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A simulation test of the industry analysis hypothesis
A SimuZution Test of AnaZyd8 EypatZleais*
the ha4zuatry
0. Maurice Joy, Universi8yof Kamas, Lawrence
Is industry analysis a nezssary step in a security evaluation process? Some finance studies support its usefulness; others do not. This article further investigates the question by. reporting a test which compares returns generated from an investment strategy using industry analysis with those from a strategy omitting industry analysis. The article begins with a review of the opposing views of the utility of industry analysis, then presents an alternative approach to investigating the problem and the results of a study using the approach. The ReZevance of Industry AnalyaM The notion that industries consist of companies homogeneous with respect to certain importaut attributes is derived from microeconomic theory. The theory asserts that for many problems, firms can be classified into industries, the groupings being such that attribute similarities within these groups are more important than dissimilarities. Gertainly this notion is true in at least some instances. But, conversely, classification into industries is irrelevant in instances where dissimilarities are more important than similarities. In security analysis there is no concensus on the role of industry security analysis. Some assert that industry analysis serves a useful purpose in the 0WraZZevaluation scheme while others maintain that industry analysis is unnecessary, Orthodox security evaluation asserts that industry analysis is a necessary part of the evaluation process and the investment literature is replete with allusions to this perceived importance. For example, in 1939 Mead and Grodinsky expressed an extreme and a dated view: “Beside the selection of the industry, other considerations of investment are unimportant” [2L p. 41. Volume2, Number4
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in common stocks that one should first select the most promising in. dustry or industries end then pick out the best companies in those industries” [ll, p. $71. Markowitz [20] also supports industry appraisal. Having established that an adequate number of securities is necessary for diversification, he qualified this finding by distinguishing between mere diversification and diversification for the “right” reason: The adequacyof diversificationis not thoughtby investorsto depend on the number of different securities held. A portfolio with sixty different railway same
securities.
likely
for example,
size portfolio
ous sorts
with
some
of manufacturing,
for firms
within
would
the same
than for firms in dissimilar
not be as well
railroad, etc. The
some reason
industry
industries
diversified
as the
public
utility,
mining,
vari-
is that
it is generally
more
to do poorly
at the same
time
[ZO, p. 891.
While “proper” diversification need not require formal industry analysis, Markowits’s last sentence clearly is consistent with the literature advocating industry analysis. Latan6 and Tuttle [18] investigated interindustry security evaluation within the broader context of formulating investment relative prob. ability beliefs for common stocks. Their approach is the fundamental evaluation scheme of sequential market analysis, industry analysis and individual company analysis. This framework, especially the order of the last two steps, embodies the industry analysis philosophy and reinforces Graham. Dodd and Cottle;. i.e., se&t the best inr’ustries and then select the best set&ties within those best industries. Commenting on the usefulness of industry analysis, Latane and Tuttle noted wide interindustry differences in performance over the period 1950 to 1%7, indicating that industry analysis was potentidly useful? These differences are a necessary, but not sufficient condition for industry analysis relevance. If there were no differences between industry returns, industry analysis clearly would be useless. More recently Reilly [23) reemphasized the role of industry analysis witbin the traditional security analysis framework. He advocated the necessity of industq analysis and strongly urged that the proper order of analysis is from market to industry to company. .&me recent empirical studies also lend support to the validity of industry analysis. King [14] found that for the stocks in the six
Simulation Test of Industry Analysis Hypothesis
dustry ix&x numbers (rather than first differences) and-( 2) tbe use of aggregate industry prices rather than individual stiurity prices. Tbe Gaumnitz study ignored the potential differential effect of information flows in noI*price series, such as earnings. By concentrating solely on price movements, the impact on price of new information is necessarily confounded with random movements in tbe residual price series.’ These strictures are the liturgy of empirical analysis; however, tbe basic issue is still unresolved. This study addresses tbe same problem, but from a different perspective.
A PIE Investment Strategy Approach Most of the empirical studies described above sbare the common feature of looking for discernible industry effects in equity investment relatives. An alternative way to investigate the industry analysis hypothesis is to devise a test whi& compares returns generated from competing investment strategies, one using industry analysis, the other ignoring industry analysis. Of course, such an approach requires specification of
376
an investment strategy and this specification squarely confronts the efficient market hypothesis in semistrong form, Very little empirical evidence has been presented that is contrary to the efficient market hypothesis in semistrong form.s/Such contrary evidence deals almost exclusively with quarterly earnings reports.’ These studies provide a convenient benchmark for testing the industry analysis hypothesis. In particular, since quarterly earnings appeared to be useful in ex ante security selection over a common time period covered in several of the studies (the late l%Os), it seems reasonable to use quarterly earnings models in formulating investment strategies over a similar time period in testing the industry analysis hypothesis. The two-strategy investment comparison used here is patterned after a simulation study performed by Breen [Z]. Breen suggested using P/E ratio investment strategies in teating the industry analysis hypothesis. Breen used company P/E ratios to identify undervalued securities in both industry and nonindustry stock selection strategies. The nonindustry strategy identified as undervalued stocks those companies with lowest positive P/E ratios. The industry strategy chose stocks whose (positive) P/E ratios were lowest relative to their industry P/E ratio. He found the nonindustry strategy performed slightly better, indicating that industry analysis is umrecessary, at least in the context of using P/E ratios as ex ante predictors of investment relatives. Unfortunately, several aspe&s of Breen’s study are unsstisfactory. First, his industry strategy appears inconsistent with the traditional industry analysis approach described above. Breen’s technique involves picking companies having low (positive) P/E ratios relative to their industry P/E ratio. A strategy more consistent with the Craham-DoddCottle and Reilly concept of appropriate industry analysis envisions a two-step process: first choose the best industries, then pick the stocks within those best industries. In the context of P/G ratio analysis this approach would imply first selecting the lowest positive P/E ratio industries, and then picking the lowest positive P/E ratio stocks within those industries. A second disturbing aspect of the Breen study is his use of “forward information” in his simulation. He used published annual reports to form P/E ratios and these P/E ratios were formed on January 1 of each year in his study. But this erroneously presumes that annual earnings figures are available to investors on January 1. Cousequently, Breen was implicitly using “forward information,” information not actually available to a hypothetical investor on January 1. This study avoids this deficiency.& A third aspect of Breen’s study warranting comment is his failure to adjust for risk, In a risk averse stock market observed differences between realiaed rates of return may only be differences attributable to
Simulution Test of hduatry Andyst
Hypothesis
risk premia. Been did not investigate the differential risk (if any) between his twe competing selection strategies, leaving unanswered the issue of risk adjusted rates of return comparison. This study corrects for this deficiency hy investigating both the Sharpe [24] and Jensen [ 131 risk adjustment performance measures.
