Related Experiment Video
Updated: Jul 25, 2025

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
Published on: June 9, 2020
Stocks Opening Price Gaps and Adjustments to New Information
Aiche Avishay1, Cohen Gil1, Griskin Vladimir1
1Department of Management, Western Galilee Academic College, Acre, Israel.
Abstract:
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.
Related Concept Videos
The Anchoring-and-Adjustment Heuristic
Testing a Claim about Standard Deviation
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Quantifying and Rejecting Outliers: The Grubbs Test
Modified Boxplots
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
Margin of Error

