On the financial characteristics of firms that experienced the highest levels of stock price stability in a period of economic recession

On the financial characteristics of firms that experienced the highest levels of stock price stability in a period of economic recession

Bruce C. Payne

ABSTRACT

Stock price instability or volatility is a measure of risk in holding common stocks, and indeed, it is the basic element in the calculation of Sharpe’s beta. Value Line publishes weekly, a ranking of stock price stability for every firm in their database. The higher the Value Line ranking for price stability, the more likely they would recommend it as a safe investment. Prior work on this subject has considered to some degree the underlying macroeconomic environment, but there have been no studies that measured the financial characteristics of those firms that achieved the highest Value Line ratings for price stability in a period of economic recession.

Our analysis tests for significant differences in the financial profiles of those firms that achieved the highest Value Line ratings for price stability in a period of economic recession and companies selected at random during the same period, and from the same industries. A unique financial profile is established for the highest rated group, and it is suggested that the profile may be used to predict firms that may achieve high levels of stock price stability in the event of future economic downturns.

INTRODUCTION

The price level stability of common stocks is considered an important factor in the safety of equity investments. Indeed, the lack of stability, or a high measure of volatility is the basic element in Sharpe’s beta, a familiar tool used to measure the systematic (relevant) risk in a portfolio. Safety of investment has been of interest to investors, investment counselors, financial managers, and academicians for many years, and in periods of economic recession it becomes of paramount importance.

The period from March 2001 to November 2001 was identified by the National Bureau of Economic Research’s Business Cycle Dating Committee, as a period of economic recession in the United States (Associated Press, 2003). However, the downturn in the economy had started much earlier. On Jan. 14, 2000, the Dow-Jones Industrial Average closed at 11,722.98. That was an all-time high, but over the next two years, the Dow lost 38.7% of its value as it fell to a low of 7,286 on October 9th 2002 (Dow-Jones, 2006). The summer of the year 2000 was characterized by a great deal of political activity. It was a presidential election year and the race was apparently going to be very close. Thus, too little notice was given to such announcements as J. C. Penny declaring their intentions to close many of their stores, including stores in their Eckerd’s chain, that were just marginally profitable. The action of Penny’s and other firms was to result in many layoffs in the retail industry (Payne & Daghestani, 2000). During the next two years savings and retirement funds were significantly diminished, and unemployment increased to 6.3 percent in June 2003 when 9,338,000 persons were unemployed, (U.S. Department of Labor, February 2004). The decline in business activity, and rising unemployment were not only the result of the recession. The holocaust that has become known as nine-eleven, and the disclosures of corporate scandals also contributed to the downturn. (Payne & Wong, 2004). Thus, the period from March to October 2001 provides a “workshop” to study the financial characteristics of firms during an economic recession.

It would be a gross understatement to say that in the period of roughly two years, described above, when the Dow-Jones Industrial average lost 38.7 percent of its value, all parties whether they were investors, sellers, financial counselors, or financial managers considered the measure of the price stability of common stocks to be of a measure of primary importance. However, the subject of stock price stability has been the topic of very little research. Most previous studies have collected data from time periods of relative market stability or periods of growth such as the decade of the 1990’s. For example see: (Gittens, 2006; Shen & Shen, 2006; Business Week, July 3, 2006). Payne and Daghestani (1998) studied the financial determinants of safety during a period of great optimism, expectations of future growth, and a general feeling among investors that the market could only go up. They concluded on the basis of their analysis, that the firms characterized by safety of investment during this period had in their financial profile a greater level of capital spending, a greater return to total capital, less financial leverage, and surprisingly, less growth potential than a randomly selected group of similar firms. Three of those explanatory variables are also present in this study, and therefore may be compared in the conclusions presented in this analysis. There have been no studies that have considered financial variables that are consistent with stock price stability during economic downturns and recessions.

The purpose of this study is to establish a financial profile of firms identified by Value Line as having the highest levels of stock price stability in their database during the abovementioned period of economic recession, and to determine whether these firms have financial profiles that are significantly different from firms selected at random. If such a profile is established, and it can be validated without bias, it is suggested that it may be used to predict firms that will maintain the highest levels of stock price stability in periods of future economic downturns. This would have implications for financial managers, investors, investment counselors, and indeed, the entire market, and business community.

