Value line investment survey rank changes and beta coefficients

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BEBR

FACULTY WORKING
PAPER NO. 1288

Value Line Investment Survey Rank
Changes and Beta Coefficients

Cheng F. Lee
HunY Park

College of Commerce and Business Administration
Bureau of Economic and Business Research
University of Illinois, Urbana-Champaign

BEBR

FACULTY WORKING PAPER NO. 1288
College of Commerce and Business Administration
University of Illinois at Urbana-Champaign
September 1986

Value Line Investment Survey Rank Changes and Beta Coefficients

Cheng F. Lee, IBE Distinguished Professor
Department of Finance

Hun Y. Park, Assistant Professor
Department of Finance

Digitized by the Internet Archive

in 2011 with funding from

University of Illinois Urbana-Champaign

http://www.archive.org/details/valuelineinvestm1288leec

Value Line Investment Survey
Rank Changes and Beta Coefficients

Abstract

We use a Value Line rank varying market model to test the existence
of a possible systematic association of Value Line ranks with the beta
coefficients of securities. The results indicate that about 57 percent
of the companies' betas in the sample are associated with Value Line
ranks and that these firms are in general small. It is also found that
the mean and the volatility of Value Line ranks per se are negatively
and positively related to the beta coefficient, respectively.

Value Line Investment Survey Rank Changes and Beta Coefficients

The information content of Value Line Investment Survey rank
changes has attracted considerable attention of financial academicians
as well as security traders. A number of studies have analyzed the
performance of Value Line ranking system. The main conclusion of pre-
vious studies, with few exceptions, is that Value Line rank changes
have better ability to predict stock price movements than asset pricing
models, i.e., several versions of the CAPM. In other words, an
investor can generate excess returns even net of transaction costs by
following the Value Line rank changes. This, being called the "Value
Line enigma," has been used as a typical example against the semi-
strong form of the market efficiency hypothesis. If the market is
efficient, stock prices instantaneously adjust to reflect all publicly
available information including Value Line rank changes and that
knowledge of such information cannot lead to excess returns.

The purpose of this paper is to investigate the association of
Value Line rank changes with security beta changes, in an attempt to
explain how and why the Value Line enigma has been observed. To test
the systematic relation between security beta changes and Value Line
rank changes, a specification analysis technique is used. The next
section describes the model for testing the association of Value Line
ranks and the security beta. In the third section, we describe the
data and present empirical results. The last section contains a brief
conclusion.

■2-

Methodology

The empirical version of the market model to estimate the beta

2
coefficients of securities can be written as

R.to - o. + B.Jl . + e4t_, (1)

jt J jt mt jt'

where R. = the rate of return on security j in period t,

R = the rate of return on market portfolio m in period t,
mt

8. = the beta of security j in period t, and

e . = the disturbance term for security j , which is assumed to have
mean zero and constant variance.

If the beta of security j is related to Value Line rank changes, we may

specify the beta as a functional form of Value Line rank as:

8. = 8. + Y.V. , (2)

Jt J J Jt

where V. represents Value Line rank of security j in period t.

Then, substituting equation (2) into equation (1), we have a Value
Line rank varying market model as

R.„ = a. + 6 .R . + y.(V. *R „) + t. . (3)

Jt j J mt 'j jt mt jt

The variable, V. R , in equation (3) "can be interpreted as an inter-
J t mt

action variable reflecting the association of Value Line rank with the
time varying beta. If the coefficient, y., is not equal to zero, we
may interpret that the market reacts to Value Line ranks and thus the
Value Line rank has extra explanatory power for forecasting the beta
coefficient and the rate of return of the security. In addition, to
obtain the average relation between the Value Line rank and the beta,
the following two cross-sectional regressions will be run as:

-3-

M . = a + b6 . (4)

a , = a' + b'B . , (4)'

vj i'

where M . and a . represent the mean value and the standard derivation
Vj vj

of Value Line ranks of security j, respectively.

