resulting from: A illusion of control B framing C availability Solution: C The analyst was most likely influenced by the availability bias Newspaper coverage is often selective Also, the analyst might see the additional information confirms her forecast on the company’s growth rate, exhibiting a representative bias Remedies Prompt, well-structured feedback and rewards for accuracy can help encourage analysts to recalibrate their processes when necessary: • • • • • Prompt and accurate feedback helps provide an effective method to catch the analyst closest to the frame of mind in which the forecast was made and thereby reduce hindsight bias Both financial rewards and accountability encourage the right incentives for analysts to perform Effective stmcture can best be achieved when analysts have well-documented reasons for a judgment, because the analyst can assess each point leading to the judgment Numbers should be included where possible to render forecasts less ambiguous and therefore subject to revision in hindsight This extensive documentation should not, however, be used by analysts to promote overconfidence Forecasts can become better calibrated when feedback can be offered by superiors, systems, or colleagues and the analyst is directly accountable to superiors and clients Forecasts can also become better calibrated and overconfidence can be moderated if the process requires analysts to provide at least one reason why a conclusion might not materialize Analysts should make deliberate efforts to avoid small sample sizes and select only comparable data to avoid overconfidence bias Additional data that cannot be analyzed or compared should be omitted as they inevitably increase confidence and not necessarily accuracy, leading to an illusion of control Bayes’ formula can often decrease the risk of illusion of knowledge bias by incorporating new information The base case becomes less important than the process of acquiring a sequence of useful information A good example occurs when an analyst must overcome the assumption that all stocks are rising (a base case) in order to consider growth for a particular company’s earnings and stock price © W iley BEHAVIORAL FINANCE AND INVESTMENT PROCESSES Example 4-2 The chief investment officer (CIO) of a major investment firm has received a recommendation from an analyst to sell Tribeca Co and buy Duopoly, Inc The two firms have similar track records and are in the same industry, but the analyst believes Duopoly will outperform Tribeca The CIO believes there is a 75% chance switching costs will exceed return gains The analyst has had a 70% success rate of determining when to switch and when not to switch If the CIO follows the analyst’s recommendation, which of the following is closest to the likelihood that a switch to Duopoly will result in additional portfolio gains? A 18% B 25% C 44% Solution: C The probability of successfully switching in this case is 43.75% One way of solving this problem is to assume there are 1,000 outcomes, of which 250 (25% x 1,000) outcomes will be successful (return gains exceeds switching costs) and 750 (75% x 1,000) outcomes will be unsuccessful (switching costs exceed return gains) The analyst is expected to correctly identify 175 (70% x 250) of the successful switching opportunities The analyst will recognize 525 (70% x 750) of the 750 unsuccessful outcomes, but will wrongly predict the firm should switch 225 times (750 - 525) The following table summarizes this information: Switch No Switch Total Gains 175 75 250 Losses 225 525 750 Total 400 600 1,000 Because 175 of the 400 calls to switch will be successful, the analyst will have a 43.75% (175/400) chance of successfully switching and obtaining a net gain This can also be solved using Bayes’ formula, given the following definitions: P(A) = Switch would yield gains (250 / 1,000) = 0.25 P(B)= Analyst recommends a switch = (400 / 1,000) = 0.40 P(B | A) = Analyst recommends a switch that yields a gain [(175+ 525)/1,000] = 0.70 P (A 5) = P (A I* )XP(A) = Q-7QXQ-25 = 0.4375 P(B) 0.40 In summary, a well-structured evaluation process must be established to ensure quality and accurate analyst forecasts Evaluation should be systematic to ensure unambiguous conclusions, provide counterarguments, and document comparable data Providing analysts with regular, prompt, and accurate feedback, recognition by supervisors, and accountability are also important in the process © W iley 175 BEHAVIORAL FINANCE AND INVESTMENT PROCESSES External Influence and Company Management Company management often selects and presents information to portray its company in the best light Analysts, therefore, should be cautious that company management is similarly subject to behavioral biases Causes Company management may suffer from optimism based on overconfidence or illusion of control biases For example, management may give analysts a piece of information and frame it in such a way as to give it undue impact Analysts could be influenced by the management’s perception and, subsequently, produce a forecast that includes these biases An analyst with conservatism bias will then tend to give less weight to contradictory information because he already has the anchor and frame in place Management of a company with temporarily depressed earnings or earnings that have suffered from a one-time setback may offer pro forma earnings Such estimates may be overly optimistic Analysts should consider, along with their own ratio and analysis, whether such pronouncements affect growth rates, earnings quality (progression of earnings), and valuation in a way that could impact their estimates of growth and risk for the firm Remedies Analysts should maintain a systematic and disciplined approach to forming judgments and making forecasts They can improve accuracy