Data for this study were taken from two sources. Industry data were taken from Standard and Poor’s The Analyst’s Handbook [25]. This monthly publication contains per share quarterly sales, taxes, earnings and dividends for industry group indexes adjusted for stock splits and dividends. Monthly high, low and close prices and selected financial ratios also are published. Quarterly earnings and dividends and monthly prices were collected from this source for the 71 selected industries listed in the Appendix. A total of 23 industries was excluded from the study for one of the following reasons: 1. The indus”q was a composite of several industries. ; In the industry, 2. Except for exclusion of the largest cornpan_ the industry was a duplicate of another-full industry set. 3. The industry did not report quarterly earnings. 4. The industry had no companies on the Standard and Poor’s Compustat tapes. The industry data collected covered the time period September, 1%5
through June, 1969. Company data were taken from a Standard and Poor’s Quarterly Compustat tape dated March, 1970. Quarterly earnings, dividends and monthly prices, a11 adjusted for stcck splits and stock dividends, were collected for 955 companies with the only exclusion criterion being a December fiscal year closing. Industrial classifications of the companies were cross cbcked against The Analyst’* Handbook classifications. Firms in none of the 71 Handbook industry classifications were retained in the study, hut only for testing in the nonindustry investment strategy. Simulation Procedure
The investment simulation procedure as used here is merely a method of repetitively choosing two alternative portfolios of stocks. These repetitive choices result in two time series of portfolio returns which can be compared. From this comparison inferences can he drawn concerning the industry analysis hypothesis. Ebtplanation of this twostrategy investment simulation is best done hy example. Assume that the date is June 30,1%7. A hypothetical investor has the June, 1967 Handbook Monthly Supplement and calculates P/E
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ratios for all of the 71 designated industries in the Supplement that bave preliminary or complete first quarter I.967 earnings reports. These ratios are formed for each industry by dividing its first quarter, 1967 earnings into its June 34 1%7 price. Coincidental with these calculations, P/E ratios are calculated for all of the 955 companies with announced first quarter 1967 earnings. These ratios are formed by divi&ng each company’s first quarter, 1967 earnings into its June 30, 1967 price.” The competing stock selection schemes now are developed as follows: A. Nonindustry Analysis Approach All companies are rank ordered on the basis of the algebraic value of their P/E ratio (described above) and the 10 best positive P/E stocks are taught (in equal dollar amounts) at the closing June 30, 1967 price.’ This portfolio of stocks is held three months and then sold on September 30, 1967. The portfolio investment relative over this three-month period is the arithmetic mean of the 10 individual stock investment relatives. Each stock’s in. vestment relative is the sum of its September 30, 1967 price plus any dividends paid between July 1,1%7 and September 30,1%7 inclusive, divided by its June 30, 1%7 closing price. B. Industry Analysis Approach All industries are rank ordered on the basis of the algebraic value of their P/E ratios and the 10 lowest possitive P/E industries are identified. Then, within each of these 10 industries, companies are rank ordered on the basis of the algebraic value of their P/E ratios. Within each of these 10 separate intraindustry rank orderings the company with the best (lowest positive) P/E ratio is identified. The resultant 10 stocks are bought (in equal dollar amounts) at the closing June 30, 1%7 price and put into a portfolio. This portfolio is held three months and then sold on September 30, 1967. Tbe determination of the portfolio investment relative over this three-month period is identical to the nonindustry analysis approach described above. The two illustrative procedures used first quarter 1967 earnings as the starting point. Each of the 15 quarters or investment “decision periods” used in the study would have similar calculations associated with it. To genera?ize the relationship between P/E ratios and investment relatives, let E denote earnings, P denote price, ID denote dividends and let the subscript t refer to time (in quarters). Let the time reference be at the investor’s buy date. By construction then, the P/E ratio variable is defined as P@t_I. Note that the price and earnings have different subscripts; i.e., the price is a current price (June 30, 1967 in the example above-coinciding with the buy date), but the earnings variable is lagged one quarter (first quarter 1%7 in the example) to reflect the reported lag of earnings. The
Simulation Test of Industry Analysis Hypothesis
379
subsequent investment relative associated with the decision variable Pt/E,_, is (P,+l + Dt+l)/P,, where Dt+* refers to the quarterly dividends received during the quarter subsequent to the buy date (in the example, third quarter 1%7 dividends).’ Each calendar quarter used in the study constitutes one decision period. Fifteen such quarters enter into the s&dy. Repetition in each quarter of the rank order, buy and then sell simulation procedure generates a time series cf investment relatives for each portfolio. These investment relative series are analyzed in the next section. Before turning to a detailed analysis of the portfolio investment relatives, it is interesting to noie the extent to which the two alternative portfolio strategies selected the same sepuities. In four of the 15 periods no stocks were in both portfolios, in five periods one stock was in both, in another five periods two stocks were in both and in one period three stocks were in both portfolios. The relatively low incidence of dual selection indicates that the two strategies are somewhat independent. Results Investment relative time series for the two strategies are shown in Table 1 along with a comparable series of Standard and Poor’s 500 Investment relatives. This latter series provides perspective regarding the types of quarterly markets prevailing over the period from late 1965 to mid-1969.8 Summary risk and return measures also are shown in Table 1. The objective of the research is to determine whether or not portfolio performance is enhanced by industry ‘analysis. Consequently, in all instances of comparison the following hypotheses are tested: HO: II (Industry) = II (Nonindustry) Hl: II(1ndustr-y) > II(Nonindustry) where II is investment return (however defined), and the “Industry” and “Nonindustry” designators refer to industry and nonindustry portfolio strategies, respectively. Arithmetic mean returns, which do not reflect risk differences, provide no basis for the rejection of the null hypothesis of equal returns. Indeed, the nonindustry strategy portfolio has the higher arithmetic mean. The next level of analysis compares geometric mean returns Of the two competing strategies. The geometric mean return has been advocated by Latank [15] as a plausible operational goal of portfolio management on the grounds that, in the long run, maximization of the geometric mean of the probability distribution of portfolio avoids ruin (see Hakansson [U] ) and leads to larger terminal wealth than would
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Table 2: Portfolio Simulation Results
Rcmuch
’
Investment Relative
= Rt= (Pt+l + Dt+l)/Pt
----Decision Period
Industry Strategy Portfolio
Time Period
1 2 3 4 5 6