METHODOLOGY

The issues to be resolved are first, classification or prediction, and then evaluation of the accuracy of that classification. More specifically, can firms be assigned, on the basis of selected variables, to one of two groups: (1) firms chosen by Value Line for their number one rating for stock price stability (VLSPS),1 or (2) firms chosen randomly, but from the same industries as the first group (RCF)? Multiple discriminant analysis (MDA) provides a procedure for assigning firms to predetermined groupings based on variables or attributes whose values may depend on the group to which the firm actually belongs.

If the purpose of the study were simply to establish a financial profile of each group of firms, simple ratios would be adequate. In an early seminal paper on the use of MDA in finance, Altman (1968) showed that sets of variables used in multivariate analysis were better descriptors of the firms, and had more predictive power than individual variables used in univariate tests.

The use of MDA in the social sciences for the purpose of classification is well known. MDA is appropriate when the dependent variables are categorically measured and the predictive variables are measured metrically. In addition to its use in the Altman study to predict corporate bankruptcy, other early studies used MDA to, predict financially distressed property-liability insurance firms (Trieschmann & Pinches, 1973), growth (Payne, 1993), and the failure of small businesses (Edmister, 1982). This study also employs categorically measured dependent variables and metrically measured predictive variables. The two categorically measured dependent variables are the group of VLSPS firms and the group of RCF firms. The computer program used to perform the analysis is SPSS 11.5.0 Discriminant Analysis (SPSS Inc., 2002).

Since the objective of the analysis is to determine the discriminating capabilities of the entire set of variables without regard to the impact of individual variables, all variables were entered into the model simultaneously. This method is appropriate since the purpose of the study is not to identify the predictive power of any one variable, but instead the predictive power of the entire set of independent variables (Hair et al., 1998, p. 208-209).

SELECTION OF SAMPLE AND INDEPENDENT VARIABLES

All data used in the analysis were gathered from Value Line Ratings and Reports. Value Line ranks every firm in its database on a scale of 100 to 5 for stock price stability, where stocks with a ranking of 100 have the greatest stability and stock ranked 5 have the least stability. The sample selected for this study consists of two groups of 100 firms. The first group was identified by Value Line as having the highest ratings in our sample. The second group is a group of 100 firms randomly selected from the Value Line database, but from the same industries as the first group.

In periods of decline and recession all industries will not experience the same adverse effects. It follows that for an unbiased study the effects of industry must be held constant. In a seminal article on the assessment of risk, Hinich and Roll (1975) wrote that industries (or portfolios) will average out by cross section events peculiar to individual companies (or securities). In addition, Blume, (1971) wrote that even a large magnitude of individual variance may make the model inadequate for valuing an individual firm, but that it is adequate in cross section analysis.

The cross sectioning elimination of industry bias in this study was accomplished by matching the companies in the VLSPS group with companies from the same industry in the RCF group. For example from the drug industry, Pfizer is in the VLSPS group, and Biovail is in the RCF group. From the oilfield services industry, Transocean Incorporated is in the VLSPS group and Rowarn Companies is in the RCF group. From the medical supplies industry, Boston Scientific is in the VLSPS group and Priority Health is in the RCF group. O’Charlie’s is in the VLSPS group from the restaurant industry, and P.F. Chang’s is in the RCF group. In this manner each company identified by Value Line as having high ratings for stock price stability was matched with a randomly chosen company, from the same industry. Thus, the matching method of randomly choosing, and matching companies from the same industries eliminates any bias due to differences in industry listings. Previous studies using this and other statistical methods have chosen explanatory variables by various methods and logical arguments. In this study the group of explanatory variables chosen for analysis includes a measure of return to all investors, a measure of the market’s perception of the firms potential, two measures of risk, a measure of investment safety, a measure of investment timeliness, and finally a measure of earnings predictability. An evaluation of these measures is needed to accomplish the purpose of this study.

The measure of return is return to total capital. Return to total capital includes a return to creditors as well as owners, and recognizes that value is affected by the cost of debt. A measure of return to equity could be used, but it would ignore the cost of debt and the fact that debt as well as equity finance the assets owned by the firm.

The ratio of market price to earnings (P/E) has been used for years as a rough measure of how the market values a firm. Indeed, the P/E multiple, and dividend yield are the only ratios reported every day on the financial pages of newspapers, and it has been argued that in efficient markets the multiple reflects the intrinsic value of the stocks, (Scripto, 1998; Payne & Tyler, 2002). More recently, the price earnings growth ratio (PEG) has grown in popularity. The price earnings growth multiple adjusts the P/E ratio for potential growth, and it is suggested that the price earnings multiple (P/E) used without the adjustment for growth has a high potential for undervaluing a company. Damodaran, (2002) writes that the PEG ratio is a better measure of a company’s potential future value, and was developed to address the shortcomings of the P/E multiple. He further writes that many analysts have abandoned the P/E ratio, not because of any perceived shortcomings, but simply because they desire more information about a stock’s potential. Thus, the use of the PEG ratio is used here as a measure of a company’s potential long term value to investors.

