an investigation of financial ratios in predicting firms future performance an application of pr's...

7/24/2019 An Investigation of Financial Ratios in Predicting Firms Future Performance an Application of Pr's Methodology

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AN INVESTIGATION OF FINANCIAL RATIOS IN PREDICTING FIRMSFUTURE PERFORMANCE: AN APPLICATION OF PRs METHODOLOGY

A Dissertation

Presented to

the Faculty of the College of Business Administration

University of Houston

In Partial Fulfillment

of the Requirements o f the Degree

Doctor of Philosophy

by

Lijyun Angela Hwang

April 18, 1997



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UMI Number: 9817079

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AN INVESTIGATION OF FINANCIAL RATIOS IN PREDICTING FIRMS

FUTURE PERFORMANCE: AN APPLICATION OF PRs METHODOLOGY

APPROVED:

C.S7 Ames Cheng, Associate Pr|, then the ratio is likely to be a profitability-oriented ratio.

Gh hit (15)

The Retums-Target Prediction Model:

bhXhn (16)

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If a a; 0 and \a\ * \b\,then the ratio is likely to be a risk-oriented ratio.

I f a=*=0 and * \b\, then the ratio is likely to be a ratio with a mixture of both

profitability and risk.

Based on these relationships, the scheme of classifying financial ratios can be illustrated as

follows:

CLASSIFICATION OF FINANCIAL RATIOS BY COMPARING LOGISTIC

COEFFICIENTS OF EARNtNGS-TARGET PREDICTION MODEL

AND RETURNS-TARGET PREDICTION MODEL

a sare the coefficient estimates form the eamings-target prediction model. 6 s are

the coefficient estimates form the retums-target prediction model. The difference of"a 5 and bs is evaluated by taking the mean o f paired differences o f a and 6during each evaluation period.

a ~0 a *0

a ~ b Not useful Profitability

a &\b Risk Mixed

Table 5 reports the yearly coefficients obtained every three years from univariate

logistic regressions of the respective models. For example, the coefficients for year 1980

are obtained by pooling firm-year observations o f 1977-1979. Panels A and B report the

yearly coefficients for the eamings-target prediction model and the retums-target

prediction model during 1980-1986. Based on these yearly logistic regressions, an across-



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-and-hold exc ess returns are the dependent var iables I'mthe earnmgs-target prediction miHlel and the retums-target prediction model respect ively Independent variables are

those identified by principal component analysis Coefticieu ts labeled for year 1980 are obtained by pooling firm-year observations from 1977-1979.

Panel A: Earnings-Targct Prediction Model

Net profit margin %A in Net profitmargin

Debt-equityratio

%A in Debt-equity ratio

Inventory /totalassets

%A in Inventory/total assets

Quick ratio %A in Quickratio

1980 1.399 -0.030 -0.113 0.390 -1.586* -1.010b 0.403* 0.448"

1981 -2.703 -0.091 -0.062 0.855* -1.195b -1.572* 0.2 94 1 0.081

1982 -4.043* -0 .1571 -0.092 0.67 lb -0.264 -1.294* 0.130 -0.117

1983 -8.102* -0.250* -0.075 0.293 -0.401 -1.880* 0.107 0.185

1984 -5.095* -0.172* -0.169 0.082 -1.302b -1.191* 0,173 0.293

1985 -4.539* -0.141* -0.062 0.037 -1.182 -1.472* -0.013 0.332

1986 -4.048* -0.094 0.041 0.478* -1.578* -1.000b -0.061 -0.143

Meana -3.876k - A / i/ -0.076 0.401" -1.073* -1.345* 0.148 0.154

Mean Sii c rror 1.609 0.063 0.082 0.241 0.566 0.411 0.117 0.246

Wald Chi-square* 5.799 4.481 0.858 2.770 3.597 10.720 1.604 0.393

Panel B: Returns-" 'arget Prediction IVodel

1980 4.1 78 1, 0.007 -0.012 -0.151 -1.225b -1.152* -0.026 0.218

1981 1.776 0.046 -0.022 -0.080 -0.128 -0.618 0.134 -0.058

1982 -0.815 0.085 -0.1941 -0.103 0.873 -0.121 0.380* -0.246

1983 4.372* -0.046 -0.081 -0.216 0.107 -0.210 0.004 -0.053

1984 -0.199 -0.072 -0.045 -0.355 -0.457 -0.422 -0.049 0.435"

1985 1.885 -0.034 -0.024 -0.368 -1.688* -0.732 -0.203 0.450

1986 7.187* 0.125* -0.090 -0.112 -1.895* -0.062 -0.086 0.300

Mean 2.626" 0.016 -0.067 -0.198 -0.630 -0.474 0.022 0.149

Mean Std Error 1.583 0.060 0.082 0.255 0.562 0.401 0.115 0.243

Wald Chi-square 2.754 0.070 0.672 0.598 1.260 1.400 0.037 0.378

Panel C: Evaluation of Ratio Attribute

Mean dilTerencer 1.360 0.074 0.021 0.203 0.162 0.871 0.043 -0.023

Prob>|T| 0.302 0.039 0.516 0.098 0.539 0.006 0.619 0.707

a not equal to 0s ** ** * * *+*

Attribute Profitability Mix Not useful Mix Profitability Mix Not useful Nol useful


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TAB1.H5 (CONTINUED)UNIVARIATE ANALYSIS OF LlXJISTIC COIiM-lCU-lN IS AND FINANCIAL ATTRIBUTES FOR RATIOS IDENTIFIED BY PRINCIPAL COMPONENT ANALYSIS

Panel A: Earnings-Targct Prediction Model

Sales/total assets %A in sales /

total assets

%A in LT debt %A in total

assets

Cash dividend as

% of cash flows

Sales to fixed

assets

Sales to

inventory

Sales to

working capital

1980 -0.610* 1.1261 -0.145" -1.692* 1.956* -0.04Sb 0.008 -0.0131981 -0.250* 0.474 -0.140" -1.256* 1.453* -0.013 0.015 0.001

1982 0.173 0.891" -0.111 -0.936* 0.800" 0.051* 0.031* 0.013"