9/30/G - 12/31/65 12/31/65 - 3/31/66 3/31/66 - 6/30/66 6/30/66 - Y/30/66 9/30/66 - 12/31/66 ~2/31/66 - 3/31/67 :/31/6? - 6,‘30/67 e/30/67 - 3/30/67 I/30/67 - 3Z/31/67 K/31/67 - 3/31/68 In/31/68 - 6/30/68 (t/30/68 - 9,‘30/68 Y/30/68 - 12/31/68 1:!/31/68 - 3/31/69 .x$/31/69 - 6/30/69
8 9
10 11 12 13 14 15
Nonindustry Strategy Portfolio
1.198 1.108 .896
1.174 1.155 1.065 .860 1.035 1.365 1.133 1.131 1.045 .979 1.474 1 .OSl
.830
1.006 1.349 1.143 1.198 .996 .976 1.307 1.098 1.184
1.269
.953
.911
.905 .895
G
1.080 1.071
1.102 l.OYO
n
.149
RV o (oo) Notes
.455 .0099 (.022)
.I.72 .5:4
.029 (.032)
s E P 500 Portfolio 1.058 .998 ,981 .937 1.087 1.159 1.059 1.100 1.028 .96.5 1.137 1.051 1.041 1.007 .993 1.041 1.039 .063 .460 _-
:
Y
is
the arithmetic
mean:
v = (“t Rt)/15
G is the geometric mean:
G=
o is the standard deviation:
Cl=
RV is Sharpe’s
m-
[24] Reward to Variability
Ratio:
RV = (u - 1.012)/o
The three-month mean interest rate, 1.012, expressed as a three-month investment relative. was determined by averaging the relevant three-month rates over the time period in the study. o is Jensen’s [13] measure of risk adjusted performance. It is the intercept of the regression: (Rjt - It) = a + 8(\,, - It) where Rjt and Rmt refer to portfolio j and market anvestment relatives in period t. respectwely, and It refers to the risk free interest rate in period t. r?o is the standard error of (1.
other investment strategies. The ex post geometric mean is the compound average per period rate of return. Comparing geometric mean returns of .&e two wwtfolios, the msults are not consistent wkh the alternative hypothe& that industry analysis increases investment returns, since ‘he nonindustry strategy portfolio has the better geometric mean. Roth portfoliw, however, have higher geometric mean return3 than doea the S B P 500 portfolio.
Simulation Test of Industry Analysis Hypothesis
381
Apparently, part of the observed difference between the three uortfolio returns can be explained by differences in portfolio risk as measured by s’,andard deviation. Table 1 shows that the nonindustry strategy portfolio was slightly more risky than the industry portfolio and both were considerably more risky tban the S & P 509 or market portfolio. In a risk averse market, differer.ces in ex post rates of return are consistent with differences in ex post risk. Consequently, the next level of analysis compares risk adjusted measures of return to further test the null hypothesis of no difference of portfolio retuma Considerable precedent exists for looking at one-parameter performance measures (measures that embody both return and risk in a single parame“r) in evaluating portfolio performance. However, recent criticism by Friend and Blume [9] of the adequacy of the capital asset pricing model in relation to performance measurement implies that the results discussed here are only suggestive of the validity of the underlying research hypothesis. Two measures of risk aijusted performance were investigated; both are reported in Table 1. First, Sharpe’s [24] reward to variability ratio was determined for both portfolio strategies and the 5 & P 500. Again the results indicate that portfolio performance was not improved by industry analysis since the nonindustry strategy had a reward to variability ratio superior to the industry strategy. In comparison to the S & P portfolio, only the nonindustry strategy reward to variability ratio was higher. As an alternative test of risk adjusted performance, Jensen’s [ 131 measure of portfolio performance was determined for the portfolios. If portfolio performance for either the industry or nonindustry strategy was superior to a naive biry and hold strategy, the intercept, a, from the regression” I$,, - Ir =
a +
0 (L
- It)
would be positive, In the context of the industry analysis hypothesis, however, the comparison of interest is between a’s of the PNO portfolio selection strategies. Table 1 results indicate again the absence of evidence supporting the rejection of the null hypothesis in favor of the alternative. Both a’s are positive and the nonindustry a is algebraically larger; however, neither is significant at the .05 level. The conclusion once more is that industry analysis does not increase portfolio return, when returns are risk adjusted. Conclu8iona The intent of this study was to perform a simulation test of the notion that industry analysis is a necessary and important step in the security evaluation process. The industry analysis notion implies that stock selection strategies utilizing an appropriate intermediate industry
hLFW6d
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analysis step will be superior to stock selection strategies omitting this intermediate step. The test developed here is based on a simulation P/E ratio stock selection scheme used in several previous studies. As such, the test is incomplela since it only considers industry vs. nonindustry analysis within the context of P/E ratio selection strategies. A complete simulation test of the hypothesis is impossible since it would require an exhaustive enumeration of an infinite array of possible security selection strategies1 However, tbe test was designed to advantageously use the appapalent‘information content of previously reported “quarterly data” studic;s. Within the context of the test, industry analysis does not enhance portfolio performance either before or after risk considerations. These results support &en’s previous empirical findings and imply that, in ?e absence of relative cost savings that may be attributable to the industry analysis, industry analysis is an unnecessary intermediate evaluation step when quarterly P/E ratios are used to ordinally evaluate common stocks for portfolio selection. Footnotes lTbie research was st-ppcrtod in part by a grant from the IJniversity of Kansas GeneA Research Fund. Vbile I,ataJ and Tuttle found substantial difterenees in interindustry performance they found no evidence of persistence in these differences; i.e., performance benveen periods waa unrelated. They also noted that the differences had not narrowed over time despite the growth of wnglomerates. Wther s&dies analyzing only investment relative data are subject to the same criticism. See [I] for the counter-example. %ce Fama [?I for a thorough summary and a tricbotomiration of the efficient market hypothesis into weak, semistrong and strong forms. *See, for example, Latan6, Joy and Jones Cl61 and Litrenberger, JOY and Jones 1191. For a dtiferent aspect of the utility of quarterly earnings, see Brown and Kenuelly [4]. &For ‘xxzubcrating evidence of the spurious effect of “forward information” on P/E ratio’ stock selection. see I&an6 and Tuttle [173. eIt s&Id bs emphasized that no “forward information” is involved in the determinaf riun of tg+se. P/6 ratios. Only those industries and companies actually having preliminary or compEe;e quarterly earnings reports published by the third month after tbo close of the &zter were eligible for inclusion in either of the two portfolios described below. rA p%folio of 10 was aelected because recent empirical work has indicated and 16 securities are sufficient to reduce diversifiible risk to a p-c&al mfniium. Sea Evar~ and Archer [63. aThat is, the hypothetical investor selects stocks in accordance with the industry and strategies described above using PJE,,, as the selection criterion. The fnve-stment relative realiid from any individ& sscurity so selected is (Pt_,_l -t_ 4+:)/P, Where wure 10 up and five down quarterly market perfcds during the study where up and down refer to S L P 600 portfolio investment rd.&~ gmttm than and less than l.@lc. lWpectfve1. Waria~lea in the rqmesion m&l are defined in Table I.
