do common stock quality ratings predict risk?

CFA Institute

Do Common Stock Quality Ratings Predict Risk?Author(s): Robert A. HaugenSource: Financial Analysts Journal, Vol. 35, No. 2 (Mar. - Apr., 1979), pp. 68-71Published by: CFA InstituteStable URL: http://www.jstor.org/stable/4478229 .

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by Robert A. Haugen

Do Common Stock Quality Ratings

Predict Risk?

o Can quality ratings of the type provided by Standard & Poor's predict the subsequent price behavior of common stocks? The author ranked all New York Stock Exchange stocks rated by Standard & Poor's on the basis of the latter's quality ratings and constructed portfolios based on these rankings. He then examined the degree of correspondence between the original quality rankings of the portfolios and their subsequent price risk, employing as his risk measures beta, which measures sensitivity to general market fluctuations, and the variance of monthly rates of return, which incorporates both market and non-market risk.

The author found perfect correspondence between quality ratings and subsequent beta, and nearly perfect correspondence between ratings and variance. Furthermore, the dispersion of returns on the individual stocks within each portfolio displayed nearly perfect correspondence with the portfolio's average quality ranking. ,

A BROADLY based empirical study aimed at assessing the predictive power of the quality ratings of the investment advisor, Standard &

Poor's Corporation, indicates that quality ratings may be used successfully by the portfolio investor as an input in assessing the future relative risk of common stock portfolios. The results, reported in this article, show that Standard & Poor's was able to group stocks into seven classes in 1956 and correctly rank those classes in terms of their relative systematic risk over the (then) future period, 1956 through 1971. With the literature documenting the. inability of investment advisors to predict the future relative profitability of firms over comparatively

short periods of time, this evidence of the predict- ability of relative risk over a much longer period may be a source of encouragement to professional analysts.1

Design of the Test

Our study concentrated on the quality rankings of Standard & Poor's Corporation as given in their Se- curity Owners Stock Guide. Concentration on a single service, rather than on the consistent rankings of several services, enables the observation of a larger number of issues. And observing the performance of a broad spectrum of issues provides a stronger test of accuracy than observing only those issues constantly ranked by the major services.

The sample included 806 New York Stock Ex- change common stocks-all stocks ranked by Stan- dard & Poor's in 1956 and listed on the Exchange as of that date-observed on a month-by-month basis over the period December 1956 through December 1971. We obtained the data from the summary price relative tapes of the Center for Research on Security Prices of the University of Chicago, as updated by Standard & Poor's Corporation.

From the 806 individual stock issues, we constructed seven portfolios based on the quality rating of each stock in 1956 (A+, A, A-, B+, B, B- and C). All the stocks in each portfolio were uniform with respect to their 1956 quality rating, but we made no attempt to assure uniformity in subsequent periods; to do so would have biased the results in favor of the accuracy of the rating service.

For each portfolio, we assumed an equal amount of money to be invested in each stock. Once placed in a portfolio, an issue remained there unless delisted, in which case it was removed from the portfolio in the month in which delisting occurred. We then observed the performance of each portfolio over the 1956-71 period to determine the degree of association between the 1956 quality rating and the subsequent risk of the portfolio.

II each month, we computed the rate of return to the portfolio as:

r= Dt + Pt-Pt-i Pt-i

where:

Dt = the total dollar amount of cash dividends Robert Haugen is Professor of Finance at the Grad- uate School of Business of the University of Wisconsin, Madison. 1. Footnotes appear at end of article.

68 O FINANCIAL ANALYSTS JOURNAL / MARCH-APRIL 1979



distributed in the month to all stocks in the portfolio,

Pt the total market value of the portfolio at the end of the month, and

Pt= the total market value at the beginning of the month.

We also computed the following statistics for each portfolio: The Arithmetic Mean of the Monthly Rates of Return

n

r E/ rt,(1

The Geometric Mean of the Monthly Rates of Return n l /n

GR [I (1 + rt) -1 , (2)

The Standard Deviation of the Monthly Rates of Return

[_i'nin 1/2 Sr.- _ l/n-1 (rt_i)2 (3)

t~~~~~~ The Beta of the Portfolio

Cov(r, rm) (4) rm

where rm is an index of the average rate of return to all stocks on the New York Stock Exchange (as given by Fisher's Investment Performance Index).