1983 0.196 0.173 -0.111 -2.450* 0.798b 0.036* 0.020* 0.006

1984 -0.069 1.119b -0.184* -2.181* 0.551 -0.006 0.017 -0.003

1985 -0.090 -0.065 -0.288* -1.943* 0.697* -0.055* 0.000 -0.014b

1986 -0.163 -0.161 -0.206b -0.829" 1.151* -0.046b 0.009 -0.017*

Mean -0.116 0.508 -o. i6r -1.612 i . o s r -0.011 0.014 -0.004

Mean Std Error 0.132 0.522 0.092 0.439 0.398 0.019 0.012 0.007

Chi-square 0.774 0.948 3.395 13.481 7.061 0.340 1.436 0.259

Panel B: Rctums-Target Prediction Model

1980 -0.542c 1.332b -0.072 0.248 -1.604* -0.025 0.001 -0.006

1981 -0.048 1.076b -0.074 -0.494 0.125 0.048' 0.010 -0.006

1982 0.388* 0.783 -0.041 -0.739* 0.218 0.082* 0.014 -0.013"

1983 0.469* 0.448 -0.119 -1.693' 0.724b&00o 0.035' 0.012"

1984 0.410* 0.366 -0.077 -1.343* 0.147 0.016 0.038' 0.012*

1985 0.245 0.276 -0.305* -1.795* 0.545 -0.031 0.043' 0.015b1986 0.200 0.427 -0.154 -0.581 0.686* -0.035 0.03 lb 0.004

Mean 0.160 0.673 -0.120 -0 .W / 0.120 0.015 0.025* 0.003

Mean Std Error 0.141 0.517 0.091 0.428 0.415 0.019 0.012 0.008

Wald Chi-square 1.300 1.691 1.730 4.564 0.084 0.573 4.133 0.118

Panel C: Evaluation of the Ratio Attr )UtCS

Mean difference -0.107 -0.100 0.049 0.628 0.480 -0,005 -0.010 0.000

Prob>|T| 0.195 0.560 0.028 0.012 0.022 0.627 0.236 0.971

a not equal to b * *** *+*

Attribute Not useful Not useful Mix Mix Mix Not useful Not useful Not useful


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T A U 1 ,1 - 5 ( C O N T I N I J I ' D )

UNIVARIATH ANALYSIS Ql; LOGISTIC COITTICTI-NTS AND FINANCIAL ATTRIBlITHS FOR RATIOS IDENTIFIED BY PRINCIPAL COMPONENT ANAI.YS1S

'' *c Denotes statistical significance at the 0.10.0 .05 . and 0.01 levels respectively, based on a two-tail t-test.

' Similar to Lev and Thiagarajan j 1993|, the yearly logistic regression is used to obtain an across-year mean, standard error to calculate Wald chi-square foreach financial ratio.

cThe statistical meanings of Wald chi-square is similar to that of t-value in regression analysis. It indicates the statistical significance of a coefficient.

r Mean difference is the mean of the differences between the absolute value of the coefficient for each variable obtained from the eamings-target prediction

model and the corresponding coefficient from the retums-target prediction model during the same time periods. As a result, 7 paired observations for each

financial ratio are included in the mean tests. As both of the parametric test (paired t lest) and the nonparamctric test (paired Wilcoxon singed rank test)

produce similar results, only the paired t test is reported as Prob>|T|.

KThis row summarizes across-year (mean) coefficients that are statistically significant in the earnings model as reported in Panel A. *. **, and *** denote the

significance level at 0.10. 0.05. and 0.01 respectively.


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year mean, standard error are used to calculate Wald chi-square for each financial ratio to

evaluate their statistical significance. This approach attempts to control for upward bias

due to cross-sectional correlation in a pooled analysis (Bernard [1987], Lev and

Thiagarajan [1993], and Cheng, Liu and Schaefer [1996]).

Panel C of Table 5 reports the classification o f the financial attributes of the 16

financial ratios based on the above framework. Net profit margin and inventory/total

assets are classified as profitability-oriented ratios. Percentage change (%A) in net profit

margin, %A in debt-equity ratio, %A in inventory/total assets, %A in long-term debt, %A

in total assets, and cash dividend as a percent o f cash flows are classified as ratios with a

mixture o f profitability and risk. Debt-equity ratio, quick ratio, %A in quick ratio,

sales/total assets, %A in sales/total assets, sales to fixed assets, sales to inventory and sales

to working capital are classified as non-useful ratios. None o f the ratios are classified as

pure risk-oriented ratios.

6.2.3 Prs Constructs

6.2 .3.1 An Initial Evaluation Based on Multivariate Logistic Regressions

The empirical models used to evaluate the constructs of Pr in the two multivariate

logistic regressions are expressed as follows:

The Eamings-Target Prediction model:

AEiti v ' / 1 - = 2 aioch,, ( 15 )

" h=\

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5 3

The Retums-Target Prediction model:

m

R~i = ^bkXh,t (16)>1=1

where,xh(/ = 1 ,2 m) is the set o f mfinancial ratios for firm iat timeI,and ahand bh

are the vectors o f the estimated coefficients for the financial ratios in the respective

models. The definition o f the dependent variables are the same in both the multivariate

models and the univariate models. Table 6 reports the yearly coefficients obtained every

three years from multivariate logistic regressions of the respective models. The estimation

of coefficients are similar to that of the univariate regressions except that only one

financial ratio is included in the univariate models but all 16 financial rations are included

in the multivariate models.

A coefficient estimated in a logistic model indicates the effect of a unit change in

an independent variable on the probability o f the occurrence o f the dependent variable.

Given a significant statistical test, a positive sign o f a parameter estimate suggests that the

probability o f one-year-ahead earnings changes (excess returns) increases with the value of

the financial ratio. A comparison o f the coefficients in the univariate analysis in Table 5

and multivariate analysis in Table 6 reveals consistent signs between the two analyses.

This implies the collinearity is not a problem for the multivariate analysis. However, the

significance level o f the coefficients is stronger in the univariate analysis than in the

multivariate analysis. This is expected because univariate analysis only accounts for the

effect of an individual independent variable. Ratios that are statist ically significant in the

eamings-target prediction model include: %A in debt-equity ratio, %A in inventory/total

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TABLE 6

MULTIVARIATE ANALYSIS OF THE LOGISTIC COEFFICIENTS FOR RATIOS IDENTIFIED BY PRINCIPAL COMPONENT ANALYSIS

Coefficients of logistic regressions arc estimated for every three .years. Signs of onc-ycar-ahcad earnings change without drift and signs of 12-month market-

adjusted buy-and-hold excess returns are the dependent variables for the eamings-target and the returns-target prediction model respectively. Independent

variables are those identified by principal component analysis. That is. coefficients labeled for year 1980 arc obtained by pooling firm-year observations from

1977-1979. The coefficients are then fitted to the accounting data of year 1980 to obtain the eamings-target Pr (E-Pr) and retums-target Pr (R-Pr).