Sharpe’s beta coefficients contain the effects of both operating and financial leverage. It is felt that the VLSPS firms may have less of both types of risk than the RCF. This may not be the case however, and separate measures of financial and operating risk will identify any differences. The separation is accomplished by “unlevering” published betas using the well known Hamada (1972) equation designed for that purpose. The unlevered beta is a measure of operating risk, and the debt to total capital ratio as the measure of financial risk, or financial leverage. (Van Horne, 2001, p. 207). Hamada’s equation requires a tax rate for each firm in the sample. These rates are published in Value Line Ratings and Reports for every firm in their database.

The ratio of long-term debt to total capital is used as the measure of financial risk. There are other ratios that measure financial risk very well, but the long-term debt to total capital ratio recognizes that the firm is financed by creditors as well as owners.

When entering into a period of economic recession and declining market values investors may value the safety of investment factor more than it would be valued in more normal capital markets. Even in periods of slow economic recovery such as the years 2001 to 2004, where investors, have just emerged from a period of economic recession, safety of investment may be of primary concern. Value Line ranks all firms in their database on the basis of safety of investment from one to five where number one is the safest ranking and number five is the worst. Accordingly, the Value Line ranking system is used here for the purpose of measuring safety of investment.

The measure of the timeliness of investing in a particular firm is Value Line’s proprietary timeliness rating. It is their ranking of a stock’s probable relative market performance in the year ahead. It is derived by Value Line via a computer program using as input the long-term price and earnings history, recent price and earnings momentum, and earnings surprise. All data are known and actual. Stocks ranked 1 (Highest) and 2 (Above Average) are likely to outpace the year-ahead market. Those ranked 4 (Below Average) and 5 (Lowest) are expected to under perform most stocks over the next 12 months. Stocks ranked 3 (Average) may advance or decline with the market in the year ahead. For four decades, Value Line’s Timeliness Ranking System has accurately anticipated stocks’ subsequent relative price performance (http://www.valueline.com/whyusehow). It is expected that stocks identified as high levels of price stability would also have a high timeliness ranking.

Whereas the value of any firm may ultimately depend on its ability to generate earnings and positive cash flows, the predictability of those earnings would seem to be a primary concern to potential investors, particularly in a period or economic recession. Thus, a measure is needed for earnings predictability. The Value Line measure of earnings predictability is a measure of the reliability of an earnings forecast. According to Value Line, predictability is based upon the stability of year-to-year comparisons, with recent years being weighted more heavily than earlier years. The most reliable forecasts are those with the highest rating (100); the least reliable, are those with the lowest rating (5). Value Line’s earnings predictability is derived from the standard deviation of percentage changes in quarterly earnings over an eight-year period. Special adjustments are made for comparisons around zero and from plus to minus (Value Line, 2006).

A basic tenet of this study is that investors at the margin evaluate the degree of risk in an investment and compare it to the investment’s potential rate of return. In modern finance textbooks this is a fundamental principle referred to as the “risk-return tradeoff.” (Van Horne, 2001) Investors at the margin “trade off” proxies for risk and return in buying and selling securities to establish demand and thus, price or market value. Stock price stability is simply one side of that tradeoff, but when investors become more risk averse, as in the period under study, they have to realize a greater potential return to assume marginal risks.

In sum, there are seven explanatory variables in the multiple discriminant model.

They are as follows:

X1–The Return on Total Capital

X2–The Price Earnings Growth Ratio

X3–Long Term Debt to Total Capital

X4–Operating Leverage-Unlevered Beta

X5–The Value Line Safety Rating

X6–The Value Line Timeliness Rating

X7–Earnings Predictability

The explanatory variable profile contains basic measures of common financial variables. They were chosen, as in any experimental design, because of their consistency with theory, adequacy in measurement, the extent to which they have been used in previous studies and their availability from a reputable source.

TESTS AND RESULTS

The discriminant function used has the form:

Zj = V1X1j+V2X2j+…..+VnXnj Formula (1)

where:

Xij is the firm’s value for the ith independent variable.

Vi is the discriminant coefficient for the firm’s jith variable.

