"Evaluating Stock Screens with Performance Attribution", MSCI Research Bulletin, June 2010
Topic: Investing (Investment Management), Performance Analysis |
Asset Class: Equities
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Stock picking often is carried out with stock screens that reduce the set of eligible stocks. Such screens usually are assessed through the level and volatility of the resultant returns, mostly with associated statistics such as information ratio or Sharpe ratio. This evaluation, however, often is insufficient to uncover unintended bets, and this research bulletin suggests complementing this evaluation by conducting Barra performance attribution on the portfolios obtained from such screening strategies. For example, performance attribution indicates that the book-to-price screen performed relatively poorly in Australia because of an unintended tilt toward low-momentum stocks. In Japan, performance attribution shows that the superior performance of book-to-price over dividend yield as a selection screen from 2009 onward was largely due to non-value tilts.
Publication: MSCI Research Bulletin
Authors: MSCI Barra Applied Research
"Beyond Brinson: Establishing the Link Between Sector and Factor Models", MSCI Barra Research Insight, April 2010
Topic: Performance Analysis |
Asset Class: Multi-Asset Class
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Brinson sector-based attribution explains active return in terms of intuitive allocation and selection decisions. However, it cannot easily disentangle competing industry and style effects. We introduce a special type of factor model with five defining characteristics that exactly replicates the classic Brinson model. We show that this “Brinson-replicating” factor model easily extends to explain more general types of investment processes. In this extension, returns are decomposed into style effects and pure industry effects, net of styles. Moreover, much of the classic Brinson model “stock selection effect” is attributed to contributions from a handful of style factors. We show that in this framework risk and return can be attributed to the same set of decision variables. This provides a means of comparing return contributions on a risk-adjusted basis.
Publication: MSCI Barra Research Insight
Authors: DAVIS Ben, MENCHERO Jose
"Custom Factor Attribution", Financial Analysts Journal, Volume 64, Number 2, CFA Institute
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Topic: Performance Analysis |
Asset Class: Multi-Asset Class
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Portfolio analysts often use one set of decision variables for attributing portfolio returns and a different set for attributing risk. This practice obscures the relationship between the sources of risk and return. This article demonstrates how to align the attribution model with the investment process. The attribution methodology can be applied ex ante or ex post. A factor-based investment process illustrates the general framework. Specifically, active return, tracking error, and the information ratio are attributed to a user-defined set of factors that reflect the manager's investment decision-making process. A concrete example with actual market data, a style portfolio, and a parsimonious set of custom factors illustrates how to apply the analysis.
Publication: Financial Analysts Journal
Authors: MENCHERO Jose, PODURI Vijay
"Performance Attribution Using Daily Data", Barra Newsletter, Fall 2003
Topic: Performance Analysis |
Asset Class: Equities
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Aegis Performance Analyst can help you understand the drivers of volatility and returns, allowing you to evaluate the risk-adjusted performance of our portfolios and continuously improve your results. Performance Analyst now offers daily asset and factor returns for the United States (USE3), United Kingdom (UKE6), Japan (JPE3) and Australia (AUE3). The newest release of Aegis 3.4 introduces daily data for the European market (EUE2) and an improved attribution scheme that better reflects regional characteristics. Additional markets will be added in the coming months.
Publication: MSCI Barra Newsletter
Authors: GILFEDDER Neil, ZHELEZNYAK Alexander
"Karnosky Singer Attribution: A Worked Example", Barra Research Insights, 2003
Topic: Performance Analysis |
Asset Class: Equities
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In 1994, Denis Karnosky and Brian Singer published the monograph "Global Asset Management and Performance Attribution". It described a method for multicurrency performance attribution that had certain advantages. The principal advantage of the method was demonstrated in several examples that showed that the Karnosky-Singer (or 'KS') model would reward optimal active portfolio 'bets', while more traditional approaches to multicurrency attribution would reward non-optimal bets. This document works through one of those examples with a view to helping performance analysts explain to their colleagues the strengths and weaknesses of the Karnosky-Singer model.
Publication:
Authors: LAKER Damien
"Fundamentals of Performance Attribution: Implementation Considerations", Barra Research Insights, 2003
Topic: Performance Analysis |
Asset Class: Equities
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The first four articles in this series focused on performance attribution calculationsthe 'theory' of performance attribution. Practically speaking, however, performance attribution is normally a commercial activity. Fund managers and their clients use performance attribution reports to understand how a portfolio is being managed. So the emphasis in this final article of the series is squarely on the practical issues that are important to the successful implementation of performance attribution in a commercial setting.
Publication:
Authors: LAKER Damien
"Fundamentals of Performance Attribution: Stock Selection and Interaction", Barra Research Insights, 2003
Topic: Performance Analysis |
Asset Class: Equities
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The previous article in this series dealt with multiple period attribution. This is an important technical problem for performance analysts, but for the end-users of attribution reports, there are more compelling issues: 'drilling down' into the results and understanding how specific decisions have added or subtracted value for the portfolio. Four of the main active decisions that can affect a portfolio's performance are: 1) Stock selection; 2) Asset allocation; 3) Currency allocation/hedging; and 4) Transaction costs incurred through trading. In the following article, we will consider asset allocation and currency issues. In the one after that, we'll consider transactions costs. In this article, we will consider stock selection, as well as the interaction term that is so frequently lumped in with stock selection. To facilitate the practical application of the techniques presented in this paper, some worked examples are available as spreadsheets.