by focusing on quantifiable relationships and measureable and comparable data of the firm rather than relying on unverifiable or merely descriptive insights Analysts should uncover underlying base rates, gather information, and frame issues appropriately Analyst Biases and Research Causes While many analyst errors may stem from cognitive biases, emotional biases can also creep into analyst forecasts For example, the analyst’s search for information may result in developing a story rather than an objective assessment of the material As additional information accumulates, confirmation bias will result in the analyst providing undue weight to information that supports the story Analysts should remember that the models will only be as good as the data Continued seeking of additional data to support one’s opinion is a typical confirmation bias Another example of this kind of behavioral bias is when an analyst adds seemly independent probabilities instead of multiplying them In this case, the analyst exhibits a conjunction fallacy Gambler’s fallacy refers to analysts believing random data patterns will revert to the longterm mean over a specified period Another form of this common bias is when gamblers or others believe a continuation of a recent trend This example is regarded as a hot hand fallacy Analysts, in addition, may place higher value on assets based on the emotional response associated with them In a form of endowment bias, investors can attribute stable companies to stable growth even if the underlying economy is performing poorly Analysts might also look for additional information to confirm their beliefs and disregard 176 © 2018 Wiley BEHAVIORAL FINANCE AND INVESTMENT PROCESSES information contradictory to their recommendations Some studies find that analysts tend to bias for growth stocks and against value stocks Under such circumstances, analysts suffer from representative bias because they fail to incorporate base rate and the macromarket in which the company operates Example 4-3 A portfolio manager (PM) advising a new client has suggested he begin moving money from risk-free assets to a riskier allocation based on the client’s risk tolerance as determined by a questionnaire The PM suggests there has been a 30% probability the market will fall in the following month and a much higher probability of an increase over any particular year The market has fallen 22 of the previous 24 months The PM believes the market should revert to the mean return at any time The PM’s best course of action is to: A reallocate to the riskier portfolio immediately B wait a couple of months for the market to get back some positive momentum C average into the market over several weeks or months to offset the volatility of an investment that could immediately result in losses Solution: C The PM’s belief that the market will “revert to the mean” and gains should begin any time cannot be justified on the basis of any objective facts His behavior is often referred to as a gambler’s fallacy The appropriate action is to perform some fundamental analysis to understand whether it is reasonable to advise his client to reallocate his assets Remedies Analysts should avoid forecast error and overconfidence by using trailing earnings to inform their opinions about management estimates of future earnings Gathering information and systematically analyzing it before drawing conclusions also helps to avoid emotional biases Analysts should attempt to assign probabilities to expected outcomes and use a Bayesian approach to combining probabilities Prompt feedback allows analysts to reevaluate their conclusions and gain experience that can be helpful in making future forecasts An analyst’s search for information should include looking for contradictory as well as supportive information A structured approach can help avoid story creation and incorporating evidence sequentially can lead to faster adaptation A good forecast will admit to missing information in the sequential chain, contradictory information, or alternate possibilities that could result in a different conclusion While intuitions and the process of reaching complex analytical results can often be difficult to communicate, documenting decisions leads to better ex post evaluation and helps avoid hindsight and other biases that can hold back an analyst’s growth In short, analysts can remedy forecasting errors based on biases by systematically evaluating previous forecasts, using consistent and reliable current data, researching for contrary positions, assigning probabilities, sequentially incorporating data, documenting the process, and seeking prompt feedback © 2018 Wiley © BEHAVIORAL FINANCE AND INVESTMENT PROCESSES LESSON 5: HOW BEHAVIORAL FACTORS AFFECT COMMITTEE DECISION MAKING LOS 7f: Discuss how behavioral factors affect investment committee decision making and recommend techniques for mitigating their effects Vol 2, pp 136-138 IMPACT OF BEHAVIORAL BIASES ON COMMITTEE DECISIONS Combining individuals having different knowledge, skills, and experiences into a group can lead to synergistically better outcomes Analyst stock recommendations can be made stronger with the help of a research committee, and investment decisions can be stronger when the board of trustees or other oversight body examines the asset allocation decisions and fund manager selection process, etc Nevertheless, individuals’ biases can, in return, influence committee decisions Social proof bias occurs when individuals wrongly accept, favor, and follow the judgment of their peers or group without fully considering the viewpoint As an example, a buy side analyst may moderate his views on a particular investment to correspond to his company’s internal portfolio positions Naturally, with the goal of reaching consensus, group decisions limit