An observer would expect to find a greater degree of heterogeneity in the performance of stocks in the higher risk classes. We computed the following statistic to measure the degree of cross-sectional dispersion in the individual rates of return to the common stocks populating each portfolio:

The Average Dispersion of Rates of Return Within the Portfolio

Srj = 1 /n : [l/m-1 1(rj,t-ij:,t)2] ,(5) t=l j=l

where:

Sr. = the average of the monthly standard devia- tions of the rates of return across the stocks

in the portfolio, n = number of months observed, m = number of stocks in the portfolio in any

given month, rj,t= the rate of return to thejth common stock in

month t, and r=,t the arithmetic mean rate of return to all

stocks in the portfolio in month t. If the quality ratings are useful in estimating future risk, one should find a high degree of correspondence between the original ranking of the stocks in each portfolio and the risk of the portfolio (as given by Equations 3, 4 and 5) in later periods. We also examined the relation between the quality ratings and the geometric average rate of return, even though (as we discuss below) the nature of this relation is highly dependent on the performance of the general market in the period examined.

Results

Table I presents the statistics produced by the test. The results indicate a perfect correspondence between the ratings and portfolio risk as measured by portfolio beta, ,B. We also note a near-perfect correspondence between the ratings and the variance of the month-to-month rates of return, $t, and between the ratings and the average, interstock dispersion of rates of return within the portfolios, Sr,. It is ap- parent from the latter statistic that the stocks that ranked lower in quality in 1956 exhibited a greater degree of heterogeneity in their relative monthly rates of return over the subsequent period.

There is also a near-perfect match-up between the 1956 ratings and the survival rate of the stocks in each portfolio; a larger percentage of the lower quality issues were delisted over the 1956-71 period. One should be cautious in interpreting this particu- lar statistic, however, since the delisting of many of these stocks was the result of their acquisition by other firms, rather than a deterioration in their financial condition.

The systematic risks (J3) for the full period (1956-71) and for the first (December 1956 - Sep- tember 1964) and second (October 1964 - Decem- ber 1971) halves are graphed in Figures A, B and C,

TABLE 1: The Relation Between Original Quality Rating and Subsequent Risk and Return

1956 Number of Stocks Survival Quality Rating 1 Srt S r Beginning End Rate Gr r

A+ 0.769 0.00140 0.0033 102 81 0.79 0.0071 0.0078 A 0.779 0.00139 0.0047 149 111 0.74 0.0082 0.0089 A- 0.803 0.00143 0.0040 130 92 0.71 0.0093 0.0100 B+ 0.938 0.00191 0.0054 198 106 0.54 0.0089 0.0099 B 1.127 0.00283 0.0084 93 51 0.55 0.0110 0.0124 B- 1.208 0.00331 0.0094 74 31 0.42 0.0098 0.0114 C 1.378 0.00457 0.0106 60 22 0.37 0.0093 0.0115

FINANCIAL ANALYSTS JOURNAL I MARCH-APRIL 1979 O 69



Figure A: Figure B: The Relation Between Systematic Risk The Relation Between Systematic Risk

And Original Quality Rating And Original Quality Rating Over Entire Period Over First Half

1.378 1.368

.17 1.208 (31.127 1.128

(30 1. 127.0-0.6 1.0

~ ~ .0.38 100.832 0.845 0.866096

0.769 0.7790.3

0.5 2 . , H B B 0.5-B B A |A+ O rA 15 Qu a Bi BRating A 15 Q R a ti n

Original 1956 Quality'Rating Original 1956 Quality Rating

Figure C: The Relation Between Systematic Risk

And Original Quality Rating Over Second Half

1.342

1.112 1.112

1.0 0.953

0.826 0.837 0.854

0.5-

A + A A- B+ B B- C

Original 1956 Quality Rating

respectively. The 1956 quality rankings are about as successful in predicting relative systematic risk for the second half of the period as they are for the first half, or the full period. This indicates a high degree of stationarity in the risk measure, and is consistent with the results of previous research.2