between d&t
noniuhstry
that
Sirnw?ution Test
of Muatry
Ar&ysis
Hypothesk
3B3
References 1. Ball, R. and Brown, P. “An Empirical Evaluation of Accounting Income Numbers.” Joumaf of Accowrting Research (Autumn 1968) 159-178.
:
2. Breen, W, “LQW Price-Earnings J~amaI (July-August 1968)
Ratios and Industry
: 125-X27.
Re!atives.” FiaanciaI RncIysts
3. Brown, P. and Ball, R. “Some Preliminary Findings on the Associatiun between the Earnings of a Firm, Its Industry, and the Economy.” FmpiricaI Research in Accounting: Selected Studies. supplement to Journol of Accourating Research 5(1%7) : 55-77. 4. Brown, P. and Kennelly, J. “The Information Content of Qwrterly Earnings: An Extension and Some Further Evfdence.” .lournal of &iness (July 1972) 48341%
:
5. Elton, E. and Gruber, M. “Improved Forecas,iog through the Design of Homogeneous Groups.” learnal of Business (October 1971) 432450.
:
6. Evans, J. and Archer, S. “Diversification and the Reduction of Dispersion: Empirical Analysis.” Journal of Finance (December 1968) 761-767.
:
An
7. Fama, E. “Efficient Capital Markets: A Review of Theory and Emp!rical Work.” Journal of Finance (May 1970) 383417.
:
8. Farrar, D. The Investment Decision under Uncertuinry. Rnglewdr,l Prentice&i& Inc., 1962.
Cliffs,
9. Fritlid, I. and Blur;e, hf. “Measurement of Pertfolio Performam e o&r ‘Am,+tican Economic Review (September 1970): 561-575.
N_ J.:
Dncertz~lnty.”
10. Geua tits, J. ‘The Influence of Industry Factors in Stock Price M ‘vemen;;s.”WarZig Papep No. 44, Schwl of Business, University of Kansas, June lwl. 11. Graha,,. B.; Dodd, D.; and C&e, Hill Blak Co., Inc., 1962.
S. Security A&y&
4th ed. N. ir York: NcGww-
12. Kakanssc:n, N. “Capital Growth and the Mean-Variance Approach to Portfelic Selection.” Journal of Financial and Quantitative AnaIysis (January 1971) 517-557.
:
13. Jensen, M. “‘lbe Performance of Mutual Funds in the Period 1945-l%&” JoarnaI oj Finance (May 1968): 339416. 14. King, B. “Market and Jndnstry Factors in Stack Price Behavior.” lourncl (January 1’66) Part II, 139.190.
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15. Iatani, I-I. “lrritiria for Ckoice among Risky Ventures.” Journal of PoWcal Ecmuxay (April 1%9r 144155. 16. Lab&, R.; Joy, 111.; and Jones, C. “Quarterly Data, Sort Rank Routines and Security Evaluation.” Journal of Basiness (October 1970)
: 427438.
17. Latani, Ii. and Tllttle, D. “‘An Aualysis of Common Stock Price Ratios.” Sourhem Economic JoarnaI (January 1965): 343-354. 18. Latani, Ii. and Tuttle, D. “Industry Analysis Framework for Forming Probability Beliefa” Financial Analysts Joaraal (July-August 1968) 51-61.
:
19. Litaenberger. R; Joy, M.; and Jones, C. “Ordinal Predictions and the Selection of Common Stocks.” .iownd FinanciaI and Quantitative Analysis (September 1971) 1059-1068.
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20. Markowitz, Ii. “Portfolio Selectioc.” Jo&
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of Fiaance (Mar& 1952) : 77-91.
21. Mead, E. and Grodinsky, J. The Ebb and Flow of Invesrment VaIaes. New York: Appleton-c&utury-Crofts, Ino., 1939. 22. Meyers, S. “A Reexamination of Market and Industry havior.” Joumaf of Finance (June 1973) : 695-705.
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Journal of &k.ness Research
384
23. Reilly. F. “The Mficted Emphaaie in Security Evahtion.” Jwmal (Jamtery-F&ntary 1973): 5%60. 24. zG
Financial
W. “lUatual Fund Performance.” Joumal of Business (January l!k%)
Analysti
: Part
II.
23. Standard and Pwn’e. The Ana&% Handboo& montldy supplement (March, June, September and hember iesuea from September, 1965 t$roq& Mnrch, 1970).
