DOCSLIB.ORG

CIPM Principles Review Course

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
CIPM Principles Review Course 3/2/2017 CIPM Principles Review Course Reading: Investment Performance Appraisal 1 Active Management Skill LOS A • The ability of a portfolio manager to add value on a risk‐adjusted basis through investment analysis and insights. • Contrast Active and Passive Management – Passive = Indexing – Skill is in the portfolio construction, not security selection. – Active manager skill is found in asset allocation, and sector and/or security specific insights. 2 1 3/2/2017 Gross vs. Net Perf. Appraisal LOS B • What the reading says: – Performance Appraisal should be conducted on the client’s return. (i.e., the net return) • Fees differ among managers. Some charge for superior service. • Some management fees are fixed, and expense ratios are inversely related to AUM. • Better managers may charge more. – Expenses for active management are certain, while the value‐added for active management is uncertain. • What Cairn says: • Management fees add “noise” to the analysis. • Use net returns when comparing rates of return, and deciding if the manager is “worth it.” • Use gross returns when identifying manager skill, and measuring volatility or relative risk measures. • For risk‐adjusted measures, be sure to be consistent in use of gross or net. 3 Luck vs. Skill LOS C A cautionary statement: • As your population increases its knowledge level, the difference between those with skill and those without diminishes. • Past performance is not indicative of future results. • Skill may come from consistency, not necessarily past outperformance. 4 2 3/2/2017 Risk: A Review LOS D • Risk = Uncertainty of outcomes • Investors cannot make decisions based on returns alone: – Investment A: 100% probability of 2% return – Investment B: 50% probability of ‐2% return, 50% probability of 6% return. 5 Risk: A Review LOS E Types of Risk: • Total Risk (aka “Stand‐Alone” risk): Use Standard Deviation (σ) – Systematic (“Non‐diversifiable”) risk: Use Beta – Non‐Systematic (“Diversifiable”) risk • Downside Risk: Use downside deviation, VaR, etc. 6 3 3/2/2017 Sharpe Ratio LOS F‐H • Excess Return to (Total) Variability: Sharpe Ratio = • Identifies the fund that offers the most excess return, per unit of volatility. • More appropriate for evaluating the investor’s entire portfolio. • Make sure you compare apples‐to‐apples: • Annualize by SR × √n, where n = # of periods in 1 year. 7 Modigliani & Modigliani Ratio LOS F‐H • M2 Risk‐Adjusted Performance = Uses Sharpe ratio, scaled to the same volatility as the Market Turns the Sharpe ratio back into a return statistic 8 4 3/2/2017 Treynor Ratio LOS F‐H • Treynor Ratio = Excess Return per unit of Systematic Risk Uses Beta as risk; less useful when analyzing a total portfolio. 9 Alpha LOS F‐H • Recall CAPM: E(R) = RFR + β(RMkt –RFR) • Alpha = Actual Return – Expected Return • Jensen’s Alpha = Actual Return – E(R)CAPM • Realized Alpha = Actual Return – Required Return • Do not confuse with Active Return: Actual Return – BM Return 10 5 3/2/2017 Jensen’s Alpha Challenges LOS J Jensen’s Alpha = Actual Return – [RFR + β(RMkt –RFR)] • Beta only captures systematic risk. May not be appropriate for a single‐fund (non‐diversified) investor. • May be misleading if manager tries to “time the market,” as Beta may be artificially low. • Only uses one risk‐factor (beta). 11 Information & Appraisal Ratio LOS F‐H • Information Ratio (Active Return) = = Excess Return = Active Return Excess Risk Tracking Error Excess Return over a benchmark Risk measured as deviation from the benchmark • Treynor‐Black Appraisal Ratio = Non‐systematic risk Based on linear regression assumptions. 12 6 3/2/2017 Information Ratio LOS K Fundamental Law of Active Management Information Ratio = Information Coefficient × √n • Information Coefficient = correlation of expected & actual returns • N = market breadth (security universe) • Conceptually, this is manager skill times the number of opportunities to use that skill. • There is also assumed to be a trade‐off between IC and n. While a larger universe makes a larger IR, it would also likely lead to a lower IC. Modification: IR = IC × √n × TC • Transfer Coefficient is the speed in which the manager can transfer opportunities into action. 13 Downside Risk LOS I & L • Minimum Acceptable Return (“MAR”): specified by user. • Zero, inflation, risk‐free rate, index, actuarial rate, etc. • Downside Deviation: Similar to standard deviation, but use only those returns falling below the MAR. ∑ • Sortino Ratio = 14 7 3/2/2017 Drawdown LOS L • Measures losses after a peak: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 9% 13% 5% ‐4% 10% ‐15% 9% ‐4% ‐6% ‐7% 3% ‐10% 1,090 1,232 1,293 1,242 1,366 1,161 1,265 1,215 1,142 1,062 1,094 984 1,500 1,300 1,100 900 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 15 Maximum Drawdown LOS L Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 9% 13% 5% ‐4% 10% ‐15% 9% ‐4% ‐6% ‐7% 3% ‐10% 1,090 1,232 1,293 1,242 1,366 1,161 1,265 1,215 1,142 1,062 1,094 984 DD1: 1,293 – 1,242 51 ‐51/1293 ‐3.94% DD2: 1,366 – 1,161 205 ‐205/1366 ‐15.01% • Drawdowns: DD3: 1,265 – 1,062 203 ‐203/1265 ‐16.05% DD4: 1,094 – 984 110 ‐110/1094 ‐10.05% • Maximum Drawdown: 984 – 1,366 –382 28% , 16 8 3/2/2017 Calmar Ratio LOS L = | | .