CATEGORIES:
BiologyChemistryConstructionCultureEcologyEconomyElectronicsFinanceGeographyHistoryInformaticsLawMathematicsMechanicsMedicineOtherPedagogyPhilosophyPhysicsPolicyPsychologySociologySportTourism
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. Industry 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.
Proportion of the news’ effect on competitor(s)’ return H0: company A’s significant return does not negatively affect its competitors’ returns on the same day. Ha: company A’s significant return negatively affects its competitors’ returns on the same day.
Limitations Three limitations are immediately detectable in this research. The first is the lack of intraday data for a more thorough check on the competitors’ reactions. Due to this fact it is not possible to estimate the time taken for the prices to adjust to the information from the competitors’ news. If it is too short, and the prices adjust in less than a few seconds, then even computer-based trading may not be fast enough. However, that is not probably the case as human reaction and decision making is not that fast. The second problem is the introduction of control variable. The results show pure interdependence, without the market or industry intervention. However, as I wrote earlier, Situation X can happen if company A has 7% return, its competitor has 1% return and the market has 4% return. Overall, company A caused underperformance in its competitor, but a short-selling of the competitor’s stock is going to result in a loss. A possible remedy for that is the use of stock market betting – guessing which stock would outperform. The third limitation is the presence of transaction costs. Even if the forecast for competitor’s stock is “buy” and transaction is made, the trade may not earn any profit. Moreover, it may earn loss, if the transaction cost is higher than the profit from the trade. This could happen if the “buy” recommendation resulted in very low increase in the stock price. A possible remedy may be use of leverage, but that increases the risk.
Data Analysis Preparing Data To identify whether a news article was positive or negative, or rather, whether it had a positive effect on the share price we can look at the share price return on the day the news was released. If the return at time t is positive, then the news was positive as well. To identify whether an important news article was released we look at the volatility of the stock return on the same day. Significant news cause more volatility, and to identify only significant news we cancheck if the volatility of the returns was abnormal at time t, then there was a significant news release at time t. As we can see on the example of Agilent Technologies, some days show more volatility than others and there was a reason for that jump in the variance – some important news arrived that drove the stock up or down. And to identify these increases in the volatility we have to compare the returns on that day with the returns on the other days.
Date: 2015-12-17; view: 714
|