the range of viewpoints As such, if a group fails to allow individuals opportunity to adequately present opposing opinions it loses the advantage of the collective wisdom, skill, and experience Committee Dynamics The dynamics of a committee may be such that those who attempt contrary opinions are beaten down This limits the committee’s range of potential decisions Normally, feedback would allow the committee to understand that limiting options doesn’t always work out so well But committees are notorious for failing to keep track of decisions in a way that allows them to learn from errors Continuously changing the makeup of a committee compounds the problem Additionally, committees are often comprised of members with similar backgrounds and ways of doing things This can lead to groupthink, the process of committee members moderating their own viewpoints toward the group consensus How to Structure and Operate Committees to Mitigate Biases Committees work best when diversity is promoted by including members with different backgrounds and experiences who are willing to think independently and express their opinions While diverse committee makeup may avoid groupthink, it can also lead to various management issues The chairman of a committee has the responsibility of not only assembling a diverse group of individuals that can express dissenting views, but for keeping such a group to the task at hand and formulating unambiguous conclusions, courses of action, and recommendations The chair should encourage contrary opinions and ensure professional respect among group members such that everyone feels comfortable voicing their opinions View suppression can be avoided to some extent if the chair solicits viewpoints prior to a meeting in which all will be expected to participate 178 © 2018 Wiley BEHAVIORAL FINANCE AND INVESTMENT PROCESSES LESSON 6: HOW BEHAVIORAL FINANCE INFLUENCES MARKET BEHAVIOR LOS 7g: Describe how behavioral biases of investors can lead to market characteristics that may not be explained by traditional finance Vol 2, pp 138-145 MARKET BEHAVIOR While the efficient market framework provides excellent analytical tools for determining what should happen in markets, there are various anomalies that persist Behavioral finance offers insight to help explain some of these anomalies Abnormal Markets Anomalies are defined as persistent abnormal market returns and are predictable in pattern However, most of the studies indicate abnormal returns dissipate after accounting for transaction fees and expenses Also, the definition of normal returns vastly depends on the choice of pricing models A reasonable change in calculating abnormal returns that causes an anomaly to disappear might suggest that it was a failure to adequately measure risk rather than an anomaly Empirical findings suggests that post 12-month positive returns following a stock split and low returns following an IPO fall into this category Other anomalies may be explained by data errors, such as small sample size, data mining, spurious correlations, or selection and survivorship biases The Super Bowl indicator to predict the market movement is a classic example of spurious correlations In general, an anomaly may just be nonsense if there is no logical reason for the correlation between the observation and results Temporary disequilibrium behavior may survive for a period of years and then suddenly disappear after attention has been called to the anomaly Lower stock market returns on Mondays have been observed over time and across continents, however, providing some evidence of a tme anomaly The January effect for small companies, on the other hand, disappears once risk is accounted for While some apparent anomalies are explainable as rational behavior (e.g., individual actions based on tax effects), this section focuses on anomalies that persist due to identifiable cognitive or emotional biases Momentum Globally, markets tend to exhibit behavior in which the current market returns are correlated to the previous market value for up to two years before reverting to the mean return Momentum investors may be making the rational decision to follow other investors, but the madness of crowds leads to fat tails (leptokurtosis) in which large market swings occur more frequently than indicated by a normal distribution This is more common for illiquid assets with infrequent trading, or asset classes in which FMPs suspect insider information may be at play Herding occurs when investors follow what other investors by trading on the same side of the market Herding is a form of cognitive dissonance avoidance behavior because investors irrationally ignore their own private information In other words, investors have an availability bias that causes them a conservatism bias It may also be related more © W iley 179 BEHAVIORAL FINANCE AND INVESTMENT PROCESSES to regret aversion when investors give up their own sources of information in favor of following the crowd, because they not wish to be regretful if other investors knew more than they did Momentum bias can also be explained partially by an overreaction to long-term information and underreaction to relevant information Often investors sell their winning stocks because they anchor on the purchase price even when the fundamentals suggest that the price could rise further A belief in mean reversion can cause investors to underreact to positive news, believing that share prices can’t keep going up forever, and overreact to negative news, believing that no good could possibly come from the company with such news Investors placing undue weight on recent events rather than fundamentals of the market reflects a recency effect, a form of availability bias Traders influenced by recency use too small a sample size to determine the likelihood