Publication:
Authors: LAKER Damien
"An Analysis of a Source of Errors in Performance Measurement", Barra Research Insights, 1999
Topic: Performance Analysis |
Asset Class: Equities
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A portfolio's return is a measure commonly used to determine the success of its management. It is not uncommon that a portfolio's performance is compared to that of a benchmark, typically market indexes like the All Ordinaries and the SBC Bond Index. Most portfolios are actively managed and/or have contributions, withdrawals and income through performance measurement periods, and portfolio valuations are often less frequent (e.g. weekly or monthly); in these circumstances, money weighted return approximations are often used to calculate the portfolio managers return. If these money weighted returns are compared to the benchmark return, which by default is a time-weighted return if one uses the ratio of index values from the start to end of a period, then one is not comparing like with like since the portfolio return is not a time-weighted return. In this paper we consider the errors introduced in calculating portfolio returns using money weighted returns as an approximation to the time weighted return and produce a theoretical estimate of the distribution of errors. These errors depend on three variables, the timing of the cash flow through the performance measurement period, the size of the cash flow and the volatility of the portfolio value. Daily data over the last decade for the Australian and USA equity markets is used to empirically calculate these errors. The empirical results support the theoretical results. We conclude that the use of money-weighted returns can lead to the introduction of significant errors, even for small cash flows. We then provide a simple, practical method to check for the expected size of the errors. These errors have significant consequences when performing performance attribution since the errors can be the same order of magnitude as the asset allocation or stock selection components 'calculated' from the attribution analysis; thus the true attribution components may be buried in the noise resulting from these errors.
Publication:
Authors: VANN Peter
"Measuring Information Ratios", Barra Newsletter, Winter 1996
Topic: Performance Analysis |
Asset Class: Multi-Asset Class
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The ratio of active return to active risk annualized, known as the Information Ratio (IR), is a key statistic governing active management. All investors, no matter what their aversion to risk, will seek the highest information ratio possible. Unfortunately, it's very hard to accurately measure an IR. There is no quick fix to the measurement problems confronted by the investment business.
Publication:
Authors: KAHN Ronald N.
"'Sinful' Industry Returns in the United States", Barra Newsletter, March/April 1992, p1
Topic: Performance Analysis |
Asset Class: Equities
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How differently have companies in "sinful" industries (liquor, tobacco, and gambling) behaved? It seems clear that, under the restraints of this study, those institutions that excluded "sinful" stocks from their selection universe have potentially suffered a return shortfall. This is particularly true for the tobacco and liquor industries since 1985. Return differentials suggest that for managers who decide, or are required by their clients, not to invest in a category of stocks, the importance of constructing an appropriate benchmark becomes quite important to properly distinguish active performance.
Publication:
Authors: BECKERS Stan
"Performance Attribution and the International Portfolio", Barra Newsletter, September/October 1991, p10
Topic: Performance Analysis |
Asset Class: Equities
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While Barra's multiple factor approach has gained widespread acceptance among within-country managers, this approach can only explain a part of the puzzle when it comes to international managers and their multiple country and multiple currency portfolios. Barra has developed products to perform a multiple factor performance attribution on a multiple country, multiple currency portfolio. Multiple factor performance analysis of international portfolios can analyze the active performance of international equity managers, and we can analyze multiple country portfolios in much the same way that we have examined the performance of within-country portfolios. Portfolio managers and pension fund sponsors may now identify the sources of risk and return to their portfolio with greater accuracy.
Publication:
Authors: MEIER John
"Fixed Income Performance Attribution", Barra Newsletter, July/August 1991, p3
Topic: Performance Analysis |
Asset Class: Fixed Income
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How much of a particular portfolio's return was due to term structure changes such as parallel shifts, twists, and butterflies? What share of return resulted from changes in sector and quality spreads? These questions are important both to pension fund sponsors, who must evaluate their managers, and to managers striving to improve their investment strategies. This paper presents the theoretical underpinnings of bond performance analysis and analyzes the significance of returns attributed to investment factors. We illustrate the practical side with the following case study, which shows the application of bond performance theory as represented by Barra's PERFAT (Performance Attribution) program. We used PERFAT, an application of our U.S. Fixed Income Model, to generate a report for a sample portfolio.
Publication:
Authors: DAVIS Mark
"Report from the Equity Research Seminar, Part V: Value Added Continued", Barra Newsletter, March 1989, p8
Topic: Performance Analysis |
Asset Class: Equities
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In Part IV we discussed value added. The two key constructs in that discussion were the use of the expected utility of active return as a measure of value added and the use of a manager's information ratio as an indication of the manager's opportunity to add value. In this article, we investigate the determinants of the information ratio to find the sources of the manager's opportunity and thus the manager's ability to add value. We also propose a formula that can serve as a useful guide when making strategic investment policy decisions.
Publication:
Authors: Source: Barra Newsletter