% • Using the data from the previous example… = 0.06 % Note: This should really use 3‐years of data to be meaningful. Pros & Cons: • Often used to evaluate hedge funds, as this is more suitable for illiquid investments • Sensitive to outliers • Also implies that investment is sold at the worst possible time, which may not be true. 17 Multi‐Factor Models LOS M Single‐Factor models: use β or σ * Value = low P/BV Multifactor models: BV can be negative Value = high BV/P • Fama & French 3‐Factor Model: – E(R) = RFR + β(RMkt –RFR) + βC(RSC –RLC) + βS(RHBV –RLBV) CAPM Cap Premium Style Premium* • Carhart 4‐Factor Model: – E(R) = RFR + β(RMkt –RFR) + βC(RSC –RLC) + βS(RHBV –RLBV) + βS(RPHP –RPLP) Momentum Premium 18 9 3/2/2017 Methods for Appraisal LOS N How do we derive the style of a portfolio? • Returns‐Based: Regression of portfolio and various benchmarks, and look for high correlation. – Minimal data needed – Backward‐looking. May not identify changing trends. • Holdings‐Based: Identify salient characteristics of the current portfolio. – Data intensive. – Requires frequent and regular updating to spot drift. – Grinblatt‐Titman is an example. 19 10.
Load more

Recommended publications
  • Performance Measurement in Finance : Firms, Funds and Managers
    PERFORMANCE MEASUREMENT IN FINANCE Butterworth-Heinemann Finance aims and objectives • books based on the work of financial market practitioners and academics • presenting cutting edge research to the professional/practitioner market • combining intellectual rigour and practical application • covering the interaction between mathematical theory and financial practice • to improve portfolio performance, risk management and trading book performance • covering quantitative techniques market Brokers/Traders; Actuaries; Consultants; Asset Managers; Fund Managers; Regulators; Central Bankers; Treasury Officials; Technical Analysts; and Academics for Masters in Finance and MBA market. series titles Return Distributions in Finance Derivative Instruments: theory, valuation, analysis Managing Downside Risk in Financial Markets: theory, practice and implementation Economics for Financial Markets Global Tactical Asset Allocation: theory and practice Performance Measurement in Finance: firms, funds and managers Real R&D Options series editor Dr Stephen Satchell Dr Satchell is Reader in Financial Econometrics at Trinity College, Cambridge; Visiting Professor at Birkbeck College, City University Business School and University of Technology, Sydney. He also works in a consultative capacity to many firms, and edits the journal Derivatives: use, trading and regulations. PERFORMANCE MEASUREMENT IN FINANCE Firms, Funds and Managers Edited by John Knight Stephen Satchell OXFORD AMSTERDAM BOSTON LONDON NEW YORK PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO
    [Show full text]
  • Picking the Right Risk-Adjusted Performance Metric
    WORKING PAPER: Picking the Right Risk-Adjusted Performance Metric HIGH LEVEL ANALYSIS QUANTIK.org Investors often rely on Risk-adjusted performance measures such as the Sharpe ratio to choose appropriate investments and understand past performances. The purpose of this paper is getting through a selection of indicators (i.e. Calmar ratio, Sortino ratio, Omega ratio, etc.) while stressing out the main weaknesses and strengths of those measures. 1. Introduction ............................................................................................................................................. 1 2. The Volatility-Based Metrics ..................................................................................................................... 2 2.1. Absolute-Risk Adjusted Metrics .................................................................................................................. 2 2.1.1. The Sharpe Ratio ............................................................................................................................................................. 2 2.1. Relative-Risk Adjusted Metrics ................................................................................................................... 2 2.1.1. The Modigliani-Modigliani Measure (“M2”) ........................................................................................................ 2 2.1.2. The Treynor Ratio .........................................................................................................................................................
