We investigate the possible predictability of firm growth in Taiwan using cross-sectional data of financial factors for the years 1997 and 2003 via principal component analysis. Our results reveal that the 18 financial variables (sales growth rate, total assets, total sales, return on assets, return on equity, gross margin, operating cost minus depreciation divided by sales plus other trading income, acid test ratio, debt–equity ratio, time interest earned, average receivables per average daily sales, inventory, average payables per average daily sales, working capital, working capital as a fraction of total assets, long-term liabilities as a fraction of total assets, and sales as a fraction of net worth of the firm) that we employ bunch together into five different financial ratios for the years 1997 and 2003 that are stable between these years. These financial factors are short-term liquidity, return on investment, long-term liquidity, firm size and capital turnover. Regressing these ratio groups (extra...

Financial factors and firm growth: evidence from financial data on Taiwanese firms

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An Empirical Analysis of Multibank Holding Company Performance in Texas.

Marion L. Chiattello [1] has provided additional empirical support for the suggestion that, because of the high degree of linear interdependence between many of the variables commonly used in banking regression studies, it may be necessary to interpret explanatory variables in a cross-sectional regression equation, not as representing individual influences, but as representing more general factors. Further, he has provided more empirical support for the suggestion that principal component analysis might be useful in helping to isolate and identify some of these general factors.

Further Comment: “Cross-Sectional Differences among Commercial Banks”

"cross- sectionai differences among commercial banks"

Robert J. Saunders [4] has demonstrated that, because of the high degree of linear interdependence among many of the variables commonly used in banking studies, it may be necessary to interpret explanatory variables in a cross-sectional regression equation, not as representing individual influences but as representing more general factors. He attempted to demonstrate how principal component analysis might be used to isolate and identify some of these general factors.

Marion L. Chiattello

Papers

Comment: “On the Use of Principal Components Analysis to Interpret Cross-Sectional Differences among Commercial Banks”