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 1.2. 13. 14. 15. ir;: 18. 19. 20. 21. 22. 25. ::: 26. 27. 28. 29. 30. 31. 32. 33. 34.
Aerospace Air Transport Aluminu0 Autoswbiles Auto Accessories Autos-Tlucks 8 Parts Beverages-Brewers Beverages-Distillers Beverages-Soft Drinks Building Materials-Air Conditioning Buildina Materials-Cement Building Materials-Heating & Plumbing 6 Wallboard Building Materials-Roofing Chemicals Coal-Bituminous Culfectionery Consaincr&wa1 and Glass Containers-Paper copper Cosmetics Drugs Electrical Equipment Electrical-Electronic Major Cos. Electrical Household Appliances Electronic Companies Finance Small Loans Foods-Biscuit Baker Foods-6read E Cake Bakers Foods-Canned Foods-Corn Refiners Foods-Dairy Products Foods-Packaged Cold Mininrr l&se Furnishings Lead 6 Zinc
37. 38. 39. 40. 41. 42. 43. 44. 45. :;: 48. 49. SO. 51. 52. 53. 54. 55. 56. 57. 58. 59. ZY: 62. 63. 64. 65. 66. 67. 68. 69. 70. 71.
Machine Tools Machinery-Agricultural Machinerv-Construction & Material Handling Machinery-Industrial Machinery & Services-Oil Well Machinery-Specialty Machinery-Steam Generating Metal Fabricating Metals-Miscellaneous #otion Pictures Office E Business Equipment Oil-Crude Producers Oil-IntLSrated-Domestic Oil-Integrated-International paper Publishing Radio-N Broadcasters Radio-lV Manufacturers Railroad Equipment Retail Stores-Department Stores Retail Stores-Food Chains Retail Stores-Mail Order E General Shoes Scaps Steel Sugar Cane Refiners Sulohur Producers T&ile-Apparel Manufacturers Textile-Synthetic Fibers Textile P&ducts Tires 6 Rubber Goods Tobacco-Cigarette Manufacturers Tobacco-Cigar Manufacturers Vegetable Oils Vending Machines
the ha4zuatry
0. Maurice Joy, Universi8yof Kamas, Lawrence
Is industry analysis a nezssary step in a security evaluation process? Some finance studies support its usefulness; others do not. This article further investigates the question by. reporting a test which compares returns generated from an investment strategy using industry analysis with those from a strategy omitting industry analysis. The article begins with a review of the opposing views of the utility of industry analysis, then presents an alternative approach to investigating the problem and the results of a study using the approach. The ReZevance of Industry AnalyaM The notion that industries consist of companies homogeneous with respect to certain importaut attributes is derived from microeconomic theory. The theory asserts that for many problems, firms can be classified into industries, the groupings being such that attribute similarities within these groups are more important than dissimilarities. Gertainly this notion is true in at least some instances. But, conversely, classification into industries is irrelevant in instances where dissimilarities are more important than similarities. In security analysis there is no concensus on the role of industry security analysis. Some assert that industry analysis serves a useful purpose in the 0WraZZevaluation scheme while others maintain that industry analysis is unnecessary, Orthodox security evaluation asserts that industry analysis is a necessary part of the evaluation process and the investment literature is replete with allusions to this perceived importance. For example, in 1939 Mead and Grodinsky expressed an extreme and a dated view: “Beside the selection of the industry, other considerations of investment are unimportant” [2L p. 41. Volume2, Number4
octoL?r,1974
Joumd
of Bu&ess
Research
in common stocks that one should first select the most promising in. dustry or industries end then pick out the best companies in those industries” [ll, p. $71. Markowitz [20] also supports industry appraisal. Having established that an adequate number of securities is necessary for diversification, he qualified this finding by distinguishing between mere diversification and diversification for the “right” reason: The adequacyof diversificationis not thoughtby investorsto depend on the number of different securities held. A portfolio with sixty different railway same
securities.
likely
for example,
size portfolio
ous sorts
with
some
of manufacturing,
for firms
within
would
the same
than for firms in dissimilar
not be as well
railroad, etc. The
some reason
industry
industries
diversified
as the
public
utility,
mining,
vari-
is that
it is generally
more
to do poorly
at the same
time
[ZO, p. 891.
While “proper” diversification need not require formal industry analysis, Markowits’s last sentence clearly is consistent with the literature advocating industry analysis. Latan6 and Tuttle [18] investigated interindustry security evaluation within the broader context of formulating investment relative prob. ability beliefs for common stocks. Their approach is the fundamental evaluation scheme of sequential market analysis, industry analysis and individual company analysis. This framework, especially the order of the last two steps, embodies the industry analysis philosophy and reinforces Graham. Dodd and Cottle;. i.e., se&t the best inr’ustries and then select the best set&ties within those best industries. Commenting on the usefulness of industry analysis, Latane and Tuttle noted wide interindustry differences in performance over the period 1950 to 1%7, indicating that industry analysis was potentidly useful? These differences are a necessary, but not sufficient condition for industry analysis relevance. If there were no differences between industry returns, industry analysis clearly would be useless. More recently Reilly [23) reemphasized the role of industry analysis witbin the traditional security analysis framework. He advocated the necessity of industq analysis and strongly urged that the proper order of analysis is from market to industry to company. .&me recent empirical studies also lend support to the validity of industry analysis. King [14] found that for the stocks in the six
Simulation Test of Industry Analysis Hypothesis
dustry ix&x numbers (rather than first differences) and-( 2) tbe use of aggregate industry prices rather than individual stiurity prices. Tbe Gaumnitz study ignored the potential differential effect of information flows in noI*price series, such as earnings. By concentrating solely on price movements, the impact on price of new information is necessarily confounded with random movements in tbe residual price series.’ These strictures are the liturgy of empirical analysis; however, tbe basic issue is still unresolved. This study addresses tbe same problem, but from a different perspective.
