The objective of the present study is to test for pricing efficiency in equities of individual information technology companies in the emerging Indian market, where the sector holds an important place in the domestic economy and is a significant contributor to the country’s exports. The fifty-six companies currently comprising the BSE IT index are studied for the possible presence of persistence in returns. Employing all continuously available price data for these firms, the Hurst exponent is estimated using three fractal analysis techniques, viz., rescaled range, roughness length, and wavelets. Persistence or “long memory” is unambiguously detected in eleven, or roughly 20% of the return series; antipersistence is detected in the case of two series. The results suggest that not all Indian information technology securities are priced efficiently and that there exists the potential for investors to exploit a long-memory characteristic in those stocks to extract excess profits from trading rules based on historical price information.