Zj is the jth individual’s discriminant score.

The function derived from the data in this study and substituted in equation 1 is:

Zj = -2.97–1.266X1 + .033X2 – 1.491X3 + .901X4 + .637X5 + .130X6 – .012X7 Formula (2) Classification of firms is relatively simple. The values of the six variables for each firm are substituted into equation (2). Thus, each firm in both groups receives a Z score. If a firm’s Z score is more than the cutoff (critical) the firm is classified in group one (VLSPS). Conversely, a Z score less than the cutoff value will place the firm in group two (RCF). When group sizes are equal, as is the case in this study, the cutoff is the mean of the two group centroids (Garison, 2007). The group centroid for the RCF group is .790 and the group centroid for the VLSPS group is -.790. Thus, the cutoff rate in this study is zero. Since the two groups are heterogeneous, the expectation is that VLSPS firms will fall into one group and the RCF firms will fall into the other.

Interpretation of the results of discriminant analysis is usually accomplished by addressing four basic questions:

1. Is there a significant difference between the mean vectors of explanatory variables for the two groups of firms?

2. How well did the discriminant function perform?

3. How well did the independent variables perform?

4. Will this function discriminate as well on any random sample of firms as it did on the original sample?

To answer the first question, SPSS provides a Wilk’s Lamda–Chi Square transformation (Cooper and Schindler, 2001, p.581). The calculated value of Chi-Square is 95.12. That exceeds the critical value of Chi-Square of 14.07 at the five percent level of significance, with 7 degrees of freedom. The null hypothesis that there is no significant difference between the financial profiles of the two groups is therefore rejected, and the first conclusion drawn from the analysis is that the two groups have significantly different financial characteristics. This result was of course, expected since one group of firms was ranked number one for stock price stability and the other was chosen randomly. Further evidence of the strength of the Wilk’s Lamda–Chi Square transformation is given by the Eigenvalue in the SPSS output. The strength of the relationship between the pairs of variates is given by the canonical correlations. The Eigenvalues are simply the squared canonical correlations, and are sometimes referred to as the canonical roots (Hair et al, 1998, p.450). The closer the Eigenvalue is to the integer one, the greater is the strength of the relationship. The Eigenvalue given in this analysis is .631.

The discriminant function thus has the power to separate the two groups. However, this does not mean that it will in fact separate them. The ultimate value of a discriminant model depends on the results obtained. That is what percentage of firms as classified correctly and is that percentage significant?

To answer the second question a test of proportions is needed. Of the 100 firms in the VLSPS group, 74 were classified correctly. Of the 100 firms in the RCF group, 86 were classified correctly. That is, 160 of the 200 firms in the study, or 80 percent were classified correctly. In the use of this methodology that ratio (160/200) is sometimes referred to as the “hit ratio, and is similar to R2 in regression. The results are shown in Table 1.

Of course 80 percent is significant, but formal research requires the proof of a statistical test. To test whether or not an 80 percent correct classification rate is statistically significant, the Press’s Q test is appropriate (Hair et al, 1998, p. 270). Press’s Q is a Chi-square random variable:

Press’s Q = [N-(n x k)]2 / N(k-1) Formula (3)

where:

N = Total sample size

n = Number of cases correctly classified

k = Number of groups

In this case:

Press’s Q = [200-(160 x 2)]2 /200(2-1) = 72 > c2.05 3.84 with one d. f. Formula (4)

The null hypothesis that the percentage classified correctly is not significantly different from what would be classified correctly by chance is rejected. The evidence suggests that the discriminant function performed very well in separating the two groups. Again, given the disparity of the two groups, it is not surprising that the function classified eighty percent correct.

The arithmetic signs of the adjusted coefficients in Table 2 are important to answer question number three. A positive sign indicates that the greater a firm’s value for the variable, the more likely it will be in the VLSPS group. That is, it was greater than the cutoff value of zero, and designated as positive in Table 2. On the other hand, a negative sign for an adjusted coefficient signifies a Z score less than zero, and the greater a firm’s value for the variable, the more likely it will be classified in the RCF group. If a variable such as the growth rate for any firm has a negative value, it could be additive toward the calculation of the Z score. Thus, according to Table 2, the greater the following variables: the price earnings growth ratio, the Value Line rankings for safety, timeliness, and the level of operating leverage (risk), the more likely the firm would achieve a high level of stock price stability. Conversely, the greater the returns to total capital, the higher the degree of financial leverage, and the more predictable the earnings, the more likely the firm would be randomly chosen for this study.