A PIE Investment Strategy Approach Most of the empirical studies described above sbare the common feature of looking for discernible industry effects in equity investment relatives. An alternative way to investigate the industry analysis hypothesis is to devise a test whi& compares returns generated from competing investment strategies, one using industry analysis, the other ignoring industry analysis. Of course, such an approach requires specification of
376
an investment strategy and this specification squarely confronts the efficient market hypothesis in semistrong form, Very little empirical evidence has been presented that is contrary to the efficient market hypothesis in semistrong form.s/Such contrary evidence deals almost exclusively with quarterly earnings reports.’ These studies provide a convenient benchmark for testing the industry analysis hypothesis. In particular, since quarterly earnings appeared to be useful in ex ante security selection over a common time period covered in several of the studies (the late l%Os), it seems reasonable to use quarterly earnings models in formulating investment strategies over a similar time period in testing the industry analysis hypothesis. The two-strategy investment comparison used here is patterned after a simulation study performed by Breen [Z]. Breen suggested using P/E ratio investment strategies in teating the industry analysis hypothesis. Breen used company P/E ratios to identify undervalued securities in both industry and nonindustry stock selection strategies. The nonindustry strategy identified as undervalued stocks those companies with lowest positive P/E ratios. The industry strategy chose stocks whose (positive) P/E ratios were lowest relative to their industry P/E ratio. He found the nonindustry strategy performed slightly better, indicating that industry analysis is umrecessary, at least in the context of using P/E ratios as ex ante predictors of investment relatives. Unfortunately, several aspe&s of Breen’s study are unsstisfactory. First, his industry strategy appears inconsistent with the traditional industry analysis approach described above. Breen’s technique involves picking companies having low (positive) P/E ratios relative to their industry P/E ratio. A strategy more consistent with the Craham-DoddCottle and Reilly concept of appropriate industry analysis envisions a two-step process: first choose the best industries, then pick the stocks within those best industries. In the context of P/G ratio analysis this approach would imply first selecting the lowest positive P/E ratio industries, and then picking the lowest positive P/E ratio stocks within those industries. A second disturbing aspect of the Breen study is his use of “forward information” in his simulation. He used published annual reports to form P/E ratios and these P/E ratios were formed on January 1 of each year in his study. But this erroneously presumes that annual earnings figures are available to investors on January 1. Cousequently, Breen was implicitly using “forward information,” information not actually available to a hypothetical investor on January 1. This study avoids this deficiency.& A third aspect of Breen’s study warranting comment is his failure to adjust for risk, In a risk averse stock market observed differences between realiaed rates of return may only be differences attributable to
Simulution Test of hduatry Andyst
Hypothesis
risk premia. Been did not investigate the differential risk (if any) between his twe competing selection strategies, leaving unanswered the issue of risk adjusted rates of return comparison. This study corrects for this deficiency hy investigating both the Sharpe [24] and Jensen [ 131 risk adjustment performance measures.
Data for this study were taken from two sources. Industry data were taken from Standard and Poor’s The Analyst’s Handbook [25]. This monthly publication contains per share quarterly sales, taxes, earnings and dividends for industry group indexes adjusted for stock splits and dividends. Monthly high, low and close prices and selected financial ratios also are published. Quarterly earnings and dividends and monthly prices were collected from this source for the 71 selected industries listed in the Appendix. A total of 23 industries was excluded from the study for one of the following reasons: 1. The indus”q was a composite of several industries. ; In the industry, 2. Except for exclusion of the largest cornpan_ the industry was a duplicate of another-full industry set. 3. The industry did not report quarterly earnings. 4. The industry had no companies on the Standard and Poor’s Compustat tapes. The industry data collected covered the time period September, 1%5
through June, 1969. Company data were taken from a Standard and Poor’s Quarterly Compustat tape dated March, 1970. Quarterly earnings, dividends and monthly prices, a11 adjusted for stcck splits and stock dividends, were collected for 955 companies with the only exclusion criterion being a December fiscal year closing. Industrial classifications of the companies were cross cbcked against The Analyst’* Handbook classifications. Firms in none of the 71 Handbook industry classifications were retained in the study, hut only for testing in the nonindustry investment strategy. Simulation Procedure
The investment simulation procedure as used here is merely a method of repetitively choosing two alternative portfolios of stocks. These repetitive choices result in two time series of portfolio returns which can be compared. From this comparison inferences can he drawn concerning the industry analysis hypothesis. Ebtplanation of this twostrategy investment simulation is best done hy example. Assume that the date is June 30,1%7. A hypothetical investor has the June, 1967 Handbook Monthly Supplement and calculates P/E
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Research
