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Are financial measures leading indicators to firm performance?


The purpose of this study is to extend both theoretical and empirical knowledge of the use of accounting-based and market-based measures in predicting firm performance. Many studies tested the ability of accounting and financial measures to predict firm's financial and operating risks. Occhino and Pescatori (2010) showed evidence that debt delinquencies aggravate credit risk and when ignored lead to a financial crisis. They explained, as the excessive debt increases, businesses decrease their investments in projects, which increases the probability to default; this creates a vicious cycle, which leads to the financial crisis.

Other studies investigated the use of these measures to predict firm's stock prices. Ofek and Richardson (2003) pointed out that during that year 2002 market crash period, the very high volume of trade in Internet stocks indicated a wide gap between the prices and their fundamental values. Demers and Lev (2001) gave two broad reasons for how Internet stocks reached unjustifiably high prices in the late 1990s and early 2000. The first focuses on the fundamental values that highlight the elements of capital gains and losses. Investors change their opinion often based on indicators rather than on fundamental values. The second suggests that fundamentals were indeed responsible for market prices but investors' interpretations of fundamentals were irrationally optimistic in making their assessments.

In this study a new aspect of the use of fundamental and market measures is tested; could these measures be used as leading measures of firm performance to help investors make better investment decisions? As the fundamental measures are unique characteristics of firms, they provide a good source of information to the investors in assessing firms' past performance. On the other hand, investors use market measures in buy and sell stock decisions. Given that the market is efficient, stock price at all times should be reflective of a firm's true value, which is a factor of firm performance.

Nowadays, in the era of Internet Technology, it is clear that investors have access to large volumes of information about firms to assess their performances and make decisions in stock markets. Most of the time, information is used to explain technical aspects of trading activities and overlooks measures that might be able to highlight a firm's strengths and weaknesses. The purpose of this study is to explore a set of fundamental and market measures that could be used as leading measures to predict firm performance and help investors make better investment decisions. The study includes the following sections: 1--theoretical background section that highlights the most relevant research in the field 2--testing hypotheses section that defines research problem; 3--research methodology section which describes the research tools, data collection, data analysis, limitation and implication of the study; and finally 5- conclusions and recommendations section that summarizes the research output.


In assessing firm financial performance, there are a wide variety of published fundamental measures used. These measures are lagging measures as they basically summarize the results of firm's past performance. Many researchers use these measures in their studies.

Allouche et al. (2008) use profitability ratios such as return on assets (ROA), return on equity (ROE), and return on invested capital (ROIC) as well as financial structure ratios (total debt/total capital, long term debt/total capital) to assess the performance of 1,271 Japanese companies.

Similarly, the results of a study done by Onaolapo and Kojala (2010) show evidence that a firm's capital structure surrogated by debt ratio had a negative impact on the firm performance (ROA and ROE). Gompers, Ishii, and Metrick (2003) tested the relationship between corporate governance, equity returns, and the firm's value using financial measures along with other measures. They concluded that corporate governance is positively correlated with equity returns and firms values. In a study about firm performance, Dastgir and Velashani (2008) found that comprehensive income is a good measure of a firm's performance. They reported that Earnings Per Share (EPS) is positively correlated with firm performance and argued that EPS is also a measure of shareholder value. Bettis and Hall (1982), Densetz and Lehn (1985) and Habib and Victor (1991) used ROA and ROE as performance proxies in their studies.

Additional studies that have incorporated the use of fundamental measures in assessing both firm performance and stock returns have similarly found utility in measures such as return on equity, return on assets, and corporate governance. Peng (2004) studied the relationship between external directors and their impact on sales and return on equity as indicators of firm performance. Frankel and Lee (1998) found that future firm performance may be over-estimated when return on equity forecasts are greater than current return on equity calculations. The inflated ROEs also impact the price investors are willing to pay for firm shares (i.e., they conclude that future performance is associated with forecasted return on equity ratios). Clubb and Naffi (2007) utilized return on equity (along with book-to-market values) as a predictor for stock returns. In their UK-based study, the authors found that ROE significantly served as a predictor for future stock returns by serving as an additional explanatory variable. Return on assets again resurfaces in studies by Cooper, et al (2008), Hitt, Hoskisson, and Kim (1997), and McKee, Varadarajan, and Pride. (1989).

