Research Articles

In past issues of Barra's newsletters, we have presented seven quantitative insights into active management. We can summarize these seven insights by observing how they all fit into the process of active management. The Research stage involves the search for superior information. This information must be better than consensus information (Insight 1: Active Management is Forecasting). We can improve our chances for success by investigating many signals (Insight 3: The Fundamental Law of Active Management). At the same time, we need to avoid data mining (Insight 5: Data Mining Is Easy). The goal of this Research stage is a strategy with a high information ratio (Insight 2: Information Ratios Determine Value Added). The Refinement stage takes our research signals and converts them to alphas by controlling for skill, volatility, and expectations (Insight 4: Three-Part Alphas). The Portfolio Construction and Rebalancing stage and the Trading stage implement the strategy. Here the goal is to lose as little of the intrinsic strategy value as possible (Insight 6: Implementation Subtracts Value). The Performance Analysis stage looks at results, identifying (imperfectly) what worked and what didn't work, in part as feedback to Research (Insight 7: It's Hard to Distinguish Skill from Luck). It is useful to note that while we have presented (and derived) these seven insights as 'quantitative,' they apply to all managers: fundamental, quantitative, top-down, bottom-up, equity, bond, and so forth. Finally, let me point out that we have called this series 'Seven Quantitative Insights...' and not 'The Seven Quantitative Insights into Active Management.' This series has omitted some known insights. And, as a researcher, I will always claim that there remain more insights to uncover. Active management combines art and engineering. The art involves finding valuable information about future returns. The engineering involves efficiently capturing that information in superior portfolios. By assuming that it is possible to find such valuable information, we can derive many important insights into the engineering of this process. Over the next several Barra Newsletters, I will outline seven insights which follow from this perspective. Richard Grinold and I discuss these points more comprehensively in our book Active Portfolio Management, and many of these items appear throughout the lore and literature of the profession.

Publication: Barra Research Insights
Authors: KAHN Ronald N.

Every promising investment strategy on paper loses value on                           implementation. Insight, especially into the relationship between transaction costs and value added, can help minimize this loss.

Publication:
Authors: KAHN Ronald N.

The current arguments about whether to limit or prohibit pension fund investments in tobacco stocks, in contrast to earlier debates about "sin-free" investing, focus on investment considerations rather than morality. But tobacco divestiture doesn't stand up as an investment decision. It doesn't reduce risk in the typical pension fund context, nor does it constitute a clever active strategy issued from the legislature. We should see tobacco divestiture for what it is: a moral decision. Given that, public officials need to understand the investment cost they are paying to achieve the moral gain. Investment restrictions will reduce opportunities for outperformance for active managers, increase risk for passive managers, generate one-time excess transactions costs, and cause measurement problems associated with imperfect benchmarks. We have analyzed costs for each of these effects. With this knowledge, officials can make an informed choice about tobacco divestiture.

Publication: Journal of Investing
Authors: KAHN Ronald N., LEIMKUHLER Tom, LEKANDER Claes

Several issues arise when analyzing plan-wide risk. What is the appropriate plan benchmark? Dies it consist of a set of investable indices? How does it reflect the liabilities that are the true underlying target for the plan? More general still, how should we define risk? Should we use value at risk or standard deviation? Should we define risk relative to the benchmark? Even after choosing a particular risk definition, we must identify all the factors which drive risk. Finally, should our forecast of risk use simply historical returns data or specifically analyze the current plan holdings? In this paper we will discuss these issues and provide for concreteness a detailed case study.

Publication: RogersCasey Research Insights
Authors: CESARE Christopher J., DEMAKIS Drew W., KAHN Ronald N.

In 1994 many mutual fund investors experienced staggering and unexpected losses. Amid the resulting public outcry, the U.S. Securities and Exchange Commission considered how to better inform the public of investment risk, and asked for public commentary. The response, from many thousands of individual investors, far surpassed the SEC's expectations. The public has considerable interest in mutual fund risk. As the leading provider of risk models and analytics to the institutional investment community, Barra responded with two reports to the SEC. The first, 'Quantitative Measures of Mutual Fund Risk: An Overview,' outlined the basic issues and choices as informed by our twenty years of modeling risk. The second report, 'Forecasting Mutual Fund Risk: Current Holdings or Past Performance?' followed up with a study comparing methods for forecasting mutual fund risk. This Barra Research Insights document collects these two reports. While the topic specifically focuses on mutual fund risk, we believe the issues and conclusions will interest our institutional clients.