Panel A: Eamings-Target Prediction ModelNet profit

margin

%A in Net

profit margin

Debt-equity

ratio

%A in Debt-

equity ratio

Inventory

/total assets

%A in Inventory /

total assets

Quick ratio %A in Quick

ratio

1980 3.143 0.019 0.049 1.859" -1.752b -0.607 0.255 0.642

1981 0.304 -0.016 0.036 2.164' -1.169 -1.366' 0.343 0.119

1982 0.475 -0.1411 -0.133 1.851" 0.205 -1.373" 0.114 -0.066

1983 -2.862 -0.1611, -0.161 1 .2 0 2 " -0.597 -1.560" 0.086 -0.054

1984 -4.653" -0.1511, -0.2921 1.075" -2.164" -0.999" 0.147 0.223

1985 -4.658b -0.086 -0.223b 1.162" -1.143 -1.390' -0 . 1 0 2 0.386

1986 -5.830" -0.073 -0.107 1.408" -2.216b -1.050* -0.135 0.146

Mean** -2 . 0 1 2 -0.087 -0.119 1.531' -1.262 - l . l 9 f 0 . 1 0 1 0.199

Mean Std Error 2.362 0.068 0.107 0.423 1.074 0.489 0.157 0.323

Wald Chi-Square 0.725 1.661 1.230 13.104 1.382 5.954 0.415 0.380

Mean Std. Estimate* -0.046 -0.050 -0.045 0.187 -0.070 -0,094 0.025 0.026

Panel B: Retums-Target Prediction Model

1980 5.829* -0.052 -0.039 0.530 -2.555* -1.528" -0 . 2 2 1 -0.172

1981 5.332b 0.006 0.077 0.492 -0.841 -0.978b -0.029 -0.3281982 -0.045 0.088 -0.096 0.248 0.861 -0.424 0.271 -0.640

1983 -1.675 -0 . 0 1 0 -0.098 0.298 1.300 0.071 0.016 -0.075

1984 3.007 -0.077 -0.009 0.297 1.287 0 . 0 0 2 -O.I18 0.476

1985 5.727" -0.038 -0.049 0.855" 0 . 0 2 0 -0.109 -0.356b 0.723

1986 9.345" 0.074 -0 . 0 1 1 0.85 lh -1.476 0.374 -0.3901 0 .6 6 8 "

Mean 3.931a -0 . 0 0 1 -0.032 0.510 -0 . 2 0 1 -0.370 -0.118 0.093

Mean Std Error 2.271 0.065 0,103 0.394 1.057 0.473 0.152 0.313

Wald Chi-Square 2.997 0 . 0 0 0 0.097 1.679 0.036 0.614 0.604 0.089

Mean Std. Estimate 0.081 -0 . 0 0 0 -0 . 0 1 2 0.065 -0 . 0 1 2 -0.027 -0.037 0.015


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TABLE 6 (CONTINUED)

MULTIVARIATE ANALYSIS OF THE LOGISTIC COEFFICIENTS FOR RATIOS IDENTIFIED BY PRINCIPAL COMPONENT ANALYSIS

Panel A. Earnings-Targct Prediction Model

Sales /

total assets

%A in sales /

total assets

%A in LT debt %A in total

assets

Cash dividend as

% of cash flows

Sales to fixed

assets

Sales to

inventory

Sales to working

capital

1980 -0 . 1 0 1 1.124" -0.2741 -2.296 1.292b -0,006 -0.024 -0.0071981 -0.074 1.057" -0.322 -1.759 0.737 0.009 -0 . 0 1 1 0.005

1982 -0.059 1,553c -0.2481 - 1. 1 0 2 " 0.555 0.06 lb 0.029 0 . 0 1 1

1983 -0.037 0.755 -0.025 -2.432 0.502 0.051" 0.014 0.005

1984 -0.047 l.793c -0.132 -1.703 0.358 0 . 0 2 2 -0 . 0 0 0 -0.007

1985 0.007 0.594 -0.400c -1.243h 0.292 -0.037 0.005 -0.028

1986 -0.008 0.446 -0.578c 0.086 1.190b -0.008 0.013 -0.044

Mean -0.046 1.046" -0.28? -1.493* 0,704 0.013 0.004 -0.009

Mean Std Error 0.047 0.615 0.128 0.652 0.456 0.028 0.019 0 . 0 1 0

Wald Chi-Square 1 . 2 0 0 2.894 4.906 5.245 2.382 0.217 0.038 0.819

Mean Std. Estimate -0.048 0.063 -0 . 1 0 1 -0 . 1 1 1 0.056 0 . 0 2 2 0 . 0 1 1 -0.044

Panel B: Retums-Target Prediction Model

1980 -0.068 1.693 -0.114 -0.262 -2.125b 0.036 -0.036* -0.016

1981 -0.023 1.071" -0.057 -0.891 -0 . 1 1 0 0.080 0.005 -0.009

1982 0.130 0.642 0.043 -0.680 0.209 0.064b 0.037* -0 . 0 1 0

1983 0.009 -0.184 -0.004 -1.546 0.715* 0.040 0.053 0.008

1984 0.031 -0.078 0.032 -1.833 -0.030 0.017 0.054 0.0041985 0.041 -0.056 -0.261 -2.289 -0.017 0.003 0,044b -0.004

1986 0.066 0.151 -0.247 -1.176 -0.045 0.017 0.014 -0 . 0 1 1

Mean 0 . 0 1 0 0.463 -0.087 -1.23? -0 . 2 0 0 0.037 0.024 -0.006

Mean Std Error 0 . 0 2 1 0.596 0.119 0.624 0.433 0.027 0 . 0 2 0 0 . 0 1 0

Wald Chi-Square 0.226 0.603 0.533 3.950 0.215 1.804 1.557 0.326

Mean Std. Estimate 0.026 0.027 -0.030 -0.094 -0 . 0 1 2 0.061 0.068 -0 . 0 2 0

' ' Denotes statistical significance at the 0.10, 0. 05, and 0 01 levels respectively, based on two-tail t-tesl

1Simil ar to Lev and 1'hiagarajan 11993 1, the yearly logis tic regress ion is us ed to obtain an across-year mean, standard error to calculate Wa ld Chi-Square for each

financial ratio

Standardized coeificients are reported here in order to compute the relative influence of independent variables in the models


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assets, %A in sales/total assets, %A in long-term debt, %A in total assets (see Panel A in

Table 6). In the retums-target prediction model, only two financial ratios are statistically

significant: net profit margin and %A in total assets (see Panel B in Table 6).