ratios for all of the 71 designated industries in the Supplement that bave preliminary or complete first quarter I.967 earnings reports. These ratios are formed for each industry by dividing its first quarter, 1967 earnings into its June 34 1%7 price. Coincidental with these calculations, P/E ratios are calculated for all of the 955 companies with announced first quarter 1967 earnings. These ratios are formed by divi&ng each company’s first quarter, 1967 earnings into its June 30, 1967 price.” The competing stock selection schemes now are developed as follows: A. Nonindustry Analysis Approach All companies are rank ordered on the basis of the algebraic value of their P/E ratio (described above) and the 10 best positive P/E stocks are taught (in equal dollar amounts) at the closing June 30, 1967 price.’ This portfolio of stocks is held three months and then sold on September 30, 1967. The portfolio investment relative over this three-month period is the arithmetic mean of the 10 individual stock investment relatives. Each stock’s in. vestment relative is the sum of its September 30, 1967 price plus any dividends paid between July 1,1%7 and September 30,1%7 inclusive, divided by its June 30, 1%7 closing price. B. Industry Analysis Approach All industries are rank ordered on the basis of the algebraic value of their P/E ratios and the 10 lowest possitive P/E industries are identified. Then, within each of these 10 industries, companies are rank ordered on the basis of the algebraic value of their P/E ratios. Within each of these 10 separate intraindustry rank orderings the company with the best (lowest positive) P/E ratio is identified. The resultant 10 stocks are bought (in equal dollar amounts) at the closing June 30, 1%7 price and put into a portfolio. This portfolio is held three months and then sold on September 30, 1967. Tbe determination of the portfolio investment relative over this three-month period is identical to the nonindustry analysis approach described above. The two illustrative procedures used first quarter 1967 earnings as the starting point. Each of the 15 quarters or investment “decision periods” used in the study would have similar calculations associated with it. To genera?ize the relationship between P/E ratios and investment relatives, let E denote earnings, P denote price, ID denote dividends and let the subscript t refer to time (in quarters). Let the time reference be at the investor’s buy date. By construction then, the P/E ratio variable is defined as P@t_I. Note that the price and earnings have different subscripts; i.e., the price is a current price (June 30, 1967 in the example above-coinciding with the buy date), but the earnings variable is lagged one quarter (first quarter 1%7 in the example) to reflect the reported lag of earnings. The
Simulation Test of Industry Analysis Hypothesis
379
subsequent investment relative associated with the decision variable Pt/E,_, is (P,+l + Dt+l)/P,, where Dt+* refers to the quarterly dividends received during the quarter subsequent to the buy date (in the example, third quarter 1%7 dividends).’ Each calendar quarter used in the study constitutes one decision period. Fifteen such quarters enter into the s&dy. Repetition in each quarter of the rank order, buy and then sell simulation procedure generates a time series cf investment relatives for each portfolio. These investment relative series are analyzed in the next section. Before turning to a detailed analysis of the portfolio investment relatives, it is interesting to noie the extent to which the two alternative portfolio strategies selected the same sepuities. In four of the 15 periods no stocks were in both portfolios, in five periods one stock was in both, in another five periods two stocks were in both and in one period three stocks were in both portfolios. The relatively low incidence of dual selection indicates that the two strategies are somewhat independent. Results Investment relative time series for the two strategies are shown in Table 1 along with a comparable series of Standard and Poor’s 500 Investment relatives. This latter series provides perspective regarding the types of quarterly markets prevailing over the period from late 1965 to mid-1969.8 Summary risk and return measures also are shown in Table 1. The objective of the research is to determine whether or not portfolio performance is enhanced by industry ‘analysis. Consequently, in all instances of comparison the following hypotheses are tested: HO: II (Industry) = II (Nonindustry) Hl: II(1ndustr-y) > II(Nonindustry) where II is investment return (however defined), and the “Industry” and “Nonindustry” designators refer to industry and nonindustry portfolio strategies, respectively. Arithmetic mean returns, which do not reflect risk differences, provide no basis for the rejection of the null hypothesis of equal returns. Indeed, the nonindustry strategy portfolio has the higher arithmetic mean. The next level of analysis compares geometric mean returns Of the two competing strategies. The geometric mean return has been advocated by Latank [15] as a plausible operational goal of portfolio management on the grounds that, in the long run, maximization of the geometric mean of the probability distribution of portfolio avoids ruin (see Hakansson [U] ) and leads to larger terminal wealth than would
of &kncs~
Joumd
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Table 2: Portfolio Simulation Results
Rcmuch
’
Investment Relative
= Rt= (Pt+l + Dt+l)/Pt
----Decision Period
Industry Strategy Portfolio
Time Period
1 2 3 4 5 6
9/30/G - 12/31/65 12/31/65 - 3/31/66 3/31/66 - 6/30/66 6/30/66 - Y/30/66 9/30/66 - 12/31/66 ~2/31/66 - 3/31/67 :/31/6? - 6,‘30/67 e/30/67 - 3/30/67 I/30/67 - 3Z/31/67 K/31/67 - 3/31/68 In/31/68 - 6/30/68 (t/30/68 - 9,‘30/68 Y/30/68 - 12/31/68 1:!/31/68 - 3/31/69 .x$/31/69 - 6/30/69
8 9
10 11 12 13 14 15
Nonindustry Strategy Portfolio
1.198 1.108 .896
1.174 1.155 1.065 .860 1.035 1.365 1.133 1.131 1.045 .979 1.474 1 .OSl
.830
1.006 1.349 1.143 1.198 .996 .976 1.307 1.098 1.184
1.269
.953
.911
.905 .895
G
1.080 1.071
1.102 l.OYO
n
.149
RV o (oo) Notes
.455 .0099 (.022)
.I.72 .5:4
.029 (.032)
s E P 500 Portfolio 1.058 .998 ,981 .937 1.087 1.159 1.059 1.100 1.028 .96.5 1.137 1.051 1.041 1.007 .993 1.041 1.039 .063 .460 _-
:
Y
is
the arithmetic
mean:
v = (“t Rt)/15
G is the geometric mean:
G=
o is the standard deviation:
Cl=
RV is Sharpe’s
m-
[24] Reward to Variability
Ratio:
RV = (u - 1.012)/o
The three-month mean interest rate, 1.012, expressed as a three-month investment relative. was determined by averaging the relevant three-month rates over the time period in the study. o is Jensen’s [13] measure of risk adjusted performance. It is the intercept of the regression: (Rjt - It) = a + 8(\,, - It) where Rjt and Rmt refer to portfolio j and market anvestment relatives in period t. respectwely, and It refers to the risk free interest rate in period t. r?o is the standard error of (1.
other investment strategies. The ex post geometric mean is the compound average per period rate of return. Comparing geometric mean returns of .&e two wwtfolios, the msults are not consistent wkh the alternative hypothe& that industry analysis increases investment returns, since ‘he nonindustry strategy portfolio has the better geometric mean. Roth portfoliw, however, have higher geometric mean return3 than doea the S B P 500 portfolio.