    [Show full text]
  • Performance Measurement for Traditional Investment Literature Survey
    EDHEC RISK AND ASSET MANAGEMENT RESEARCH CENTRE Performance Measurement for Traditional Investment Literature Survey January 2007 Véronique Le Sourd Senior Research Engineer at the EDHEC Risk and Asset Management Research Centre Table of contents Introduction ................................................................................................................................................................. 5 1. Portfolio returns calculation ............................................................................................................................... 6 1.1. Basic formula .............................................................................................................................................................................................6 1.2. Taking capital flows into account ....................................................................................................................................................6 1.3. Evaluation over several periods .......................................................................................................................................................10 1.4. Choice of frequency to evaluate performance .........................................................................................................................11 2. Absolute risk-adjusted performance measures ............................................................................................13 2.1. Sharpe ratio (1966) ...............................................................................................................................................................................13
    [Show full text]
  • Performance Attribution from Bacon
    Performance Attribution from Bacon Matthieu Lestel February 5, 2020 Abstract This vignette gives a brief overview of the functions developed in Bacon(2008) to evaluate the performance and risk of portfolios that are included in PerformanceAn- alytics and how to use them. There are some tables at the end which give a quick overview of similar functions. The page number next to each function is the location of the function in Bacon(2008) Contents 1 Risk Measure 3 1.1 Mean absolute deviation (p.62) . 3 1.2 Frequency(p.64) ................................ 3 1.3 SharpeRatio(p.64)............................... 4 1.4 Risk-adjusted return: MSquared (p.67) . 4 1.5 MSquared Excess (p.68) . 4 2 Regression analysis 5 2.1 Regression equation (p.71) . 5 2.2 Regression alpha (p.71) . 5 2.3 Regression beta (p.71) . 5 2.4 Regression epsilon (p.71) . 6 2.5 Jensen'salpha(p.72) .............................. 6 2.6 SystematicRisk(p.75) ............................. 6 2.7 SpecificRisk(p.75)............................... 7 2.8 TotalRisk(p.75) ................................ 7 2.9 Treynorratio(p.75)............................... 7 2.10 Modified Treynor ratio (p.77) . 7 2.11 Appraisal ratio (or Treynor-Black ratio) (p.77) . 8 2.12 Modified Jensen (p.77) . 8 1 2.13 Fama decomposition (p.77) . 8 2.14 Selectivity (p.78) . 9 2.15 Net selectivity (p.78) . 9 3 Relative Risk 9 3.1 Trackingerror(p.78) .............................. 9 3.2 Information ratio (p.80) . 10 4 Return Distribution 10 4.1 Skewness (p.83) . 10 4.2 Sample skewness (p.84) . 10 4.3 Kurtosis(p.84) ................................