Cooper, Gulen, and Schill (2008) conclude that asset performance strongly forecasts firm stock returns. They found a definite correlation between firm growth potential and return on assets. Hitt, Hoskisson, and Kim (1997) surmise that of the three fundamental measures they considered (return on assets, return on sales, and return on equity), return on assets quantifiably demonstrates a high degree of correlation with firm performance. McKee, Varadarajan, and Pride (1989) utilized return on assets and return on equity as measures for financial and operational performance because the ratios allow them to compare firms with different capitalizations and they are often cited in public information offerings.

Along with ROE and ROE, corporate governance structures continue to shed light on firm performance and returns as evidenced in the following studies. Core, Holthausen, and Larcker (1999) found that ownership structure impacts firm operations and stock returns. They conclude that weak governance creates bigger principal-agent problems and these agency problems in turn adversely impact firm performance. Bhagat and Bolton (2008) assessed how corporate governance, as measured through seven different measures, might impact performance. They found that better governance (along with board member vested interest and executive/managerial independence) often leads to better firm performance. Maher and Anderson (2000) found that corporate governance indirectly impacts firm performance by first affecting a country's capital markets, competitiveness, and growth. They posit that different types of ownership may lead to better management supervision which in turn impact firm performance.

On the other hand, other studies used market measures to explain stock price movements and firm performance. Shiller (2000) argued that price-earning is a good indicator of future inflation-adjusted stock market returns. Fama and French (1992), in a study about assets pricing model, showed evidence that the relationship between return and size, price-to-book ratio, and prior returns is an evidence of incremental return with a risk component not explained in the assets pricing model. Aras and Yilmaz (2008) used price-earnings, dividend yield and market-to-book ratio to predict returns in emerging markets. Ang and Bekaert (2007) talked about the reliability of using price-earnings ratio to predict future dividend growth. In the same direction, Lamont (1998) argued that price-earnings ratio has independent predictive power for excess returns.

Lewellen (2002) highlighted the predictive power of financial ratios in determining returns. Analysts believe that prices are a reflection of what the market thinks of the value of the stock and that today's market is driven by news and uncertainty. If the news is not good the market will not improve and if the news was good the stock prices will increase (Nettles & Matthew 2003). Nettles and Matthew (2003) argued that people invest in companies that promises high returns but whose financials are unable to meet these promises. Lei, Noussair, and Plott (2001) stated that noise trader move the market. But there are the rational arbitragers who offset the market and trade against them. Lo and Lio (2005) presented a model in which he showed that the noise traders have their influence on the market. He added that this would drive the prices away from the fundamentals causing an extreme deviation from the means of the price of the asset. Other studies that continue to advocate the use of market measures in assessing firm returns have regularly promoted dividend yields, market to book ratios, and price earnings ratios.

Hodrick (1992) used dividend yields as long-term (1 year or longer) forecasting tools for stock returns. His reasoning is backed by theoretical embracement of high dividend yields leading to high expected returns (i.e. dividend yields are found to possess high predictive powers). Chen, et al (2005) incorporate the use of dividend yields in their study of firm performance and value using 412 Hong Kong firms. Even though the authors find varying relationships between dividend yields and ownership structures given differing market capitalizations, they conclude that dividend policies as assessed via dividend yields are more correlated to firm performance in larger firms.

Chan, Hamao, and, Lakonishok (1991) look at market measures, including market-to-book ratios, to assess stock returns on Japanese firms. While looking at other measures including earnings yield and cash flow yields, the authors find that book-to-market is the most significant predictor of expected returns. Penman (1996) notes how the price-earnings ratio is often utilized as an indicator for earnings growth and a barometer for misaligned stock prices. He also states that given how price earnings ratios are indicative of earnings growth, they are thereby correlated to expected investor returns (as measured through ROE).