The relative contribution of each variable to the total discriminating power of the function may be obtained by standardizing (pooled within group variances) the canonical coefficients of the discriminant function. These coefficients are given in the output of the SPSS 11.5.0 program. The standardized canonical coefficients are shown in Table 2.

Table 2 reveals that Value Line measure of safety of investment made the greatest contribution to the overall discriminating function. It is followed respectively by the price-earnings-growth ratio, the degree of financial leverage, earnings predictability, operating leverage, timeliness, and finally, the return to total capital. Some multicollinearity may exist between the variables, because return and the growth factor in the price earnings growth ratio may be a partial function of risk and leverage, and the numerator in the price earnings growth ratio may be a partial function of growth, return and risk. Hair et al. (1992) wrote that this consideration becomes critical in stepwise analysis and may be the factor determining whether a variable should be entered into a model. However, when all variables are entered into the model simultaneously, the discriminatory power of the model is a function of the variables evaluated as a set and multicollinearity becomes less important.

VALIDATION OF THE MODEL

Before any general conclusions can be drawn, a determination must be made on whether the model will yield valid results for any group of randomly drawn firms. The procedure used here for validation is referred to as the Lachenbruch or, more informally, the “jackknife” method. In this method, the discriminant function is fitted to repeatedly drawn samples of the original sample. The procedure estimates (k – 1) samples, and eliminates one case at a time from the original sample of “k” cases (Hair et al., 1992, p. 98). The expectation is that the proportion of firms classified correctly by the jackknife method would be less than that in the original sample due to the systematic bias associated with sampling errors. The major issue is whether the proportion classified correctly by the validation test differs significantly from the 80 percent classified correctly in the original test. That is, is the difference in the two proportions classified correctly by the two tests due to bias, and if so is that bias significant? The jackknife validation resulted in the correct classification of 76 percent of the firms. Since there are only two samples for analysis the binomial test is appropriate:

152 – 200(.80)/ [200 (.80) (.2)] 1/2 =–1.41 is not less than [t.sub.05] -1.645 Formula (5)

Thus, the null hypothesis that there is no significant difference between the proportion of firms classified correctly in the original test and the proportion classified correctly in the validation test cannot be rejected. Therefore, it can be concluded that while there may be some bias in the original analysis, it is not significant. The procedure will classify new firms as well as it did in the original analysis. In addition to the validation procedure, researchers usually address the question of the equality of matrices. One of the assumptions in using MDA is that the variance-covariance matrices of the two groups are equal. The SPSS program tests for equality of matrices by means of the natural logs of the determinants for the two covariance matrices. The greater the value of the log determinant the more the groups’ covariance matrix differs. The log determinant for the VLSPS group was -1.858, and for the RCF group it was -.216. Whereas these are very small values, it would be logical to conclude that the matrices are equal. However, a formal statistical test is required in scholarly research. Box’s M tests the significance of the log determinants, and thus, the assumption of homogeneity of covariance matrices. This test is very sensitive to meeting also the assumption of multivariate normality. (Garison, 2007). SPSS calculated M to be 84.006. M is then transformed by SPSS to the more familiar F statistic. The probability value of the F should be greater than the critical .05 level to demonstrate that the assumption of homoscedasticity is valid. (Garison, 2007; Hair et al., 1998). The F value for the test was 2.89, and the critical F.05 level is 1.69 with 29 and infinite degrees of freedom. Thus, the null hypothesis that the two matrices are equal cannot be rejected, and the midpoint value between the two group means (centroids) can be defined as the critical Z value. MDA is robust even when the homogeneity of variances assumption is not met, provided the data do not contain important outliers (Garison, 2007).

SUMMARY AND CONCLUSIONS

The purpose of this study was to establish a financial profile of firms that had achieved the highest ranking by Value Line for stock price stability in a period of economic recession, and to determine whether or not these firms have financial profiles that are significantly different from firms selected at random. The results of the statistical analysis indicated first, that there was a significant difference between financial variables that determine value, between the group of firms with high rankings for stock price stability, and firms chosen randomly, but from the same industries as the first group. The fact that the discriminant function separated two heterogeneous groups, and classified a significant proportion correctly is no surprise. In fact, the two groups of firms are so diverse in the matter of stock price stability that it would certainly have been a surprise if the discriminant function had not been so efficient. It was suggested that each group would have a unique financial profile. Table 2 summarizes the findings. According to Table 2, the greater the following variables: the price earnings growth ratio, the level of operating leverage (risk), the Value Line rankings for safety, and timeliness, the more likely the firm would achieve a high level of stock price stability. Conversely, the greater the returns to total capital, the higher the degree of financial leverage, and the more predictable the earnings, the more likely the firm would be randomly chosen for this study. Four of these results may have been expected, two were surprises, and one simply had no a priori expectations. The results on return to total capital, and financial leverage are consistent with the aforementioned study by Payne and Daghestani (1998). However, the findings for growth potential and operating risk were inconsistent with that study. Explanations of why the variables are associated with one group or the other are beyond the scope of this study, but again it suggests the need for further study. In any case, a few comments on the findings may be in order.

