Barra Risk Factor Analysis – Definition

Cite this article as:"Barra Risk Factor Analysis – Definition," in The Business Professor, updated April 17, 2020, last accessed June 1, 2020, https://thebusinessprofessor.com/lesson/barra-risk-factor-analysis-definition/.

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Barra Risk Factor Analysis

The Barra Risk Factor Analysis is a metric used in measuring the overall risk associated with security relative to the market risk. The Barra Risk factor was developed by Barra Inc and thereafter named after the company. It is a multi-factor model that incorporates more than 40 data metrics including senior debt trading, earnings growth, and many others.

A Little More on What is Barra Risk Factor Analysis

The founder of Barra Inc., Bar Rosenberg pioneered the creation of the Barra Risk Factor analysis. There are three major components considered in this risk factor, these are a company-specific risk, industry risk, and risk from exposure to different investment themes. The Barra Risk Factor Analysis is crucial to investors since it measures the risk associated with security, investment, or portfolio relative to the market risk. When making investment decisions, evaluating the risk involved is important, the level of risk of investment determines to a great extent the level of return. Also, the investment goals and risk tolerance level of investors make evaluating investment risks essential.

There are several metrics or factors models that investors and portfolio managers use to measure the risk of a security, the Barra Risk Factor analysis is one of the acceptable measures.

Factor models are divided into three categories; the single-factor models, multiple-factor models and the multi-factor models. The Barra Risk Factor Analysis is a  multi-factor model that embodies over 40 factors that predict the risk associated with a security or investment and also manage it. Some of the factors that this factor model incorporates include;

  • Earnings growth
  • Share turnover
  • Momentum
  • Volatility
  • Liquidity
  • Senior debt rating
  • Price earning ratios
  • Leverage
  • Size, and a couple of other factors.

When measuring the overall risk of a security relative to the market today, the Barra Risk Factor Analysis uses a single VaR number (value-at-risk).

Reference for “Barra Risk Factor Analysis”

https://www.investopedia.com › Investing › Financial Analysis

https://pdfs.semanticscholar.org/6a24/ebdc4bf26fee1c6706d017ba4fb735d8c247.pdf

https://www.msci.com › Research & Events › Research Archive › Barra’s Risk Models

https://www.quora.com/What-is-the-barra-risk-model

https://www.mbaskool.com › Concepts › Finance and Economics

Academics research on “Barra Risk Factor Analysis”

Validating empirically identified risk factors, Pettengill, G., & Chang, G. (2019). Validating empirically identified risk factors. Journal of Economics and Finance43(1), 162-179. Fama and French (J Financ, 33, 3–56. 1992); Fama and French (J Financ, 47, 427–465. 1993) provide discipline altering studies which ended the dominance of Capital Asset Pricing Model (CAPM) and supplanted it with the Fama and French three factor model. The CAPM identified the market factor as the only systematic risk factor; the three factor model added size and value as systematic risk factors. The latter study validated the size and value risk factors by showing a correlation between portfolio and factor time-series returns. This model has been widely accepted but has proved “open-ended” as researchers have mimicked this effort to identify a large number of additional factors. Harvey et al. (Rev Financ Stud, 29, 5–68. 2016) note that researchers have empirically identified 316 factors tested as systematic risk factors and argue that the discipline needs to identify the few relevant risk factors. Motivated by this seemingly futile effort to find the correct set of risk factors, we contribute by suggesting necessary conditions to validate empirically identified risk factors. We apply these conditions to the factors of the original Fama-French model. Based on our analysis we argue that neither the size nor value mimicking factors should be considered systematic risk factors.

Equity portfolio risk estimation using market information and sentiment, Mitra, L., Mitra, G., & Dibartolomeo¶, D. (2009). Equity portfolio risk estimation using market information and sentiment. Quantitative Finance9(8), 887-895.

 

The three types of factor models: A comparison of their explanatory power, Connor, G. (1995). The three types of factor models: A comparison of their explanatory power. Financial Analysts Journal51(3), 42-46. Multifactor models of security market returns can be divided into three types: macroeconomic, fundamental, and statistical factor models. Macroeconomic factor models use observable economic time series, such as inflation and interest rates, as measures of the pervasive shocks to security returns. Fundamental factor models use the returns to portfolios associated with observed security attributes such as dividend yield, the book-to-market ratio, and industry identifiers. Statistical factor models derive their pervasive factors from factor analysis of the panel data set of security returns. This paper compares the explanatory power of these three approaches for U.S. equity returns.

Is there a green factor?, Chia, C. P., Goldberg, L. R., Owyong, D. T., Shepard, P., & Stoyanov, T. (2009). Is there a green factor?. Journal of Portfolio Management35(3), 34.

 

Active risk and information ratio, Qian, E., & Hua, R. (2006). Active risk and information ratio. In The World Of Risk Management (pp. 151-167). One of the underlying assumptions of the Fundamental Law of Active Management is that the active risk of an active investment strategy equates estimated tracking error by a risk model. We show there is an additional source of active risk that is unique to each strategy. This strategy risk is caused by variability of the strategy’s information coefficient over time. This implies that true active risk is often different from, and in many cases, significantly higher than the estimated tracking error given by a risk model. We show that a more consistent estimation of information ratio is the ratio of average information coefficient to the standard deviation of information coefficient. We further demonstrate how the interaction between information coefficient and investment opportunity, in terms of cross-sectional dispersion of actual returns, influences the IR. We then provide supporting empirical evidence and offer possible explanations to illustrate the practicality of our findings when applied to active portfolio management.

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