De Bondt and Thaler (1985) discussed how price earnings ratios have historically been shown to impact returns; low price earnings ratios tend to encounter larger adjusted returns, while high price earnings ratios tend to reflect lower adjusted returns. When considering the impact of price earnings ratios and stock market performance, Shen (2000) found that P/E ratios do in fact play a role in returns and hence stock market potential. High P/E ratios have been shown to precede dismal stock returns, but the author concludes that due to the current changing economic landscape, one cannot easily dismiss the relationship between this market measure and returns. It is quite evident that fundamental measures are key lagging measures to explain a firm's past performance and they are used along with market measures in many studies to explain stock price movements. However, it is of critical importance to identify a set of leading measures of firm profitability to help investors make sound investment decisions. The following is the research problem:

Could fundamental or market measures be used as leading measures of firm performance?


Research Instrument

To test if fundamental or market measures are good predictors of firms' future performance, this study uses a research instrument, which is made of four parts. In the first part, the correlation between a leading measure and a firm's performance measure is calculated for each of the firms in the study using the following equation (1).


In the second part One-Wav ANOVA (Analysis of Variance) is performed to test the significance of the correlations between the set of leading measures and firms' performance.

In the third part, multiple comparisons of two population correlations (i.e. leading measures) among the different sets are done to test if there is a significant difference between the mean correlations among the groups to select the strongest leading measure; testing is done using Tukey-Kramer procedure. Critical range (CR) is computed using the following equation (2):

Critical Range = [Q.sub.[alpha]][square root of MSW/2 (1/[n.sub.j] + 1/[n.sub.j'] Equation (2)

MSW is the mean sum of squares within the groups (unexplained); [Q.sub.[alpha]] is the upper-tail critical range value from studentized range distribution having c degrees of freedom in the numerator and n - c degrees of freedom in the denominator; [n.sub.j] = number of observations per group; n = number of observations in the study; and c = number of groups. The absolute difference between the two means is compared to the critical range. The two mean correlations are not equal if the absolute difference (ABS) between the two is greater than the critical range (CR).

In the fourth part, the population's coefficient correlation of the leading measures is tested if they are significantly different from zero using the following equation (3):

[t.sub.stat] = (r - P) / [[(1-[r.sup.2]) / (n - 2)].sup.0.5] Equation (3)

r = sample correlation coefficient, p = population coefficient correlation, n = sample size, degrees of freedom = n - 2. Alpha of 5% is used in the four parts of the study (Hair et. Al., 2012).

Sample and Data Collection

The sample data was collected over a twenty-year period (i.e. December 31, 1992 December 31, 2012. It included all public firms that are listed on national and regional exchange stock markets. Data was taken from Compustat. The original number of firms listed was 9,503 and because of missing data, 5,701 firms were removed from the study. The number of firms remained in the study was 3082.

Measurement of Variables

The variables in the study are made of the following two groups of leading measures: 1- Fundamental measures are return on equity (ROE) and return on assets (ROA); 2- Market measures are price earnings ratio (P/E), change in stock's market value (MV), return on investment (ROI). As for the firm's profitability (E%), it is measured by the average change in earnings of the next three years given a leading measure year. An example, the leading measure of year 2009 is correlated with the annual average change of earnings of the next three years i.e. years 2010, 2011, and 2012.

Data Analysis

The first stage of the study starts by highlighting the characteristics of the firm's profitability correlations with the leading measures. Table 1 represents mean leading measures correlations, which is done in two steps; 1) compute correlation coefficient of each of the 3082 firms using equation(1); 2) compute the average correlation (mean), which is the sum of the correlations divided by the number of firms. Summary output shows a firms' profitability correlation with 1- ROA is -1.05% with standard deviation of 40.68%; 2- ROE is -00.29% with standard deviation of 41.38%; 3- PE is +27.05% with standard deviation of 43.438%; 4- MV is 08.91% with standard deviation of 43.48%; and 5- ROI is -00.78% with standard deviation of 40.98%. The average correlation of firms' profitability with all leading measures is +03.32% with standard deviation of 26.84%. It is quite interesting that four of the leading measures have negative correlation with firm's profitability.