The signs of these ratios in both models are consistent except for the net profit

margin, which is operationalized as the ratio of earnings before extraordinary items to

sales. In the earnings model, the sign o f the net profit margin is negative which indicates

that an increase in the current earnings level will decrease the probability of an increase in

the future earnings change. This observation is consistent with the earnings reversal

behavior documented in previous studies (Freeman, Ohlson, and Penman [1982], Ou

[1990], Elgers and Pfeiffer [1996]). In the returns model, however, the net profit margin's

sign is positive which suggests that higher current earnings lead to higher probability of

future excess returns.

The other significant ratio in the retums-target prediction model is %A in total

assets, and it has a negative sign. These observations o f a positive coefficient for net

profit margin and a negative coeffcient for %A in total assets suggest that the probability

of future excess returns for a firm will increase if operating profits can grow without tying

up more investment in assets. These empirical results confirms the relationship between

returns and accounting information specified in the finance free cash flow concept. Firm

value equals the present value of discounted future free cash flow, and free cash flow is a



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positive function of net operating margin (profitability) and a negative function of growth

in operating assets as suggested in Ohlson and Feltham [1995] (p. 695).25

As reported in Panel A of Table 6, financial ratios that are significant in the

eamings-target prediction model are (%A in debt-equity ratio, %A in inventory/total

assets, %A in sales/total assets, %Ain long-term debt, %A in total assets. Based on Table

5, they are all classified as ratios with a mixture of profitability and risk except for %A in

sales/total assets, which is categorized as a non-useful ratio in the univariate logistic

analysis. Although these ratios are classified as ratios with a mixture of profitability and

risk, they are more profitability oriented because they are statistically significant in the

eamings-target prediction model but not significant in the retums-target prediction model.

6.2.3.2 A Further Look at Prs Constructs by Exam ining Trading

Profitabilities of the Earnings-Target Prediction Model and the Returns-Target

Prediction Model

The interpretation o f Prs constructs cannot be complete without looking into the

trading profitability for the two models. In HL, financial ratios that are significantly

associated with future earnings increases (excess returns) are used to form eamings-target

Pr (E-Pr) and retums-target P r (R-Pr), respectively. They found that the trading

' ' The free cash flow concept states that:Free cash flow,

= Operating earnings, + Operating assets,./ - Operating assets,

= Operating earnings, - (Operating assets, - Operating assets,./)

= Operating earnings, - (A in Operating assets,)

= [(Operating earnings, / Operating assets,./) - (A in Operating assets, / Operating assets,./)) * Operating assets,./= (Operating margin, - growth in operating assets,) * Operating assets,./



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profitability based on R-Pr dominates eamings-target Pr. In this study, principal

component analysis is used to obtain a set o f financial ratios that only depends on the

interrelationship among a large number o f financial ratios without considering their

association with the dependent variables to form Pr.

Trading profitability is measured by monthly rebalanced market-adjusted returns

gained from a hedge portfolio over various holding periods as in Greig [1992], Hedge

portfolios for the eamings-target prediction model and retums-target prediction model are

formed based on two schemes. The first scheme is to form a hedge portfolio that take a

long (short) position if Pr is greater (less) than 0.6 (0.4). The second scheme involves

trading strategy by dichotomizing the whole sample. That is, trading strategy is based on

taking a long position for Prs that is greater than 0.5 and taking a short position for Pr

less than and equal to 0.5. While Table 7 provides the small prints for the by-year

trading profitability to the long position, short position and hedge portfolio for various

holding periods of 3, 6, 9, 12, 18, 24, 36, 48, 60 and 72 months. Table 8 highlights the

mean returns for the average of the by-year returns reported in Table 7. To get a better

picture of the trading profitability trends, the trading profitability over various holding

periods are plo tted side-by-side. Finally, the trading profitability patterns for the various

trading strategies, on a common scale, are comprehensively presented in Figure 3.



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I Mill- 7

H Y - Y K A R T R A D I N G I 'R O I I I A H I I I T Y T O M il- : 11 I. IK .I -, P O R T F O L I O S O V I -R H O L D I N G P F R I O D S O T .3 . 6 , 9 . 1 2 . 1 8 . 2 4 , 3 6 . 4 8 . 6 0 A N D 7 2 M O N T H S

T h e m o n t h l y r e b a l a n c e d m a r k e t a d j u s t e d r e t u r n s t o t h e h e d g e p n r t o l i o s a r c c a l c u l a t e d b y t a k i n g l o n g p o s i t i o n f o r f i rm s w i t h P r > 0 .6 ( P r - 0 . 5 ) a n d s h o r t f o r f ir m s w i t h P r- 0 4

( P r - - 0 . 5 ) f o r t h e c a i n i n g s - t a r g c t m o d e l a n d t h e r c t o r n s - t a r g c t m o d e l . P o r t f o l io s a r e fo r m e d t h r e e m o n t h s a f t e r t h e D e c e m b e r y e a r e n d f o r w h i c h t h e a c c o u n t i n g i n f o r m a t i o n is

n s s m u c d t o b e p u b l i c l y a v a i l a b l e . T h e r e t u r n s a r e c a lc u l a t e d u s i n g e q u a t i o n s ( 1 0 ) . ( 1 1 ) . a n d ( 1 2 ) i n t h e t e x t . M e a n r e t u r n s a r e t h e ' a v e r a g e o f t h e b y - y e a r m e a n s .

lannel A: Kamings-'Parget Model with Pr 0 6 for long posilon (n -1017) and lr 0.4 (n 472) lor short position

No. of Observations Holding Period 3 Holding Period 6 Holding Period-9 Holding Period-12 Holding Periods-18

Year Ixmg Short t-ong Short 1ledge 1AMlg Short 1ledge long Short Hedge Ixtng Shon Hedge 1aitig Short Hedge

80 341 17 0.0457 -0.0086 0.0543 0.0146 -0.0876 0 1022 0.0207 -0.0744 0.0952 0.0371 -0.1527 0.1898 0 0218 -0.2638 0 285681 208 33 0 0100 -0.0229 0.0328 -0 0048 0.0071 -0.0119 0.0370 0.1177 -0.0807 0.0989 0.1783 -0.0794 0 1606 0.1739 -0.013382 56 127 0.0916 0.0389 0.0526 0.0808 0.0388 0.0420 0.0807 0.0551 0.0257 0 1063 0.0383 0.0680 00910 0.0124 0078683 50 107 0.0210 0 0121 0.0089 0.0094 -0.0089 -0.0006 -0.0797 -0.0332 -0.0465 -0.0597 -0.0143 0.0454 -0 0823 -0.0174 -0 064884 94 76 -0.0563 -0.0647 0.0083 -0.0281 -0 0532 0.0251 -0.0488 -0.0865 0.0377 -0.0215 -0.0766 0.0551 -0.0470 -0.1261 0.079083 161 42 -0.0113 0.0072 -0 0 184 -0.0123 -0.0419 0.0295 0.0132 -0.0229 0.0097 0.0283 0.0075 0 0208 0.0993 0.0246 0.123986 107 70 00199 -0.0308 0.0507 0.0534 0.0230 0.0765 0.0241 -0.0419 0.0659 0.1002 0.0717 0.0284 0 1008 0.1010 -0 0002