    [Show full text]
  • Certification Examination Detailed Content Outline
    Certification Examination Detailed Content Outline Qualification Certification Qualification Certification Certification Examination Detailed Content Outline exam exam Certification Examination Detailed Content Outline exam exam I. GOVERNANCE 4% 7% 3. Differentiate between measures of central tendency A. IMCA Code of Professional Responsibility and 4. Describe and differentiate between measures of dispersion: 2% 5% Standards of Practice • Range • Semi-variance 1. Serve the financial interests of all clients • Absolute deviation • Semi-deviation 2. Disclose fully to clients services provided and compensation received • Variance • Coefficient of variation 3. Provide to clients all material information related to the investment decision- • Standard deviation making process as well as other information they may need to make informed 5. Apply probability concepts: decisions based on realistic expectations • Probability • Variance • Expected value 4. Maintain the confidentiality of all information entrusted by the client to the fullest extent permitted by regulatory and legal entities in conjunction with the 6. Analyze and interpret hypothesis testing using probabilistic methodologies and professional’s firm/company policy models (e.g., Monte Carlo simulation) 7. Analyze correlation, regression, and multiple regression 5. Comply fully with all statutory and regulatory requirements affecting the (a) Correlation analysis: delivery of investment consulting services to clients • Scatterplots • Correlation coefficient (b) Linear regression: 6. Maintain competency in investment management consulting and financial • Independent and dependent variables services through education and training to better serve clients and enhance • Coefficient of determination investment management consulting • Standard error • Hypothesis testing 7. Maintain a high level of professional ethical conduct • Prediction intervals (c) Multiple regression analysis: B. Regulatory Considerations 2% 2% • Multiple Linear Regression Model 1.
    [Show full text]
  • Measuring the Investment Risk of Equity Funds a Reference for Fund Directors
    Measuring the Investment Risk of Equity Funds A Reference for Fund Directors May 2018 Investment risk includes both intended or expected risk from the investment process and unintended risk that may result from investment decisions, assumptions, market movements, and other factors. While the fund’s adviser manages this risk, Introduction directors have an important oversight role. Why this Reference? This Reference provides an overview of the statistical measures that may be used to evaluate investment risk in equity funds and that may be included in reports provided to directors. It has 4 sections: • Key Factors | 4 key factors to consider when reviewing risk measures. For more background, • Common Uses | a summary of the most common uses be sure to read the of the risk measures. Mutual Fund Directors Forum February 2017 publication • Follow Up | questions that directors might ask to learn more about how the adviser measures investment risk. Role of the Mutual Fund Director in the Oversight of the Risk Management • A to Z | a glossary with definitions of common risk Function measures. Includes the arguments for and against specific measures and reviews when measures may be used. The definitions are summarized in tables at the end of the glossary. 2 Risk statistics can be divided into two categories: ex post and ex ante. Key Factor #1 • Ex post Ex Post vs. Ex Ante Ex post statistics measure risk over a period in the past. They measure historical price or return volatility. Standard deviation, the Sharpe ratio and alpha are commonly-used ex post risk statistics. Ex post measures can be computed for any period (e.g., daily, weekly or monthly), but they are generally annualized to make comparisons easier.
    [Show full text]
  • Performance Comparison of Canadian Hedge Funds and Mutual Funds
    Performance Comparison of Canadian Hedge Funds and Mutual Funds by Amitesh Kapoor MBA, Institute of Management Studies, Shimla, India PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF FINANCIAL RISK MANAGEMENT in the Financial Risk Management Program of the Faculty of Business Administration © Amitesh Kapoor 2010 SIMON FRASER UNIVERSITY Summer, 2010 All rights reserved. However, in accordance with the Copyright Act of Canada, this work may be reproduced, without authorization, under the conditions for Fair Dealing. Therefore, limited reproduction of this work for the purposes of private study, research, criticism, review and news reporting is likely to be in accordance with the law, particularly if cited appropriately. APPROVAL Name: Amitesh Kapoor Degree: Master of Financial Risk Management Title of Project: Performance Comparison of Canadian Hedge Funds and Mutual Funds Supervisory Committee: ____________________________________________ Dr. Peter Klein Senior Supervisor Professor of Finance ___________________________________________ Dr. Christina Atanasova Supervisor Assistant Professor Date Approved: ______________________________________________________ ii Abstract Canadian hedge funds have outperformed the benchmark index by an average of 72 basis points monthly from January 2000 through May 2009. By comparison, Canadian mutual funds have outperformed the benchmark index by an average of 18 basis points monthly in the same period. This contrast in performance persists even after adjusting for risk, as measured by Sharpe Ratio, Treynor Ratio, and Information Ratio. It also persists on market risk adjusted basis. Using CAPM, Fama and French three Factor Model, and Carhart, the alpha is much higher for Hedge Funds than Mutual funds. I have analysed the performance in different sub periods and market environments.