Data and Empirical Results

Weekly ranks of all securities of Value Line were secured for the
period July 1978-February 1983. Five ranks are provided by Value Line
depending on the expected price performance over the next 12 months.
Ranks 1 and 5 represent the best and the worst securities, respectively.
Excluding the firms not included in the CRSP monthly files, we obtained
1331 companies. Monthly rate of returns on the individual securities
and the value weighted NYSE index are used to estimate the coefficients
of equation (3). For V. in equation (3), monthly average of weekly
Value Line ranks are used.

Through examination of t-statistics of the coefficients in eq.
(3), we find that 189 firms have y. significantly different from zero
at the 5 percent level. The names of these companies are listed in
Appendix A. To save space, the empirical results of only the first 32

companies in alphabetical order are listed in Table 1 for an exhibition

3
purpose.

For example, the American International Company's beta can be

decomposed into two components — the constant component, 3.477, and the

Value Line rank related component, -.583. In other words, the re-

sonsive coefficient of beta to Value Line rank is -.583 for American

-4-

International Company, and thus one percent increase in Value Line rank
causes .583 percent decrease in the company's beta.

Insert Table 1 about here

In addition, we also find that 567 firms have t-statistics for
Y. coefficients larger than one. In statistical sense, this number of
companies, 756 (567 plus 189), certainly implies that the market per-
ceives Value Line ranks as an important source of information in
pricing securities. It is interesting also to note that most of these
756 companies are small in terms of size. Therefore, it appears that
the smaller the size of the firm, the greater the impact of Value Line
ranks on the determination of the beta. More importantly, most of y.
coefficients are negative (even in other companies not reported here) ,
suggesting that the Value Line rank is negatively related to the rate
of return. The lower the rank, the better the projected performance of
the security and thus the higher return (note that rank 1 represents
the security which is projected to perform best). This result is con-
sistent with the findings in previous studies on the performance of
Value Line. However, this paper shows that the result may be through
the association of Value Line ranks with the beta. This is confirmed
by examining the coefficients of equation (4).

The results on the cross-sectional regressions in (4) are shown in
Table 2. The beta in Table 2 was estimated using monthly rate of
returns on individual securities and the NYSE index, based on equation

(1). M . and a . were calculated using weekly Value Line ranks. As

vj vj * '

expected from negative coefficients of y . , in general, in Table 1, the b

-5-

coefficient in Table 2 is significantly negative. More interestingly,
the results in Table 2 also suggest that the volatility of Value Line
ranks per se is positively related to the beta. The b' coefficient in
equation (4)' is .1490, which is significant at the 1 percent level.
Since the beta is a measure of the volatility of a security relative to
the market and the Value Line rank is a relative measure of projected
performance of individual security, it is not surprising that the beta
is positively associated with the volatility of the Value Line rank.

Conclusion

A number of previous studies have shown outstanding performance of
Value Line ranking system. We use a Value Line rank varying market
model to test the existence of a possible systematic association of
Value Line ranks with the beta coefficients of securities. Using
weekly ranks of 1331 companies for July 1978-February 1983, we find
that about 57 percent of the companies' betas are associated with Value
Line ranks and that these firms are in general small. This finding
provides an insight into how Value Line rank changes affect the indivi-
dual firm's stock price. It is also found that the mean and the vola-
tility of Value Line ranks per se are negatively and positively related
to the beta coefficient, respectively.

-6-

Footnotes

See, for references, F. Black, "Yes, Here is Hope: Tests of
Value Line Ranking System," Financial Analyst Journal 29 (1973), pp.
10-14; T. E. Copeland and D. Mayers, "The Value Line Enigma (1965-
1978): A case Study of Performance Evaluation Issues," Journal of
Financial Economics 10 (1982), pp. 289-321; C. Holloway, "A Note on
Testing an Aggressive Investment Strategy Using Value Line Ranks,"
Journal of Finance 36 (1981), pp. 711-719; S. Stickel, "The Effect of
Value Line Investment Survey Rank Changes on Common Stock Prices,"
Journal of Financial Economics 14 (1985), pp. 121-143.

2
See E. Fama, Foundations of Finance," Basic Books, New York

(1976).

3
The results of all other firms are available from the authors

upon request.