Mean Returns 0 0172 -0.0098 0.0270 0.0134 -0.0241 0.0375 00030 -0. 0123 0. 0153 0.0414 0.0075 0.0339 00492 -0.0207 00698

Pannel H: Famings-Targct Model with Pr 0.5 forlong posilon (n -1814) and P r 0.5 (n 1100) for short position

80 444 39 0.0479 0.0545 -0.0066 0.0047 -0.0294 0.0341 0.0076 -0.0015 0.0091 0.0275 0.0413 0.0689 0.0195 -0.0954 0.114881 362 105 -0.0002 -0.0215 0.0213 -0.0140 -0.0033 0.0108 0.0293 0.0595 -0.0302 0.0927 0.1490 -0.0563 0.1605 0.1754 -0.015082 130 298 0 0616 0.0416 0.0199 0.0630 0.0507 0.0123 0. 0784 0.0680 0.0104 0 1054 0.060S 0.0448 0.0904 0.0330 0.057483 170 247 0.0237 0.0037 0.0200 0 0081 -0.0115 0.0196 -0.0248 -0.0196 -0.0052 -0.0075 -0.0040 -0.0035 -0.0202 -00153 -0.0049

84 226 168 -0.0281 -0.0404 0.0122 0.0003 -0.0282 0.0286 -0.0065 -0.0429 0.0363 0.0214 0.0277 0.0491 0.0124 0.0676 0.080083 271 101 -0.0105 0.0220 -0.0325 0.0246 -0.0093 -0.0153 -0.0237 0.0121 -0.0116 0.0145 0.0230 0.0084 0.0534 0.0207 0.032786 211 142 0.0086 -0.0155 0 0241 00502 0.0037 0.0466 0.0259 0.0022 0.0238 0 1048 0.0924 0.0124 0.1102 0.0893 0.0209

Mean Returns 0.0147 00064 0.0084 0.0125 -0 .0039 0.0164 0 0123 0 007 7 0.0047 0.0513 0.0360 0.0153 0.0609 0.0200 0.0409

Pannel C: Retums-Targc t Model with 1Pr 0.6 for long positon (n 386) and Pr 0.4 (n; 487) forslion position

80 58 114 0.0522 0.0467 0.0055 -0.0177 0.0152 -0.0329 -0.0208 -0.0007 -0.0201 0.0220 0.0293 -0.0513 00830 0.0196 0.102581 21 74 0.0053 -0.0269 0.0322 0.0028 -0.0524 0.0551 0.1149 -0.0279 0.1428 0.2581 0.0658 0.1922 0.2382 0.1277 0.110582 100 41 0. 0967 0.0553 0.0414 0.0767 0.0631 0.0136 00673 0.1082 -0.0409 0.0568 0.1911 0.1343 0.0571 0.0560 0.001283 65 38 0.0355 -0.0020 0.0374 0.0139 -0.0049 0.0188 -0 0259 -0.0479 0. 0220 0.0073 0.0415 0.0342 0 0133 -0.0535 0066884 47 48 -00222 -0.0641 0.0418 -0.0169 -0.1061 0.0892 -0.0359 -0 1364 0.100S -0 0151 0.1278 0.1127 -0.0335 0.1806 0 147183 55 72 0.0190 -0.0154 0.0344 0.0207 -0.0643 0.0850 0.0204 -0.0787 0.0991 0.0763 -0.0563 0.1326 0.0848 -0 0784 0.163286 40 100 0.0100 0.0234 0.0334 0. 0383 0.0020 0.0363 0.0272 -0.0520 0.0792 0.0643 0.0381 00262 0.0772 0.0706 00066

Mean Returns 0 028) -0.0043 00323 0 0168 -0 0211 0.0379 0 0210 -00336 0.0546 0.0587 0.0141 0.0446 0 0506 -0.0055 0 0561

Pannel 1): Eamings-Target Model with Pr 0.5 for long positon (n 1381 ) and Pr 0 5 (n 15.73) lor short position

80 219 264 0.0483 0.0487 -0.0004 -0.0112 0 0131 -0 0243 -0 0089 0.0205 -0.0294 -0 0051 0.0452 -0 0502 0.0287 0.0435 -0 072281 154 313 0.0044 -0.0097 0.0141 0.0000 -0.0172 0.0172 0.0710 0.0194 0.0516 0.1398 0.0888 00510 0.1880 0.1524 (10356

82 281 147 00539 0.0357 0.0183 0 0515 00598 -0 0083 00585 0.0958 -0.0374 0 0541 0.1130 -0.0589 0 0498 0.0501 0.000383 233 184 0 0252 -0.0045 0.0297 0 0058 -0.0151 00208 -0 0141 -0 0310 0.0169 00047 -0.0180 0 02 26 -0.0032 0.0347 0 031584 177 217 -00227 -0.0419 0.0192 0 0031 -0.0239 0 027 0 -0 0030 -0.0374 0.0343 0 0213 -0.0164 0.0376 0.0135 0.0504 0.06.3983 175 197 0,0014 -0.0046 0 0061 0 0014 -0.0397 0 0411 0 0103 0.0476 0.0579 00563 0.0177 0.0740 0 1031 0 0060 0 109186 142 211 0 0116 -0 0098 00214 00372 00271 0.0101 0 0485 -0.0054 0.0539 0 0907 0.1054 00147 0 0X44 0 1128 -01)281

Mean Returns (10174 0 0020 0 0155 00125 0 0006 0 0120 0 0232 0 002) 0 0211 0 0517 0.0429 0 0088 0 0581 0 0382 0 0199


80/96

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IAHI I S

TRADING PROITIAI51I 11 Y I( ) ITU-: Ill- IKII l0 6 (Pr>() 5) and short for firms with

