.RegressionsOur main interest is to examine whether the most preferred financial ratios used by equity ana-lysts might predict stock performance. We use a two-way random effects model for our regressionanalysis. The regression model we estimate takes the form: where RET is stock return at quarter t + n; n is 4 for 1 year future returns or 8 for 2 years. FI arealternative sets of most preferred financial ratios (specified in the next section) at quarter t andlog (MVE), the logarithm of the market capitalization of the firm’s equity, and BETA, the measureof systematic risk, act as control variables. Variables for Eq. (1) are computed using accountingdata and stock prices obtained from Economatica, from 1995 to 2011, for firms included in theMSE index.We control for serial correlation and heteroscedasticity using the Newey–West correction ofstandard errors.7Table 3 provides descriptive statistics and Table 4 provides Pearson correlation coefficients ofthe main variables used in the study. These correlation coefficients help to avoid the inclusion ofhighly correlated variables in the models, as we explain next.We have five FI categories (e.g., valuation, profit and margins, etc.). To reduce multicollinearity,alternative models are run with one variable per category. To avoid redundancy of informa-tion, and to have the number of variables/models manageable and results feasible to discuss in4Financial RETt+n = α + β1FIt + β2log (MVE) + β3 BETAt+n + ε (1)
in4Financial ratios involve the income statement and the balance sheet only, with the exception of leverage ratios whichuse market capitalization instead of book value of equity. Valuation ratios or multiples combine figures from the incomestatement or balance sheet with market stock prices. Cash flow ratios use figures from the cash flow statement. Per sharedata and growth indicators were placed in the “Other” category.5While this 50% cut-off is arbitrary, it has some intuition and some level of representativeness. First, it means thatfrom the 72 equity reports, among the 10 equity analysts, a “most preferred” financial ratio is used at least by half of theanalysts (we are not allowing double counting, as could be observed from Table 1). This selection is also convenient, asit allows to have at least two ratios per category, as Table 2 shows, which in turn, allows to include financial ratios of allcategories in the regression tests, and have alternative ratios within categories.6As a cross-check, we also analyzed only those recommendation reports with upgrades or downgrades. That is, whenthere was a change in the recommendation; for instance, from hold to buy. However, as the sample of reports with changesis very small, with only 12 observations in the sample, generalizations cannot be made and we do not tabulate/discuss indetail these results. The analysis, nevertheless, reinforces two points: (a) the main financial ratios that influence changein recommendation (e.g., EBITDA margin growth, EPS growth, sales growth) are also in our list of most preferred ratios(Table 2), and (b) while equity analysts use standard templates, they tailor their analysis to the company they assess byemphasizing different ratios.7Alternatively, we ran Arellano (1987) heteroscedasticity-corrected covariance matrices estimators (HCCME) withcluster correction. We find results similar to those reported in Table 5.
.RegressionsOur main interest is to examine whether the most preferred financial ratios used by equity ana-lysts might predict stock performance. We use a two-way random effects model for our regressionanalysis. The regression model we estimate takes the form: where RET is stock return at quarter t + n; n is 4 for 1 year future returns or 8 for 2 years. FI arealternative sets of most preferred financial ratios (specified in the next section) at quarter t andlog (MVE), the logarithm of the market capitalization of the firm’s equity, and BETA, the measureof systematic risk, act as control variables. Variables for Eq. (1) are computed using accountingdata and stock prices obtained from Economatica, from 1995 to 2011, for firms included in theMSE index.We control for serial correlation and heteroscedasticity using the Newey–West correction ofstandard errors.7Table 3 provides descriptive statistics and Table 4 provides Pearson correlation coefficients ofthe main variables used in the study. These correlation coefficients help to avoid the inclusion ofhighly correlated variables in the models, as we explain next.We have five FI categories (e.g., valuation, profit and margins, etc.). To reduce multicollinearity,alternative models are run with one variable per category. To avoid redundancy of informa-tion, and to have the number of variables/models manageable and results feasible to discuss in4Financial RETt+n = α + β1FIt + β2log (MVE) + β3 BETAt+n + ε (1)in4Financial ratios involve the income statement and the balance sheet only, with the exception of leverage ratios whichuse market capitalization instead of book value of equity. Valuation ratios or multiples combine figures from the incomestatement or balance sheet with market stock prices. Cash flow ratios use figures from the cash flow statement. Per sharedata and growth indicators were placed in the “Other” category.5While this 50% cut-off is arbitrary, it has some intuition and some level of representativeness. First, it means thatfrom the 72 equity reports, among the 10 equity analysts, a “most preferred” financial ratio is used at least by half of theanalysts (we are not allowing double counting, as could be observed from Table 1). This selection is also convenient, asit allows to have at least two ratios per category, as Table 2 shows, which in turn, allows to include financial ratios of allcategories in the regression tests, and have alternative ratios within categories.6As a cross-check, we also analyzed only those recommendation reports with upgrades or downgrades. That is, whenthere was a change in the recommendation; for instance, from hold to buy. However, as the sample of reports with changesis very small, with only 12 observations in the sample, generalizations cannot be made and we do not tabulate/discuss indetail these results. The analysis, nevertheless, reinforces two points: (a) the main financial ratios that influence changein recommendation (e.g., EBITDA margin growth, EPS growth, sales growth) are also in our list of most preferred ratios(Table 2), and (b) while equity analysts use standard templates, they tailor their analysis to the company they assess byemphasizing different ratios.7Alternatively, we ran Arellano (1987) heteroscedasticity-corrected covariance matrices estimators (HCCME) withcluster correction. We find results similar to those reported in Table 5.
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