D/415

Table 1
Value Line Rank Varying Market Model

R. =a.+8.R +Y.(V R )+e
jt j .1 m,t jjCmt jt

Corporation 8 v
_1 J.

1. Am Int'l 3.477 -.583

(5.320) (-3.144)

2. Amr Corp 2.668 -.385

(5.526) (-2.176)

3. Aetna Life & Casualty 1.846 -.250

(7.124) (-2.907)

4. Albertson's, Inc. 1.501 -.319

(5.610) (-3.000)

5. Alcan Aluminum 1.964 -.290

(5.563) (-2.398)

6. Allegheny Int'l 2.832 -.457

(6.106) (-3.257)

7. Amerace Corp. 2.562 -.563

(3.722) (-2.779)

8. Amer. Broadcasting 2.025 -.396

(5.256) (-3.164)

9. Amer. Hoist Derrick 2.438 -.364

(3.611) (-1.935)

10. Amfac Inc. . -.418 .502

(-.757) ( 2.895)

11. Amrep Corp. -1.735 1.100

(-1.092) ( 2.188)

12. Anchor Hocking Corp. -.745 .371

(-1.386) ( 2.426)

13. Avon Products 1.535 -.235

(5.743) (-2.551)

14. Ball Corp. 1.632 -.347

(3.506) (-2.163)

Table 1 (cont'd.)

Corporation

B.
_1

15. Bandag, Inc.

16. Bk America

17. Baxter Travenol Labs

18. Best Products

19. Boeing Company

20. Braniff Int'l Corp.

21. British Petroleum

22. Brooklyn Union Gas

23. Burroughs Corp.

24. CCI Corp.

25. Caesors World

26. Campbell Red Lake

27. Central Soye Co.

28. Champion Int'l

29. Chasebrough-Ponds

-.339 .546

(-.594) ( 2.547)

1.563 -.243

(5.225) (-2.190)

.390 .334

(1.441) ( 2.296)

2.293 -.338

(6.199) (-2.569)

2.216 -.305

(6.652) (-2.406)

2.508 -.369

(5.805) (-3.227)

-.163 .293

(-.436) ( 2.512)

1.513 -.412

(2.653) (-2.259)

1.729 -.236

(6.360) (-2.795)

.236 .493

( .346) ( 2.009)

4.354 -1.124

(2.753) (-2.217)

1.451 -.433

(3.199) (-2.520)

3.234 -.627

(3.956) (-2.765)

2.034 -.312

(4.168) (-2.016)

.221 .217

( .870) ( 2.079)

Table 1 (cont'd.)

Corporation 6 . y .

30. Cities Service 2.107 -.273

(5.010) (-2.035)

31. City Investing 2.111 -.348

(3.796) (-1.936)

32. Coleman Co. Inc. 2.032 -.431

(4.502) (-2.811)

The numbers in parentheses represent t-statistics.

Table 2

Cross-Sectional Regressions Between Value Line Rankings and
the Betas of 1331 Companies*

M = a + bS .
vj J

vj

a' + b'S .
J

a

b

R2

D-W

a'

b1

_2

R~

D-W

3.251
(109.21)

-.2391
(-8.72)

.054

2.02

.6296
(43.07)

.1490
(11.06)

.084

2.01

*The numbers in parentheses represent t-statistics.

D-W stands for Durbin-Watson statistics,

-2 2

R represents the adjusted R .

Appendix A
The List of 189 Firms with Significant y Coefficient in Equation (3)

Am Int'l
Albertson's Inc.
Amerace Corp.
Amfac, Inc.
Avon Products
Bankamerica Corp.
Boeing Company
Brooklyn Union Gas
Caesars World
Champion Int'l Corp.
City Investing Co.
Cominco Ltd.
Cox Communications
Dana Corporation
Deltona Corp.
Disney (Walt) Prod.
El Paso Co.
Equitable Life Mortg.
Federal-Mogul
First Mississippi
Fuqua Ind.
General Cinema
Giant Portland Cement
Gulton Ind
Hewlett-Packard Co.
Hoover Universal Inc.
Ingersoll-Rand Co.
Int. Reetifier Corp.
Jamesway Corp.
Kdt Ints

Kyocera Corp. (ADR)
Leverage Fund Boston
MCA Inc.

