Proportion of the news’ effect on competitor(s)’ return

Proportion of news’ effect on competitor(s)’

H₀: company A’s significant return does not negatively affect its competitors’ returns on the same day.

H_a: company A’s significant return negatively affects its competitors’ returns on the same day.

Null hypothesis is rejected for Apple at 99.9% confidence level. The results show that significant news articles for Apple have strong opposite effect on its competitors, Google and Hewlett Packard. “Significant news articles” in the study are identified as those which cause significant price change. Since the beta-coefficients for Google and Hewlett Packard are equal to -0.7736 and -0.9780 respectively, and since Apple has very high or low returns on the day, the arbitrage opportunities are available and are rather profitable.

Possibility for practical use

The results for several companies, most notably Microsoft and Apple show that investors can attempt to exploit arbitrage opportunities.

The best option would be to get a text-mining program check Apple news articles. If they are significant and positive, the program should short sell Google and Hewlett Packard shares. If they are significant and negative, the program should buy the mentioned stocks. Google’s and Hewlett Packard returns are estimated by the regressions:

Due to the existence of text-mining programs that check news articles, Apple share price adjusts to an important news piece almost instantaneously. For safe exploitation the arbitrage opportunity, it is suggested that the program not only checks the news article for Apple, but also its immediate stock price reaction. If the reaction is strong enough, namely if its absolute value is higher than 2.8%, then implement the trade.

The average value of the significant returns for Apple is 4.18%. The number of such significant returns during the sample period is 48. A transaction described above in Google stock can result in an average return of 3.23%. A similar transaction in Hewlett Packard Stock can result in an average return of 4.09%. This return is earned during one day, and such events happen 48 times a year (for the sample period), which results in 80.75% annual return for Google stock arbitrage profits and 98.16% annual return for Hewlett Packard.

However, the results are not corrected for error terms and industry effect. R-squared for AAPL-GOOG case is 0.51 and for AAPL-HPQ is 0.64, therefore there are many other factors that cause price changes. Moreover, industry itself (S&P Technology index) has even higher beta-coefficients in explaining Google’s and Hewlett Packard’s price changes.

Limitations of the research

As suggested in the methodology part, three limitations were detected before the work was started.

Control variables, such as S&P Technology index also have significant explanatory power. Even if the indicator says “Short Sell” competitor’s stock, the industry return can be significantly positive, suggesting an increase in competitor’s share price. To alleviate these effects, the trading program may check if the industry has low returns on that day, which would result in low effect on the share price from the index.

The second limitation is the presence of the transaction costs. Due to this fact some of the arbitrage opportunities disappear. Most notably Google and Hewlett Packard causing an opposite effect in Apple share price, as the beta-coefficients for their effects are low.

	Competitor 1		Competitor 2
		P-value		P-value
AAPL	-0.1225	0.0000	-0.1480	0.0000

With leverage it can be possible to earn arbitrage profits, but that greatly increases risk in case the regression fails.

The third limitation is the absence of intraday data. Due that fact it was not possible to check the duration of price adjustments to the news releases. It was not also possible to check the duration of price adjustments to competitors’ news articles. The arbitrage opportunities are conditional on the difference in the durations of these adjustments. If they are of equal length, then the only way to earn the profits is to check if the news article is significant and positive using a text-mining program and then immediately execute a trade in the competitor’s stock, without checking main company’s reaction.

Two more limitations were detected during the study.

The fourth limitation is that Yahoo Finance states Hewlett Packard to be a competitor to a number of firms within the sample. This is caused due to diversification of company’s business. Moreover, due to that fact the company is rarely vulnerable to competitors’ news that causes Situation X. Joh and Lee (1992) supported the statement – their sample of homogeneous line of business firms were more exposed to competitors’ news due to poor business diversification.

The final limitation is the fact that news often release outside trading hours. In these cases the prices adjust before the market opens and all arbitrage opportunities dissapear.