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Proportion of the news’ effect on competitor(s)’ returnFinally, to stick to our question of the influence of important news on competitors, the third test is run. To find the important news 10% of the returns with the highest deviation from the mean is identified. Since these events happen from time to time we cannot add autoregressive part in the regression. Also, instead of having company A’s returns as the dependent variable, we regress competitors’ returns at time t Here the p-value of Justification The ratio response part of the testing is designed to provide RN (competitors’ stocks reacted strongly in the opposite direction) and Neg (competitors’ stocks reacted in the opposite direction) ratios. They are then compared to the opposite ratio. The opposite ratio is a proxy to correlation between the competitors’ stock returns. If the RN or Neg ratio is higher than the opposite ratio, which would imply that on average significant news brought more information, which led to Situation X, than just any random news. If the RN or Neg ratio happened to be higher than 0.5 we can try to use computers. They would trade in competitors’ stocks in the opposite way and due to the fact that the ratio is higher than 0.5, on average they are going to earn profits. If RN ratio happens to be higher than 0.5, then the profits would be high. But if only Neg ratio happens to be above 0.5, then the profits are still going to be earned, but not as high. The proportion of competitors’ effect on stock price return is meant to provide explanation of a company’s stock return. If the values of Similarly, proportion of news’ effect on competitors’ return explains competitors’ reaction to company A’s news. If the value of
Hypothesis Date: 2015-12-17; view: 880
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