Publication:
Authors: KAHN Ronald N.

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.

Information ratios determine value added.

Publication:
Authors: KAHN Ronald N.

Active management combines art and engineering. The art involves finding valuable information about future returns. The engineering involves efficiently capturing that information in superior portfolios. By assuming that it is possible to find such valuable information, we can derive many important insights into the engineering of this process.  Over the next several Barra Newsletters, seven insights that follow from this perspective will be outlined. Insight One: Active management is forecasting.

Publication:
Authors: KAHN Ronald N.

Which mutual funds will be next year's winners? Conventional wisdom in the investment community says that to predict future performance, look at past performance. But does it help to know last year's? Do winners repeat? The idea that winners repeat is so obvious and popular, it has spawned an entire mini-industry devoted to documenting past winners. Mutual fund performance reviews regularly appear in publications like Barron's, Business Week, and Consumer Reports.  Services such as Morningstar and Lipper exist to publish mutual fund rankings.  Pension plan consultants closely examine past performance before recommending managers, and successful managers proudly document their past performance. All this suggests that everyone choosing active managers, from pension plan sponsors to  individual investors, thinks past performance predicts future performance. Is this true?  In this article, we will review past investigations into this question, and then present new results looking at performance of active equity and fixed income managers over the past decade. (1)

Publication:
Authors: KAHN Ronald N., RUDD Andrew

Active management is forecasting. We can use a basic forecasting formula to adjust forecast returns away from the consensus, based on how far the raw forecasts differ  from their consensus, and on the information content of the raw forecasts. We capture this basic result in the forecasting rule of thumb: The exceptional return forecast takes on the form Volatility · IC · Score. This article has applied these relationships in several specific examples.

Publication:
Authors: GRINOLD Richard, KAHN Ronald N.

We will present in this article a specific multifactor risk model of the Canadian bond market that we developed at Barra. First, we will describe the particular features of the Canadian bond market. Second, we will present a valuation model of the Canadian Government bond market that will identify and measure the various sources of risk, including the term structure and tax and liquidity factors. Third, we will describe the analysis of the historical variance and covariance of excess returns(4) to these factors, which will lead us to the risk model. Fourth, we will present quantitative results pertaining to the market. An article in the next issue of the Barra Newsletter will focus on the valuation and risk models of the Provincial and Corporate sectors of the Canadian bond market.

Publication:
Authors: GULRAJANI Deepak, KAHN Ronald N.

Performance analysis focuses on the central goal of investment management: achieving superior                                         performance. A rigorous multi-factor approach to bond analysis can identify sources of fixed-income return,  estimate their statistical significance, and analyze manager skill and value added.

Publication: The Journal of Portfolio Management
Authors: KAHN Ronald N.

The argument that convexity can generate exceptional return assumes that the term structure always shifts in parallel. This article examines whether the deviation from  the parallel shift assumption is sufficient to negate this conclusion. The analysis used in this article is return attribution analysis, a technique long used to analyze equity returns.

Publication: Journal of Portfolio Management
Authors: KAHN Ronald N., LOCHOFF Roland

Consider the trading process at its most basic. You have cash and you want to buy a  stock. You think the stock will go up. You want to buy soon, before the stock rises. But to avoid market impact, you are willing to be patient and assume some risk of missing the stock rise. What is your optimal trading strategy? Every day, portfolio managers face these decisions: portfolio construction, portfolio rebal-ancing, trading to achieve excess returns net of transaction costs. In the portfolio context, the number of stocks increases but the issues are the same. The manager wants to transact before anticipated price moves, yet wishes to minimize transaction costs. This article will illustrate a quantitative approach to determining an optimal trading strategy, using the particular and simple example of buying a single stock

Publication:
Authors: KAHN Ronald N.

This is the first in a series of articles describing a case study using Barra's HJM Option Analysis  System. This article will describe the underlying analytics of the HJM System, and then use the system to construct and analyze a portfolio. Next month, we'll show how the HJM System uses options to hedge the portfolio.

Publication:
Authors: KAHN Ronald N.

The argument that convexity can generate exceptional return assumes that the term structure always shifts in parallel. This article examines whether the deviation from  the parallel shift assumption is sufficient to negate this conclusion. The analysis used in this article is return attribution analysis, a technique long used to analyze equity returns.

Publication:
Authors: KAHN Ronald N., LOCHOFF Roland