Pr0.6 for long position (n= 1017) and Pr0.5 ft

Months Long Short Hedge

.3 0.0147 0.0064 0.0084

6 0.0125 -0.0039 0.0164

9 0.0123 0.0077 0.0047

12 0.0513 0.0360 0.0153

15 0.0593 0.0374 0.0219

18 0.0609 0 . 0 2 0 0 0.0409

24 0.0929 0.0457 0.0472

.36 0.0950 0.0297 0.0653

48 0.0864 -0.0152 0.1016

54 0.0598 -0.0.388 00986

60 0.0817 -0.0317 0 11.34

72 0.0834 -00365 0.1198

0 2500

0.2000

0 1500

01000

0.0500

0.0000

-0 0500

-01000

-0 1500

-0.2000

L I

11AMlg I Short Hedge

0.1200

0 1000

0.0800

0.0600

0.0400

0.0200

00000

00 200

-II 0-10011ong Slum Iledge


82/96

TABLE X(CONTINUED)

TRADING PROFITABILITY TO THE HEDGE PORTFOLIOS OVER HOLDING PERIODS OF 3.6 . 9. 12, 15, IX. 24. 36. 4X. 54.60 AND 72 MONTHS

Panel C: Rcturns-Targcl Model with Pr> ( ) . 6 for long position (iv=386) and Pr0.5 for long position (n -1381) and Pring I Short I ledge


83/96

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84/96

In contrast to HL [1992], this study finds that the trading profitability of Retums-Target

Pr (hereafter R-Pr) only dominates that o f Eamings-Target Pr (hereafter E-Pr) up to a 9-

months holding period (see Panels A & B in Table 8 and Figure 3). After that, the

trading profitability based on E-Pr continues to grow monotonically. On the other hand,

the trading profitability based on R-Pr remains constant and eventually declines (see

Panels C & D in Table 8 and Figure 3). Specifically, the excess returns observed from E-

Pr are monotonic increasing with the holding period (from 2% to 24%) and persistent

beyond 72 months. This suggests that E-Pr might only capture firms profitability

prospects and fails to incorporate risk factors. As a result, the trading profitability

generated by E-Pr is probably due to some omitted risk factors.

The R-Pr based trading profitability curve demonstrates a reverse V curve with a

peak at the holding period o f 9 months (5.6%),26 which signifies that the market slowly

adjusts to the fundamental value. In fact, the excess returns for R-Pr are not gre ate r than

zero when one considers the transactions cost, which ranges between 4.3% and 9.5%

(Holthausen and Larcker [1992]). The implication of the trading profitability observed

from R-Pr suggests that the market is efficient with respect to the use of accounting

information.

6Although the highest trading profitability of 9.3% is shown at 36-month, it is not significantly higher tlian tlwtof 9-month at the p-value of 0.21 (one-tail test).



85/96

6.2.3 .3 Benchmarking With Trading Profitability Patterns of Two Famou

Risk Proxies: Earnings-to-price Ratio and Book-to-Market Ratio

The conjecture that E-Pr proxies for some omitted risk factor is further evaluated

by comparing it with two benchmarks, namely trading strategies based on eamings-to-

price ratio (E/P) and book to market ratio (B/M) respectively. These two ratios are well

documented in studies o f market anomalies as proxies for omitted risk factors (Lev

[1978], Fama and French [1992], Bernard and Wahlen [1995] and others). E/P is the

earnings per share excluding extraordinary items (COMPUSTAT annual data item #58)

scaled by the price as o f December fiscal year-end. B/M is the book value of common

equity (COMPUSTAT annual data item #60) scaled by the market value of common

equity (product o f COMPUSTAT annual data items #25 and #199) as o f December fiscal

year-end. The monthly rebalanced market adjusted abnormal returns for each trading

strategy are equally-weighted pooled cross-sectional and time-series observation during

1980-1986. That is, once all observations have been assigned to a portfolio, the mean for

the portfolio is based on an equally-weighted average o f all observations in that

portfolio.27 Subsequently, cumulative abnormal returns for the long and short position are

calculated for various holding periods. As the E-Pr based trading strategy involves taking

long positions fo r 1017 firm-year observations with E-Pr>0 .6 and taking short positions

472 firm-year observations with E-Pr


86/96

72

(B/M) based portfolios is determined by taking long positions for 1017 firm-year

observation with highest E/P (B/M) and taking short positions for 472 firm-year

observation with lowest E/P (B/M).

Imposing the same frequency of stocks in long and short position as in the

eamings-target prediction model, both E/P and B/M models demonstrate similar but

stronger trading profitability patterns than does E-P r (see Panel D in Table 9). Moreover,

stocks with long position have significantly higher E/P and B/M than those with short

position for the eamings-target model but not for the retums-target model. The result is

consistent with Ou and Penman [1989b] in which they find similar properties between E-

Pr and E/P and conclude that ...information in prices that leads (future) earnings is

contained in financial statements [p. I l l ] ,

6.2.3,4 Evaluation o f Firm Size Effects

In light of continuous trading profitability based on the Pr strategy, Greig [1992]

examines the firm size effect on the cross-sectional relation between Pr and subsequent

stock returns. He finds that the positive associations between Pr and subsequent stock

returns disappear after allowing for cross-sectional differences in CAPM beta and

implementing tighter controls of firm size than those in OP [1989a], The hypothesized

effect of E-Pr on trading profitability should be positive. When Greig regresses the E-Pr

and firm size, defined as the log of the market value o f equity, on the mean adjusted buy-

and-hold return for the twelve-months (Table 6, p. 432 in Greig [1992]), the significance



87/96

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i

TABLE 9. COMPARISON OP TRENDS OF T R A D I N G PROFITABILITY TO THE HEDGE PORTFOLIOS BASED ON

EARNINGS-TA RGET Pr. EARNINGS-TO-PRICE RATIO (E/P) RATIO, AND BOOK-TO-MARKET (B/M) RATIO

The monthly rebalance d market adjusted returns associated with the trading strategies based on carnings-bascd Pr (E-Pr). carnings-pricc ratio, and

bo ok -to -m ark ct ra ti o ar c eq ua lly -w eig ht ed PO OL ED cr os s-s ec tio na l an d lim c- sc ric s ob se rva tio ns ov er the tim e pe rio d 1980 -198 6. Th e tr ad in g s tra teg y

by g oi ng lo ng for fir ms wi th E -P r >0 .6 an d sho rt for fir ms wit h E-P r- 0 4 re su lts in 1017 fir m s an d 472 fir ms res pec tiv ely . Th e tr ad in g str at egy for the

E/P (B/M) based portfolios is determine d by taking long for 1017 firm-vcar observations with highest E/P (B/M) and taking short for 472 firm-year_______________________________________________________ observat ions with lowest E /P (B/M ).

