Proportion of competitors’ influence on the stock return
However, the testing previous method does not account for the proportions of the effects given by competitors and the market. The market may have only partial effect on the stock return or no effect at all. To get more precise values of the effects of the market and competitors a regression is run.
The
first test checks whether the company is affected by the market and
its competitors and if so, by how much. The return of Agilent
Technologies at time t
is regressed onto its previous-day return
,
first competitor’s return at time t
,
second competitor’s return at time t
(if there is such) and market’s return at time t
.
After
the regression is run, the p-values of
and
will show if competitors have any effect of the return of company A
at time t. The values of
and
themselves will show by how much the return of firm A is affected.
Market return is introduced into the regression to control for
economy-wide news. In the first and second tests we can conclude that
competitors’ stock returns negatively affect the company A if
and
values are significantly negative.
We
do control for market, but we do not control for the industry.
Technology industry may have its own news that we also have to take
into account. The second test almost exactly copies the first one,
but this time it controls for S&P Technology index returns at
time t
.
Again, p-values of and will show if competitors have any effect of the return of company A at time t. The values of and themselves will show by how much the return of firm A is affected.
Proportion of the news’ effect on competitor(s)’ return
Finally,
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
on firm A’s significant returns
.
Here
the p-value of
will show if the significant returns of company A have effect on the
competitors’ returns. And the value of
will show the scale of the effect. If the value of
is significantly negative we make a conclusion that significant
positive (negative) news for company A has a negative (positive)
effect on its competitor.
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 and for a company A are significantly negative, then we can imply that Situation X on average happens more often than Situation Y. With the use of intraday data, computers may analyse competitors’ returns. If the returns are positive (negative), then a short (long) position should be opened for company A’s stock. However, the intraday time frame for determination of the direction of competitors’ returns cannot be calculated in this dissertation due to the absence of intraday data. Since the data included all the observations, computers can perform trades on any day, not necessarily those with significant returns.
Similarly, proportion of news’ effect on competitors’ return explains competitors’ reaction to company A’s news. If the value of is significantly negative, we can conclude that on average Situation X effect is dominant. Then computers can take advantage of the Situation X and perform trade accordingly. To do so, first they check if the returns are significantly positive (negative) on an intraday scale after the news release for company A. If that is the case they open a short (long) position in the competitor(s)’ stock(s).
Hypothesis
Ratios of response to significant news analysis
Significant opposite reaction test
H0: news that significantly affects company A does not have a significant opposite effect on its competitors. RN < OR
Ha: news that significantly affects company A has a significant opposite effect on its competitors. RN > OR
Opposite reaction test
H0: news that significantly affects company A does not have an opposite effect on its competitors. Neg>OR
Ha: news that significantly affects company A has an opposite effect on its competitors. Neg>OR
Proportion of competitors’ influence on the stock return
Market return as control variable
H0:
there is no negative relationship between the return of company A and
its competitors.
Ha:
there is negative relationship between the return of company A and at
least one of its competitors.
