Indian Journal of Finance

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Evaluating Momentum and Contrarian Strategies For Technology Stocks During Excessive Speculation


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1 Assistant Professor of Economics, College of Business and Public Administration, University of North Dakota, ND, United States

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Momentum and contrarian returns have been documented by a large number of studies. In this paper we investigate the performance of technology stocks by using these two widely followed investment strategies during a speculative boom period. We discuss the effectiveness of various long and short trading strategies for a well balanced sample of technology stocks and find that by executing a simple buy bottom strategy, a technology investor could take advantage of the overreactions in order to generate higher returns during the period of excessive speculation. We also show that accounting for conditionally heteroskedasticity increases the evidence that risk adjusted returns are strongly related to market betas of a benchmark return and NASDAQ composite index returns.
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  • Evaluating Momentum and Contrarian Strategies For Technology Stocks During Excessive Speculation

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Authors

Pradosh Simlai
Assistant Professor of Economics, College of Business and Public Administration, University of North Dakota, ND, United States

Abstract


Momentum and contrarian returns have been documented by a large number of studies. In this paper we investigate the performance of technology stocks by using these two widely followed investment strategies during a speculative boom period. We discuss the effectiveness of various long and short trading strategies for a well balanced sample of technology stocks and find that by executing a simple buy bottom strategy, a technology investor could take advantage of the overreactions in order to generate higher returns during the period of excessive speculation. We also show that accounting for conditionally heteroskedasticity increases the evidence that risk adjusted returns are strongly related to market betas of a benchmark return and NASDAQ composite index returns.