N u m b e r

P a n e l A .

M o n t h s

o f o b s e r v a t i o n s i s 1 0 1 7 f o r l o n g p o s i t i o n a n d 4 7 2 f o r s h o r t p o s i t io n .

T r a d i n g P r o f i ta b i l it y B a s e d o n P a m i n g s - t o - P r ic e R a t io ( P . /P )

L o n g S h o r t 1 l e d g e

.16

9

121 5

1 8

2 4

3 6

4 8

5 4

6 0

7 2

P a n e l B :

M o n t h s

0 0 1 9 5

0 . 0 1 5 2

0 . 0 2 7 9

0 . 0 7 8 9

0 . 0 9 0 5

0.10000 . 1 5 5 8

0 . 1 9 7 2

0 . 1 9 1 2

0 . 1 7 3 7

0 . 1 8 8 7

0 . 2 0 5 1

0 . 0 0 9 1

- 0 . 0 0 3 8

- 0 . 0 3 2 3

0 0 0 4 4

-0.0017- 0 . 0 1 2 8

- 0 . 0 0 6 8

- 0 . 0 3 5 4

- 0 . 0 4 9 3

- 0 0 7 4 5

- 0 . 0 4 0 3

- 0 . 0 5 3 0

0 . 0 1 0 4

0 0 1 9 0

0 . 0 6 0 2

0 . 0 7 4 5

0 . 0 9 2 3

0 . 1 1 2 9

0 . 1 6 2 6

0 2 3 2 6

0 . 2 4 0 5

0 . 2 4 8 2

0 . 2 2 9 0

0 . 2 5 8 1

0 . 3 0 0 0

0 . 2 5 0 0

02000

0 1 5 0 0

01000

0 . 0 5 0 0

0.0000

- 0 0 5 0 0

-0 1000 tUing Short -Hedge

T r a d i n g P r o f it a b i li ty B a s e d o n B o o k - l o - M a r k e l R a t i o ( B / M )

L o n g S h o r t I l e d g e

3

6

9

1215

1 8

2 4

3 6

4 8

5 4

6(17 2

0 . 0 2 1 6

0 . 0 1 7 4

0 . 0 2 6 5

0 . 0 8 5 5

0 . 0 9 5 1

0 . 0 9 3 1

0 . 1 5 4 3

0 . 1 8 3 5

0 . 1 7 7 4

0 . 1 5 5 4

( I 1 7 8 4

0 I 8 I < >

0 . 0 1 4 0

- 0 . 0 0 9 3

- 0.0120

- 0 . 0 0 4 7

- 0 . 0 0 1 8

- 0 . 0 2 6 9

- 0 . 0 2 6 1

- 0 . 0 5 1 3

- 0 . 0 7 9 2

- 0 . 1 0 8 0

- 0 0 9 2 8

- 0 1 0 4 2

0 . 0 0 7 7

0 . 0 2 6 7

0 . 0 3 8 5

0 . 0 9 0 2

0 . 0 9 6 9

0 . 120(1

0 . 1 8 0 4

0 . 2 3 4 8

0 2 5 6 7

0 2 6 3 4

0 . 2 7 1 1

0 2 8 5 7

0 3 0 0 0

0 . 2 5 0 0

02000

0 1 5 0 0

0.1000

0 0 5 0 0

00000

- 0 0 5 0 0

-01000

- 0 1 5 0 0

I

I "HI! I I S h o rt -Hedge


88/96

f

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7

TABLE 9 (CONTINUED): COM PARISON OF TRENDS OF TRADING PROFITABILITY TO THE HEDGE PORTFOLIOS BASED ON

Panel C Trading Profitability for Earnings-!ai'gel PTf!7

Months Long Short 1ledge0 2500

3 0.0181 -0 0 0 1 0 0 0214

6 0.0091 -0 009(i 0 0189 0 2 0 0 0

9 0.0116 -00092 0 0207 0150012 0.0491 0 01.18 0 0151

15 0.0612 0 0 ) 0 1 005110 . 1 0 0 0

18 0.0629 -0.0058 0.0687

24 0.1181 - 0 0 0 1 2 0.1193 0.0500

.16 0.1440 -0.0172 0 16120 0 0 0 0

48 0.1.114 -00511 0.182654 0.1007 -00715 0.1722 -0 050060 0 . 1 2 1 0 - 0 0621 0 1810

72 0.1.190 -00784 0 2174 I Ling I ledge

Panel D: Comparison of Trading Profitability to the Hedge Portfolios Based on Earnings-Targcl Pr (E-Pr), Earnings-to-Pricc Ratio (E/P), and Book-to-

Markcl Ratio (B/M)

0 3000 nB/M

E/ P0 2500-

E-Pr0 2000

0 1500-

01000-

0 0500

000006 1 5 24 363 9 12 1 8 5 44 8 6 0 7 2


89/96

75

of E-Pr disappears and becomes negative. The control variable, firm size, is negative and

statistically significant. Moreover, when he forms a trading strategy merely based on size

quintiles, the results show that the size strategy outperfo rms OPs Pr measure in predicting

abnormal returns by going long in the smallest firms and short in the largest firms.

This study complements Greigs study [1992] by further examining whether E-Pr

or R-Pr proxies for size effect after controlling for the E/P and B/M effects. Table 10

reports the results o f regressing the market-adjusted buy-and-hold returns for the holding

periods o f 12, 24 and 36 month subsequent to the computa tion o f Pr. Similar to Greig, E-

Pr is not significant in explaining market-adjusted buy-and-hold returns in the presence of

firm size (measured as log o f market value) with or w ithout controlling for E/P, B/M (see

Panel A in Table 10). However, R-Pr remains significant despite the inclusion of firm size.

E/P and/or B/M (see Panel B in Table 10). One noticeable observation is that the

significance of firm size in both models disappears as the holding periods prolong. On the

other hand, the significance o f E/P and B/M remains robust.

Reproduced with permission of the copyright owner. Further reproduction prohibited withou t permission.


90/96

76

TABLE 10

COMPARISON OF CONDITIONAL COEFFICIENTS (T STATISTICS) OF EARNINGS-TARGET

PR (E-PR) AND RETURNS-TARGET PR (R-PR)

The sample comprises 2.914 December year-end industr ial firms from 1980-1986. AR,t is the market-

adjusted buy-and-hold return for the holding periods of 12 (24,36) months subsequent to thecomputation of E-P rlt (R-Prlt) for firm / at time t. E-P rIt (R-Pr,t) is the estimated probability o f an

earnings (returns) increase in year r. Size,t is the log of market value of equity as of December fiscal

year-end. E/Plt is earnings per share excluding extraordinary items (Compustat annual data item #58)

scaled by the price (Compustat annual data item #199) as o f December fiscal year-end. B/M,t is the

book value o f common equity' (Compustat annual data item #60) scaled by the market value o f common

equity' (product of Compustat annual data items #25 and #199) as of December fiscal vear-end.