of an outcome Individuals are more likely to be influenced by recent experience where professionals are more likely to expect reversion to the mean Regret, the feeling of missing an opportunity or an expression of hindsight bias, is especially painful during a volatile market or when people believe they should have known something about the market Hindsight bias occurs when people believe the past was predictable Regret aversion may lead to a trend-chasing effect when an investor missed a security’s major market move and trades into that same security in the hope of alleviating the regret This not only may result in lower returns from security selection, but increases transaction costs The disposition effect represents loss aversion bias, in which investors only reluctantly trade out of losing security positions, and results in only a gradual reaction to deteriorating market fundamentals Thus, mean reversion may occur at longer intervals of three to five years as FMPs finally purge their positions Bubbles and Crashes Bubbles (irrational buying that increases prices well above fair value) and crashes (irrational selling that decreases prices well below fair value) contradicts the theory of an efficient market A crash is generally defined as a 30 percent fall in asset price over a short period If asset prices are normally distributed, one would expect a price index would only trade outside two standard deviations no more than percent of the time Empirical studies, however, find these extreme market valuations account for over 10 percent of the time Bubbles typically develop more slowly than crashes, which points to the different behaviors involved For example, investors might logically respond to periods of easy money by increasing asset prices, but this view would moderate as asset prices approach the new fair value In a bubble, however, investors fail to modify their expectations on price appreciation Several rational observations are offered to explain some of the bubbles and crashes For instance, some consider that the recent technology bubble leading to the market crash of March 2000 might have been a result of ineffective arbitrage due to the high cost of short selling The simple unavailability of suitable hedging instruments to offset risk may have 180 © 2018 Wiley BEHAVIORAL FINANCE AND INVESTMENT PROCESSES worsened the 2005-2007 real estate bubble In addition, managers were encouraged to accept risks because of short-term performance incentives Asset prices normally contain relevant information as well as noise (mood of the crowd) In an asset bubble, however, the so-called noise trading, when market participants buy and sell with no updated relevant information, increases and FMPs can misinterpret gains as skill at evaluating such information Overconfidence bias can lead people to believe they are responsible for their successes in a rapidly rising market, especially if hindsight bias allows them to rewrite history in a way that reinforces their beliefs Confirmation bias helps them shut out relevant information that contradicts their perception of the market Regret aversion also contributes, encouraging investment so they won’t miss out on the gains Anchoring causes investors to underreact when the bubble unwinds Investors initially are reluctant to accept losses This response can initially cause an underreaction to bad news However, investors later change their minds and begin selling at such an accelerated pace that causes share price decline Hedge funds will be among the first to recognize impending investor capitulation and can sell shares short to take advantage of this There may also be a feeling that short sellers have superior information and the herd turns to selling As such, the vicious cycle begins Relative Value and Size Anomalies Value stocks are defined as those equities with a high book-to-market ratio while growth stocks are defined as those with a low book-to-market ratio Studies have shown that value stocks tend to outperform growth stocks over long time horizons Other studies have shown that small-capitalization companies outperform large-capitalization companies in a majority of markets Fama and French expanded on the capital asset pricing model (CAPM) to explain the anomalies by explicitly incorporating three factors: size, value, and market beta Rather than mispricing, Fama and French believed small-cap companies might have greater risk—for which investors must be compensated—associated with distress during bad times Unfortunately, their three-factor CAPM model fails to explain the anomaly associated with value stocks Based on behavioral finance, others suggest the persistent pattern in value and growth stocks are simply pricing anomalies rather than risk as suggested by Fama and French In the halo effect, for example, a company with a good record and recent superior results might be viewed as having higher than warranted return potential (representativeness bias) Perceptions of potential returns can be enhanced by brand reputation, or even growth itself One study indicates that returns for funds perceived as popular in a magazine earned lower returns © W iley ... Bond Portfolios 12 7 12 7 12 9 13 3 13 5 13 7 14 0 1 43 1 43 14 4 14 7 14 8 14 8 14 9 14 9 Study Session 11 : Fixed-Income Portfolio Management (2) Reading 23: Yield Curve Strategies Lesson 1: Foundational Concepts... Financial Instruments 1 53 1 53 15 5 16 1 16 7 16 9 16 9 17 2 17 5 18 5 18 9 19 1 © 2 018 Wiley CONTENTS Study Session 12 : Equity Portfolio Management Reading 25: Equity Portfolio Management Lesson 1: The Role of... cannot be guaranteed ISBN 978 -1- 119 - 436 11 -9 (ePub) ISBN 978 -1- 119 - 436 10 -2 (ePDF) Contents About the Authors xi Wiley Study Guide for 2 018 Level III CFA Exam Volume 1: Ethical and Professional Standards