The inherent stability of the risk parameter provides a possible explanation for the relatively high degree of success observed in the prediction of future risk. As mentioned above, research has shown professional forecasters to be less than successful in predicting relative rates of growth of earnings per share. However, it has also shown that the ranking of firms with respect to the rate of growth in earnings displays an erratic pattern over time, making prediction of future relative rates of growth difficult.3 If firms are indeed more consistent in their relative risk positions, one might expect professionals to be more accurate in their forecasts of this aspect of their performance.

Risk and Return Table I shows a weak, positive relation between

the risk of each portfolio and its geometric and arithmetic mean rates of return. However, the relation between a portfolio's risk and its realized rate of return is a function of investor risk aversion and the nature of general market performance in the period observed; it reveals very little about the accuracy of forecasts of the relative risk of stocks. If, given expected rates of return, investors show a preference for low-risk shares, then they may require a premium

in the expected rates of return to stocks of higher risk. Under these circumstances, one may find a positive relation between risk and expected rates of return.

Although an investor might expect this relation to reflect itself in the relation between risk and average realized rates of return, research has demonstrated that the pattern of realized rates across a group of portfolios may give a distorted image of the actual

70 O FINANCIAL ANALYSTS JOURNAL / MARCH-APRIL 1979



relation between risk and expected rates of return. If realized rates of return on average exceed those expected for the period, the observed relation between risk and realized return exaggerates the true relation between risk and expected return.4 Risky stocks re- spond strongly to the pull of the market; it is not surprising that they outperform their less risky coun- terparts in a bullish market. For the same reason, it is also not surprising that they yield low rates of return in bearish markets.

The observed relation between risk and realized rates of return thus depends very much on the nature of overall market performance in the period observed. Although we may hope that we picked for our examination a period when, on average, realized results approximated expectations, we have no proof of this. It is thus unclear what economic meaning can be attached to the observed relation between risk and geometric mean rates of return. E

Footnotes 1. See, for example, Burton G. Malkiel and John G.

Cragg, "Consensus and Accuracy of Some Predictions

of Growth of Corporate Earnings," The Journal of Finance, March 1968.

In a previous article in this journal, Robert M. Soldofsky concluded that the quality ratings prepared by the major investment advisory services are unsatis- factory guides to the relative risk of common stock. However, given the nature of his research design, a more appropriate conclusion would be that the services look to other information in addition to the historical series of annual rates of return in order to assess future risk. See Robert M. Soldofsky, "Yield- Risk Performance Measurements," Financial A nalysts Journal, September/October 1968, p. 130.

2. Marshall Blume, "On the Assessment of Risk," The Journal of Finance, March 1971, p. 1.

3. See I.M.D. Little, "Higgledy Piggledy Growth," Ox- ford Institute of Statistics Bulletin, November 1962, p. 387. Also see John Lintner and Robert Glauber, "Higgledy Piggledy Growth in America," in Modern Developments in Investment Management, Lorie and Brealey eds. (New York: Praeger Publishers, 1972) pp. 645-662.

4. For a proof of this see R.A. Haugen and A.J. Heins, "Risk and the Rate of Return on Financial Assets: Some Old Wine in New Bottles," The Journal of Fi- nancial and Quantitative Analysis, December 1975.

WI 1999th Consecutive "-,,,, r;.. uarterly

LXi 5=Common Stock Dividend

Beneficial Corporation has announced per share dividends payable March 31, 1979 to stockholders of record at the close of business March 5, 1979.

LMJ _Common Stock- Quarterly - $.45

* $4.30 Dividend Cumulative Preferred Stock- Semi-annual -$2.15

* -

U- Payable April 30, 1979 to stockholders of record at the close of business April 6, 1979.

$5.50 Dividend Cumulative Convertible Preferred Stock-Quarterly -$1.375

February 22, 1979



do common stock quality ratings predict risk?

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