    [Show full text]
  • Capture Ratios: a Popular Method of Measuring Portfolio Performance in Practice
    JOURNAL OF ECONOMICS AND FINANCE EDUCATION • Volume 2 • Number • Winter 2013 Capture Ratios: A Popular Method of Measuring Portfolio Performance in Practice Don R. Cox and Delbert C. Goff 1 ABSTRACT We provide a brief introduction to a simple measure of portfolio risk and return, the capture ratio, which is widely used in practice, but rarely noted in finance textbooks or literature. We describe the ratio and provide examples of its rather widespread use in the investing profession. Finally, we show a specific example of calculating capture ratios using a computer spreadsheet. Finance professors and their students can benefit from basic knowledge of this common practical performance measurement tool. Introduction Finance and investments textbooks typically discuss multiple measures of portfolio risk and return that are also commonly used in practice. Among the most commonly noted measures are alpha, beta, the standard deviation of returns, the Sharpe ratio, the Treynor ratio, tracking error, and the information ratio. There are other measures of portfolio performance that are used in practice, but are generally not covered in textbooks. Among the most popular of these alternative measures are the “up-market capture” and “down- market capture” ratios. Through our experience serving on advisory boards for endowment funds and working with investment professionals we have had the opportunity to see the frequency with which capture ratios are used in practice. And, through our research we have discovered other evidence suggesting that capture ratios are widely used in practice by investment advisors and consultants. There is, however, a dearth of information from academic research and/or textbooks on the capture ratios.
    [Show full text]
  • Active Portfolio Management a Quantitative Approach for Providing Superior Returns and Controlling Risk
    Page iii Active Portfolio Management A Quantitative Approach for Providing Superior Returns and Controlling Risk Richard C. Grinold Ronald N. Kahn SECOND EDITION Page vii CONTENTS Preface xi Acknowledgments xv Chapter 1 1 Introduction Part One Foundations Chapter 2 11 Consensus Expected Returns: The Capital Asset Pricing Model Chapter 3 41 Risk Chapter 4 87 Exceptional Return, Benchmarks, and Value Added Chapter 5 109 Residual Risk and Return: The Information Ratio Chapter 6 147 The Fundamental Law of Active Management Part Two Expected Returns and Valuation Chapter 7 173 Expected Returns and the Arbitrage Pricing Theory Page viii Chapter 8 199 Valuation in Theory Chapter 9 225 Valuation in Practice Part Three Information Processing Chapter 10 261 Forecasting Basics Chapter 11 295 Advanced Forecasting Chapter 12 315 Information Analysis Chapter 13 347 The Information Horizon Part Four Implementation Chapter 14 377 Portfolio Construction Chapter 15 419 Long/Short Investing Chapter 16 445 Transactions Costs, Turnover, and Trading Chapter 17 477 Performance Analysis Page ix Chapter 18 517 Asset Allocation Chapter 19 541 Benchmark Timing Chapter 20 559 The Historical Record for Active Management Chapter 21 573 Open Questions Chapter 22 577 Summary Appendix A 581 Standard Notation Appendix B 583 Glossary Appendix C 587 Return and Statistics Basics Index 591 Page xi PREFACE Why a second edition? Why take time from busy lives? Why devote the energy to improving an existing text rather than writing an entirely new one? Why toy with success? The short answer is: our readers. We have been extremely gratified by Active Portfolio Management's reception in the investment community.
