THE IMPACT OF INDUSTRY CLASSIFICATIONS ON FINANCIAL RESEARCH

Using approximately 10,000 firms jointly covered by Compustat and CRSP from 1974-1993, we find substantial differences in the SIC codes desi gnated by the two databases. More than 36 percent of the classificatio ns disagree at the two-digit level and nearly 80 percent disagree at t he four-digit level. We examine the impact of these differences upon f inancial research in several ways. First, we show that the classificat ion of utilities, financial firms, and conglomerate acquisitions are a ffected by the choice of CRSP vs. Compustat SIC codes. Second, we show that industry classification matters in financial research by illustr ating that size- and industry-matched comparisons are more powerful th an pure size matches. Third, we test the specification and power of Co mpustat vs. CRSP classifications by simulating a typical financial exp eriment in which sample firms are matched to control firms by industry . We find that: i) Compustat matched samples are more powerful than CR SP matched samples in detecting abnormal performance; ii) nonparametri c tests outperform parametric tests; and iii) four-digit SIC code matc hes are more powerful than two-digit SIC code matches. These results a re robust to the inclusion or exclusion of extreme values, and hold fo r both NYSE/AMEX and Nasdaq firms.