Simulation Test of Industry Analysis Hypothesis
381
Apparently, part of the observed difference between the three uortfolio returns can be explained by differences in portfolio risk as measured by s’,andard deviation. Table 1 shows that the nonindustry strategy portfolio was slightly more risky than the industry portfolio and both were considerably more risky tban the S & P 509 or market portfolio. In a risk averse market, differer.ces in ex post rates of return are consistent with differences in ex post risk. Consequently, the next level of analysis compares risk adjusted measures of return to further test the null hypothesis of no difference of portfolio retuma Considerable precedent exists for looking at one-parameter performance measures (measures that embody both return and risk in a single parame“r) in evaluating portfolio performance. However, recent criticism by Friend and Blume [9] of the adequacy of the capital asset pricing model in relation to performance measurement implies that the results discussed here are only suggestive of the validity of the underlying research hypothesis. Two measures of risk aijusted performance were investigated; both are reported in Table 1. First, Sharpe’s [24] reward to variability ratio was determined for both portfolio strategies and the 5 & P 500. Again the results indicate that portfolio performance was not improved by industry analysis since the nonindustry strategy had a reward to variability ratio superior to the industry strategy. In comparison to the S & P portfolio, only the nonindustry strategy reward to variability ratio was higher. As an alternative test of risk adjusted performance, Jensen’s [ 131 measure of portfolio performance was determined for the portfolios. If portfolio performance for either the industry or nonindustry strategy was superior to a naive biry and hold strategy, the intercept, a, from the regression” I$,, - Ir =
a +
0 (L
- It)
would be positive, In the context of the industry analysis hypothesis, however, the comparison of interest is between a’s of the PNO portfolio selection strategies. Table 1 results indicate again the absence of evidence supporting the rejection of the null hypothesis in favor of the alternative. Both a’s are positive and the nonindustry a is algebraically larger; however, neither is significant at the .05 level. The conclusion once more is that industry analysis does not increase portfolio return, when returns are risk adjusted. Conclu8iona The intent of this study was to perform a simulation test of the notion that industry analysis is a necessary and important step in the security evaluation process. The industry analysis notion implies that stock selection strategies utilizing an appropriate intermediate industry
hLFW6d
of &&?WSSResearch
analysis step will be superior to stock selection strategies omitting this intermediate step. The test developed here is based on a simulation P/E ratio stock selection scheme used in several previous studies. As such, the test is incomplela since it only considers industry vs. nonindustry analysis within the context of P/E ratio selection strategies. A complete simulation test of the hypothesis is impossible since it would require an exhaustive enumeration of an infinite array of possible security selection strategies1 However, tbe test was designed to advantageously use the appapalent‘information content of previously reported “quarterly data” studic;s. Within the context of the test, industry analysis does not enhance portfolio performance either before or after risk considerations. These results support &en’s previous empirical findings and imply that, in ?e absence of relative cost savings that may be attributable to the industry analysis, industry analysis is an unnecessary intermediate evaluation step when quarterly P/E ratios are used to ordinally evaluate common stocks for portfolio selection. Footnotes lTbie research was st-ppcrtod in part by a grant from the IJniversity of Kansas GeneA Research Fund. Vbile I,ataJ and Tuttle found substantial difterenees in interindustry performance they found no evidence of persistence in these differences; i.e., performance benveen periods waa unrelated. They also noted that the differences had not narrowed over time despite the growth of wnglomerates. Wther s&dies analyzing only investment relative data are subject to the same criticism. See [I] for the counter-example. %ce Fama [?I for a thorough summary and a tricbotomiration of the efficient market hypothesis into weak, semistrong and strong forms. *See, for example, Latan6, Joy and Jones Cl61 and Litrenberger, JOY and Jones 1191. For a dtiferent aspect of the utility of quarterly earnings, see Brown and Kenuelly [4]. &For ‘xxzubcrating evidence of the spurious effect of “forward information” on P/E ratio’ stock selection. see I&an6 and Tuttle [173. eIt s&Id bs emphasized that no “forward information” is involved in the determinaf riun of tg+se. P/6 ratios. Only those industries and companies actually having preliminary or compEe;e quarterly earnings reports published by the third month after tbo close of the &zter were eligible for inclusion in either of the two portfolios described below. rA p%folio of 10 was aelected because recent empirical work has indicated and 16 securities are sufficient to reduce diversifiible risk to a p-c&al mfniium. Sea Evar~ and Archer [63. aThat is, the hypothetical investor selects stocks in accordance with the industry and strategies described above using PJE,,, as the selection criterion. The fnve-stment relative realiid from any individ& sscurity so selected is (Pt_,_l -t_ 4+:)/P, Where wure 10 up and five down quarterly market perfcds during the study where up and down refer to S L P 600 portfolio investment rd.&~ gmttm than and less than l.@lc. lWpectfve1. Waria~lea in the rqmesion m&l are defined in Table I.
between d&t
noniuhstry
that
Sirnw?ution Test
of Muatry
Ar&ysis
Hypothesk
3B3
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1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 1.2. 13. 14. 15. ir;: 18. 19. 20. 21. 22. 25. ::: 26. 27. 28. 29. 30. 31. 32. 33. 34.
Aerospace Air Transport Aluminu0 Autoswbiles Auto Accessories Autos-Tlucks 8 Parts Beverages-Brewers Beverages-Distillers Beverages-Soft Drinks Building Materials-Air Conditioning Buildina Materials-Cement Building Materials-Heating & Plumbing 6 Wallboard Building Materials-Roofing Chemicals Coal-Bituminous Culfectionery Consaincr&wa1 and Glass Containers-Paper copper Cosmetics Drugs Electrical Equipment Electrical-Electronic Major Cos. Electrical Household Appliances Electronic Companies Finance Small Loans Foods-Biscuit Baker Foods-6read E Cake Bakers Foods-Canned Foods-Corn Refiners Foods-Dairy Products Foods-Packaged Cold Mininrr l&se Furnishings Lead 6 Zinc
37. 38. 39. 40. 41. 42. 43. 44. 45. :;: 48. 49. SO. 51. 52. 53. 54. 55. 56. 57. 58. 59. ZY: 62. 63. 64. 65. 66. 67. 68. 69. 70. 71.
Machine Tools Machinery-Agricultural Machinerv-Construction & Material Handling Machinery-Industrial Machinery & Services-Oil Well Machinery-Specialty Machinery-Steam Generating Metal Fabricating Metals-Miscellaneous #otion Pictures Office E Business Equipment Oil-Crude Producers Oil-IntLSrated-Domestic Oil-Integrated-International paper Publishing Radio-N Broadcasters Radio-lV Manufacturers Railroad Equipment Retail Stores-Department Stores Retail Stores-Food Chains Retail Stores-Mail Order E General Shoes Scaps Steel Sugar Cane Refiners Sulohur Producers T&ile-Apparel Manufacturers Textile-Synthetic Fibers Textile P&ducts Tires 6 Rubber Goods Tobacco-Cigarette Manufacturers Tobacco-Cigar Manufacturers Vegetable Oils Vending Machines