    [Show full text]
  • How Sharp Is the Sharpe-Ratio? - Risk-Adjusted Performance Measures Carl Bacon, Chairman, Statpro
    How sharp is the Sharpe-ratio? - Risk-adjusted Performance Measures Carl Bacon, Chairman, StatPro Carl Bacon: How sharp is the Sharpe ratio? - Risk-adjusted Performance Measures Carl Bacon Chairman, StatPro How sharp is the Sharpe-ratio? - Risk-adjusted Performance Measures “Alpha to omega, downside to drawdown, appraisal to pain” About the author: Carl Bacon CIPM, joined StatPro Group plc as Chairman in April 2000. StatPro provides sophisticated data and software solutions to the asset management industry. Carl also runs his own consultancy business providing advice to asset managers on various risk and performance measurement issues. Prior to joining StatPro Carl was Director of Risk Control and Performance at Foreign & Colonial Management Ltd, Vice President Head of Performance (Europe) for J P Morgan Investment Management Inc., and Head of Performance for Royal Insurance Asset Management. Carl holds a B.Sc. Hons. in Mathematics from Manchester University and is an executive committee member of Investment-Performance.com, and an associate tutor for 7city Learning. A founder member of both the Investment Performance Council and GIPS®, Carl is a member of the GIPS Executive Committee, chair of the Verification Sub-Committee, a member of the UK Investment Performance Committee and a member of the Advisory Board of the Journal of Performance Measurement. Carl is also the author of “Practical Portfolio Performance Measurement & Attribution” part of the Wiley Finance Series, numerous articles and papers and editor of “Advanced Portfolio
    [Show full text]
  • The “Alphabet” of Risk
    EXPERIENCED AND INDEPENDENT ADVISORS WORKING PRIMARILY WITH ACCREDITED ENGINEERS, EXECUTIVES, AND ENTREPRENEURS 9237 Ward Parkway, Suite 320 | Kansas City, MO 64114 The “Alphabet” of Risk: Peter Vrooman, CFA®, CIMA®, CRPC® Jonathan Sarver, CPWA® Partner, Wealth Advisor Partner, Wealth Advisor 17 Modern Portfolio Theory (MPT) Statistics Fundamental Choice Portfolio Manager Fundamental Choice Portfolio Manager and Concepts for Understanding and 816-601-1152 816-601-1151 [email protected] [email protected] Measuring Investment Risk, Risk-Adjusted Returns, and Relative Performance #1 Modern Portfolio Theory Under modern portfolio theory, investors focus on managing risk as well as maximizing return. According to MPT, the riskiness of a portfolio depends on the covariance of its individual holdings, rather than the average riskiness of the separate investments (Dobbs, 2015). #2 Efficient Frontier The combination of assets that will maximize return for a given level of risk (Dobbs, 2015). MEASURING RISK #3 Standard Deviation Measures the variability of returns of an asset compared to its mean or expected return. Standard deviation measures total risk. The greater the dispersion of an investment’s returns around the average return, the greater the Standard deviation and the greater the risk (Dobbs, 2015). Approximately 68% of outcomes are + or - 1 standard deviation from the mean Approximately 95% of outcomes are + or - 2 standard deviations from the mean Approximately 99.7% of outcomes are + or - 3 standard deviations from the mean Steps to calculate standard deviation: 1. Difference between the observation and the average return. 2. Square the differences. 3. Total squared differences. 4. Divide the sum by one less than the number of observations.
    [Show full text]
  • Kaplan Schweser 2017 FRM Part I 10-Week Online Class Week 2
    Fixed Income Investments Kaplan Schweser 2017 FRM Part I 10-Week Online Class Week 2 Fixed Income Investments Getting Up to Speed on the Financial Crisis: A One- Weekend-Reader’s Guide Topic 8 1 Book 1, LO 8.1 Financial Crisis Overview The main trigger of the financial crisis was the prospect of losses on subprime mortgages In the first half of 2007, housing prices in the U.S. started to decline, causing several subprime mortgage lenders to file for bankruptcy These losses became amplified as they had a ripple effect that spread to the main vulnerabilities of the crisis, asset-backed commercial paper (ABCP), and repurchase agreements (repos) ©2017 Kaplan, Inc. 3 Book 1, LO 8.3 Lehman Brothers Failure The Lehman Brothers bankruptcy filing in September 2008 is considered the tipping point in the financial crisis It eroded confidence and caused a run on money market mutual funds (MMFs) The lack of confidence spread across markets and countries, amplifying losses in the subprime mortgage market ©2017 Kaplan, Inc. 4 2 Book 1, LO 8.4 & 8.7 Previous Financial Crises The recent financial crisis was not unique compared to previous banking crises It followed a similar pattern of increased public and private debt, increased credit supply, and increased housing prices preceding and leading to the crises Studies showed that as the recent crisis strengthened, the demand for credit decreased ©2017 Kaplan, Inc. 5 Book 1, LO 8.5 Panic Periods The two main panic periods of the financial crisis were August 2007 and September through October 2008 1.
    [Show full text]