This research studies different gap opening price strategies using artificial intelligence and big data analysis to learn how fast new information is absorbed into the stock's price. Our system is designed to optimize trading results of different gap opening investment strategies. Our data consist of ten years of daily trading prices of all the stocks comprising the three major U.S. stocks indices: S&P 500, Nasdaq100, and Russell 2000. The scope of this research, to the best of our knowledge, has never been attempted before, covering most of the U.S.A. economy across various economic conditions and market trends. We found that negative gap openings are much greater than positive gaps opening. This result is stronger for Russell2000 stocks and Nasdaq100 stocks than for S&P500 stocks. Moreover, consistent with the theoretical framework, the price adjustment for bad news was found to be quicker than for good news. We also found that after positive gaps opening price drifts occur, the stock's price rises even stronger, providing profitable trading opportunities.
Keywords: Algorithmic Trading; Bad and Good News; Long Short Strategies; Price Gaps; Stocks Information.
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