The second stage is to perform the one-way ANOVA to test if the five selected measures correlations with firms' profitability are significantly different. A 5% level of significance ([alpha]) is used. Table 2 presents the summary output. [F.sub.stat] is greater than [F.sub.critical] (P-value close to 0), this leads to conclude that there is a significant difference between the correlation coefficient of leading measures and firms' performances; in addition, it shows that the model's coefficient of determination is 7.95% i.e. the model explained 7.95% of the total variations (234.72 / 2,953.37).

In stage three of the study, multiple comparisons process is performed to test which leading measure is significantly different from the other ones as a result of stage two process as it showed significant evidence that the correlations of the five leading measures with firms' profitability are not equal.

First, the absolute value of the difference between the correlations among the five leading measures is computed. Second, the following steps are performed to compute the critical range (CR); 1) determine the critical value for an alpha of 5%, degrees of freedom in numerator = c = number of leading measures = 5; degrees of freedom in denominator = n - c = 15,410 (3082 * 5) - 5 = 15,405; Critical value is = 3.86; 2) compute the critical range using equation (2): 3.76 * SQRT [(0.27 / 2) * (1 / 3082 + 1 / 3082)] = 2.92%. Table 03 reflects the multiple comparisons between the five measures of correlations:
Table 3
Leading Measures--Multiple Comparisons

Multi-comparisons   Lead Measures             ABS--Mean
                    Correlations              Difference

ROA - ROE            01.05%      -00.29%       0.76%
ROA - PE            -01.05%      +27.05%      28.10%
ROA - MV            -01.05%      -08.91%       7.86%
ROA - ROI           -01.05%      -00.78%       0.27%
ROE - PE            -00.29%      +27.05%      27.34%
ROE - MV            -00.29%      -08.91%       8.62%
ROE - ROI            00.29%      -00.78%       0.50%
PE - MV             +27.05%      -08.91%      35.96%
PE - ROI            +27.05%      -00.78%      27.83%
MV - ROI            -08.91%      -00.78%       8.13%

Multi-comparisons   CR           Results

ROA - ROE           2.92%        Not Significant
ROA - PE            2.92%        Significant
ROA - MV            2.92%        Significant
ROA - ROI           2.92%        Not Significant
ROE - PE            2.92%        Significant
ROE - MV            2.92%        Significant
ROE - ROI           2.92%        Not Significant
PE - MV             2.92%        Significant
PE - ROI            2.92%        Significant
MV - ROI            2.92%        Significant

Table 3 shows that ROE and ROA (i.e. fundamental measures) are equal as their absolute mean difference is less than that of the critical range. In addition, they are significantly different from two of the market measures, which are PE and MV. As for the other market measure, ROI, it is not significantly different from the fundamental measures. Based on that two measures only showed evidence that they are significantly different and these are MV and PE, which are both market measures.

In stage four, the population correlation coefficient of the five leading measures is tested to check if they are significantly different from zero. The summary output is presented in table 04; the critical value of the t distribution is determined by using the following: 1- degrees of freedom of n -2 = 3082 - 2 = 3080; 2- experiment is a two tails test; 3- alpha = 5%; t critical value = +/- 1.96. As for [t.sub.stat], it is computed using equation (3). Results show that the coefficient correlation of PE and MV measures are significantly different from zero. As for the three other measures (ROI, ROA, and ROE), they are not significantly different from zero.

Cross Validity of the Model

The cross validity of the model is tested by applying it in different markets; to test it, it is recommended to conduct future studies using the same model in different markets.


There were two limitations in the study. First, a number of cases in this study had missing variables, which were removed from the study. Second, the external validity of the model was not tested.


Based on the above, it is recommended to explore the sources of unexplained variations by conducting studies to check the effect of 1- industry type, 2- volume, and or 3- other markets. In addition, it is recommended to apply the model in different markets to test its external validity.


The summary findings of the study are quite interesting, which are summarized in the following points: 1--Unexpectedly, the correlations of fundamental measures (i.e. ROA, ROE) and future earnings are not significantly different than zero i.e. firm's past performance is not a good predictor of its future performance. 2--The correlations of two of the market measures and the future performance are significantly different than zero. PE (Price Earning) ratio has a positive correlation with the firm's future earnings, which means that it could be used as a leading measure to predict firm's future performance; MV (Market Value) measure has a negative correlation with the firm's future earnings, which means when MV decreases future profitability of the firm increases and vice versa when MV increases future earnings decreases. Technically, the results are significant but have no theoretical foundation. It is expected that the stock price moves in the right direction based on market information i.e. if the market expectations about firm's profitability is positive, prices go up ; if the market expectations about firm's is negative, stock price moves down.