The variable ranked number one in discriminating power is the proxy for safety of investment. This should not be a surprise in a period of economic recession. It was expected that the Value Line ratings for safety, the market’s perception of price-earnings-growth potential, and the Value Line measure for the timeliness of an investment would be associated with price stability. Investors may shift funds into stocks that are perceived to be safer in times of recession. It would simply have been illogical for safety, the price-earnings-growth ratio and timeliness to not be associated with price stability. On the other hand, operating leverage was also associated with price stability, and this was a surprise. Operating leverage is a function of fixed operating costs that have to be met whether there is any cash inflow or not. In a time of economic recession the cash inflows to cover fixed costs are more uncertain.

The debt to total capital ratio is a measure of financial leverage (risk), and as expected, it was not associated with price stability. Financial leverage is an indicator of profit potential, but it also signals financial risk. It was measured in this study by the long-term debt to total capital ratio. According to the analysis, the greater the degree of financial leverage the more likely the firm would not be one of the firms identified by Value Line as having a high level of price level stability. Given that debt, beyond a level considered judicious creates risk, this result may have been expected (Van Horne, 2001). Moreover, this somewhat reinforces the fact that fixed operating costs were surprisingly associated with price stability. However, there have been recent studies that found that financial risk was associated with one group, and operating risk was characteristic of another. Thus, some firms may offset high levels of one type of risk with low levels of the other. This finding would not be possible with the use of levered market betas published by various financial services.

There were no a priori expectations for the return to total capital. In general, firms could expect fewer returns in an economic recession, but there was no empirical evidence of this. Thus, it was simply not known in which group this variable would be associated.

The most surprising aspect of the study was that earnings predictability was not associated with price stability. Prices are supposed to be a partial function of future earnings, and thus, it was expected that high levels of earnings predictability would be associated with price stability. This was not the case, and may in fact, defy logic. Obviously, this is a subject that requires further study.

This study has resulted in a contribution toward the construction of a theory that describes the financial characteristics of firms that maintain high levels of price stability in periods of economic recession. In order to make a more complete contribution, further research is needed. For example, it may be instructive to do the same analysis during a modern period of expansion. The economy of the United States is in growth period at the time of this writing. If the expansion continues, data should soon be available for such a study. The construction of a complete theory would aid managers, investors, academicians, and investment counselors by providing greater of knowledge on which to base investment decisions.

ENDNOTES

(1) The measure of stock price stability used in this study is the Value Line Stock Price Stability Rating. It is defined by Value Line as a relative ranking of the standard deviation of weekly percent changes in the price of a stock over the past five years. The ranks range from 100 for the most stable to 5 for the least stable. As a group, each of the Value Line ratings have historically outperformed the next lowest rated group (the one hundreds have outperformed the nineties, which outperformed the eighties, etc.). Value Line results have outperformed the DOW by 15 to 1 over the last 35-years. (Investor Home, 1999). The impressive performance of the rating system led many to refer to it as part of the “Value Line Anomaly,” or the “Value Line Enigma.”

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Bruce C. Payne, Barry University

Joan Wiggenhorn, Barry University

Adnan Daghestani, Barry University

Table 1: VLSPS-RCF CLASSIFICATION RESULTS

Predicted Results

Actual Results VLSPS RCF

VLSPS 74 26

RCF 14 86

Table 2: Relative Contribution of the Variables

Adjusted Variables Coefficient Rank

X1–The Return on total Capital -0.074 7

X2–The Price Earnings Growth Ratio 0.401 2

X3–Long term Debt to Total Capital -0.355 3

X4–Operating Leverage-Unlevered Beta 0.306 5

X5–The Value Line Safety Rating 0.452 1

X6–The Value Line Timeliness Rating 0.141 6

X7–Earnings Predictability -0.333 4

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