Panel A: AR;t = Yo + Yi E-Pr;, + Yi E/P;, + 7 3 B/M ;t + Y4 Size;,

Ym Yi Y2 Y3 Y-v Adj-R2

Holding Period = 12 months

0.024

(0.692)

0.042

(0.857)-0.092

(-2.567)b0.0019

-0 . 0 2 2

(-0.611)

0.151

(0.889)

0.165

(3.08)c

0.027

(2.524)b

-0.008

(-2.089)b0.0073


0.058

( 1 .0 2 2 )

0.046

(0.565)

-0.017

(-2.842)'

0.0023

-0.046

(-0.758)

0.058

(0.696)

0.398

(4.497)c

0.06

(3.406)'

-0.014

(-2.322)b

0.014


0.054

( 0 622)

-0 . 0 2 1

(-0.169)-0.009

(-0.981)

-0.0004

-0.099

(-1.074)

-0.017

(-0.131)

0.53

(3.808)c

0.091

(3.402)'

-0.004

(-0.423)

0.0092

Panel B: ARIf = y0+ y xR-Pr;t + 7 2 E/P;, + 7 3 B/M;, + 7 4 Size;,

Y" Yi Y: Y3 Y4 Adj-R2


-0.024

(-0.65)

0.152

(2.299)b

-0 . 0 1

(-2.784)'

0.0035

-0.072

(-1 743)-

0.151

(2.269)b

0.148

(2.793 )c

0.03

(2.882)'

-0.008

(-2.181)b

0.0088


-0.013

(-2.215)

0.208

(1.889)

-0.018

(-3.014)'

0.0034

-0.113

(-1 642)

0 . 2

(1.820)

0.376

(4.293 )c

0.064

(3.71)'

-0.015

(-2.393 )b

0.015


- 0 . 1 1 1

(-1.174)

0.331

(1.979)b

-0 . 0 1

(-1.15)

0 . 0 0 1

-0.263

(-2.498)b

0.324

(1.935)

0.511

(3.71 l)c

0.095

(3.576)'

-0.005

(-0.535)

0.0106

l c denotes statistical significance levels at 0.10. 0.05 . and 0.01 respectively.



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Chapter 7

CONCLUSIONS

Ou and Penman [1989a] form a trading strategy based on the predicted probability

of future earnings increase estimated by a set of financial ratios that can predict one-year-

ahead earnings change. In contrast , Holthausen and Larcker [1992] extract information

from financial ratios that can directly predict one-year-ahead excess returns to estimate the

probability o f future excess returns, which is then used to form a trading strategy. Both

trading strategies are able to generate substantial abnormal returns. However, the

persistence of abnormal returns resulted from using these P r methods has puzzled

researchers concerning the constructs o f Pr.

Based on a typical firm valuation model, returns can be expressed as a function of

the change o f earnings magnitude and a discount factor that reflects risk. If the change of

earnings magnitude captures mainly profitability, then, the construct of eamings-target Pr

(hereafter E-Pr) should be mainly related to profitability and retums-target Pr (hereafter

R-Pr) should be affected by both profitability and risk. Hence, comparing the relationships

between a common set o f financial ratios that compose Pr and the two prediction model

allows one to characterize the attributes o f the financial ratios and to evaluate the relative

importance of profitability and risk in predicting future unexpected earnings and future

excess returns.

77



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78

This study uses principal component analysis to identify a set of essentially

uncorrelated financial ratios that a ttempts to capture distinct aspects o f firm

characteristics. Because the set of financial ratios only depends on the interrelationship

among a large number o f financial ratios to form Pr without considering the association of

the ratios and the pred iction targets (i.e. the sign o f one-year ahead earnings change /

excess return), they can be applied in different methodologies. Using principal component

analysis, this study identifies a set of 16 essentially uncorrelated financial ratios from 59

financial ratios that were originally investigated by OP and HL. Based on a simple firm

valuation model, these ratios are subsequently classified into one of three categories:

profitability-oriented ratios, risk-oriented ratios and ratios with a mixture of risk and

profitability.

Because the targets that the financial ratios aim to predict are different for the

eamings-target prediction model and the retum s-target prediction model, investigating the

patterns of abnormal returns or trading profitability generated by eamings-target Pr (E-

Pr) and retums-target Pr (R-Pr) can provide insights regarding their respective economic

meanings. As expected, this study finds that the construct o f E-Pr different from that o f

R-Pr. Similar to eamings-to-price and book-to-market ratios, E-Pr proxies for some

omitted risk factors. Yet, R-P r supports the premise that operating profitability and

growth in operating assets are the two most important pieces of accounting information to

evaluate firm value and tha t the market is efficient in using accounting information. This

study complements Greigs study [1992] by further examines whether Pr is merely a



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79

manifestation o f the firm size effect. E-Pr is not significant in explaining market-adjusted

adjusted buy-and-hold returns in the presence of firm size (measured as log of market

value), eamings-to-price ratio, and book-to-market ratio. However, R-Pr remains

significant despite the inclusion o f firm size, eamings-to-price ratio, and book-to-market

ratio.

The understanding o f Pr s constru cts provides an explanation for the economic

meanings o f Pr and helps us to explain the market anomaly as reported in previous studies

Furthermore, it provides evidence as to the relative importance o f profitability or risk in

predicting future unexpected earnings and future excess returns.

Several limitations in this studyare: First, this study attempts to capture all relevant

aspects of firm characteristics from a relatively complete set of financial ratios, but there is

no guarantee that the set of ratios used in this study is complete . Principal component

analysis is a variable-sensitive statistical method; different financial patterns may be

generated as different financial ratios are fitted in the model. Second, the criteria for

selecting a financial ratio as a surrogate for each identified factor is somewhat subjective

The appropriateness of the ratios selected for proxying the factors and the potential

correlation among these ratios are also limitations to this study. Finally, this study is

limited to manufacturing firms with December 31 fiscal year-end. Therefore, the results

may not be generalized to other types o f firms and firms without a December 31 fiscal

year-end.

eproduced with permission of the copyright owner. Further reproduction prohibited withou t permission.


94/96

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