However, as the correlation is negative, it seems that in general the market is adjusting based on incorrect information or the market is affected by other factors, which make the market inefficient. 3- The standard deviation of the correlations is too high when compared to the mean correlations; on average, the standard deviation (27%) is almost nine times the mean (3%), which is a clear indicator that there are other factors affecting the results and are not in the research model. 4- Even though the results are significant, the research model explained 7.95% only of the total variations, which is another evidence that other key factors are not included in the research model.


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Victor Bahhouth

Ramin Maysami

Rebecca Gonzalez

The University of North Carolina- Pembroke

Victor Bahhouth is a Professor of Finance at the Department of Economics, Finance, and Decision Sciences--The University of North Carolina--Pembroke. He received his Doctorate of Business Administration in Finance from Newcastle Business School, University of Newcastle Upon Tyne--United Kingdom. His teaching interest is in the areas of corporate finance, financial management, and management accounting. His research interests are in the areas of contemporary issues related to international businesses, technology, and stock markets. He authored and coauthored research papers that have been published in refereed journals and in the proceedings of national and international academic conferences. He is a CPA and Certified Management Accountant.

Ramin Cooper Maysami is Professor of Economics and Finance , and Dean of School of Business at the University of North Carolina at Pembroke. His areas of research are regulation of financial institutions, interest-free banking and finance, entrepreneurship, and most recently online learning. His publications have appeared in academically refereed journal as well as professional/practitioners journals. Dr. Maysami's regular teaching schedule includes courses in Personal Finance, Entrepreneurship and Entrepreneurship Finance, Financial Institutions, and Microeconomics.

Rebecca Gonzalez is an Assistant Professor of Finance at the Department of Economics, Finance, and Decision Sciences--The University of North Carolina--Pembroke. She received her PhD in Business Administration with a Finance concentration from the University of Texas-Pan American. Her research interests are in the areas of entrepreneurial finance, financial markets and institutions, and behavioral finance. She has presented and published her research in refereed journals and national and regional academic conferences.

Source of     df    SS       Mean Square            [F.sub.stat]

Explained     C-1   SSA   MSA = SSA / (C-1)   [F.sub.stat] = MSA / MSW

Unexplained   N-C   SSW   MSW = SSW / (N-C)

Total         N-1

Table 1
Leading Measures Correlations Summary

Correlation            Count    Mean     Standard Deviation

Fundamental Measures
ROA - E%               3,082   -01.05%         40.68%
ROE - E%               3,082   -00.29%         41.38%
Market Measures
PE - E%                3,082   +27.05%         43.43%
MV - E%                3,082   -08.91%         43.48%
ROI - E%               3,082   -00.78%         40.98%
  Overall                      +03.21%         26.84%

Table 2
Leading Measures--One-way ANOVA

ANOVA--Leading Measures Correlations

Source of Variation    SS             df          MS

Explained              234.72         4           58.68
Unexplained            2718.65        15,405      0.17
Total                  2953.37        15,409

Source of Variation    [F.sub.stat]   P-value     [F.sub.crit]

Explained              332.50         5.93E-275   2.37

Table 4
Leading Measures--Test of Significance

Leading       Correlation   [t.sub.   [t.sub.     Results
Measures                    stat]     critical]


ROA           -1.05%        -0.58     +/- 1.96    Not Significant
ROE           -0.29%        -0.15     +/- 1.96    Not Significant


PE            27.05%        +15.60    +/- 1.96    Significant
MV            -8.91%        -4.96     +/- 1.96    Significant
ROI           -0.78%        -0.43     +/- 1.96    Not Significant
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Author:Bahhouth, Victor; Maysami, Ramin; Gonzalez, Rebecca
Publication:International Journal of Business, Accounting and Finance (IJBAF)
Article Type:Report
Geographic Code:1USA
Date:Sep 22, 2014
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