Manhattan Industries
Masco Corp.
Miller-Wohl
National Gypsum
Niagra Shave Corp.
Nortek Inc.
Olin Corp.

Overseas Shipholding
Pennzoil Co.
Petroleum & Res. Corp.
Pioneer Corp
Polaroid Corp.

Amr Corp.
Alcan Aluminum
Amer. Broadcasting
Amrep Corp
Ball Corp.

Baxter Travenol Labs
Braniff Int'l. Corp.
Burroughs Corp.
Campbell Red Lake
Chesebrough Ponds
Coleman Co. Inc.
Computer Sciences
Crane Co.

Data General Corp.
Dennison Mfg.
Diversified Ind.
Electronic Assoc
Evans Products Corp
Figgie Int'l
First Natl St Bancor
Gatx Corp
Gerber Products
Giddings & Lewis
Handleman
Hillenbrand Inds.
Hunt (Phil A) Chem
Interco, Inc.
Interstate Baker
Jewel Companies
Kansas City Southern
La Quinta Motor Inns
Libby Owens Ford
Macmillan Inc.
Map Co. Inc.
Mclntyre Mines Ltd.
Monarch Machine Tool
New York Times
Nicor, Inc.
Northwest Airlines
Oneok Inc.

Pacific Gas & Electric
Peoples Drug Store
Philip Morris
Pitney-Bowes
Ponderosa, Inc.

Aetna Life & Casualty
Allegheny Int'l
Amer. Hoist & Derrick
Anchor Hocking Corp.
Bandag, Inc.
Best Products
British Petroleum
ECI Corp.
Central Soya Co.
Cities Service
Colonial Penn Group
Copperweld Corp.
Cross (A.T.)
Deere & Co.
Dial Corp.

A G Edwards and Sons
Empire Distric Elec
Far West Financial
Fin'l Santa Barbara
Fruehauf Corp
Gemini Fund
Getty Oil
Gleason Works
Harsco Corp.
Hilton Hotels
Imperial Oil Ltd. "A"
Int'l Flav & Frag
Interstate Power
Johnson & Johnson
Kennametal Inc.
Lennar Corp.
Lockheed Corp.
Madison Fund
Maryland & Cup Corp.
Metromedia, Inc.
NVF Co.

Newmont Mining
Norlin Corp
Northwest Energy
Opelika Mfg.
Parkers Pen Co.
Peoples Energy Corp
Piedmont Nat. Gas
Pneumo Corp.
Presly, Cos.

Appendix A (cont'd.)

Onanex Corp.
Revere Copper & Brass
Rockwell Int'l
SPS Technologies
Scoa Ind.
Sears, Roebuck
Smith (A.O. ) Corp
Southern Union Co.
Suave Shoe Corp.
Swank Inc.
Talley Ind.
Texfi Industries
Travelers Corp.
United Brands
U.S. Shoe Corp.
Vista Resources Inc.
Washington Gas Light
Western Pacific Ind.

RCA Corp.
Revlon, Inc.
Ronson Corp.
Sabine Corp
Scott & Fetzer Co.
Shell Transport
South Jersey Ind.
Standard Oil (Ind. )
Sunbeam Corp
Teco Energy Inc.
Tesoro Petroleum
Tiger Int'l Inc.
Tri-South Invert Inc.
U.S. Gypsum Co.
Univar
Wainoco Oil
Washington Nat'l. Cp.
Wheeling-Pitts burgh

Reeding & Bates
Rexham Corp.
Ryan Homes
Santa Fe Industries
Scottys Inc.
Simmonds Prec. Prod's
Southern Pacific
Stop & Shop Cos
Superior Oil
Taft Broadcasting
Texaco, Inc.
Tokheim Corp.
Union Oil Co. Calif.
U.S. Industries
Valley Nat'l Corp
Warnaco Inc.
Westwart Transm'n

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