Correlation Between Internet Thailand and United Internet
Can any of the company-specific risk be diversified away by investing in both Internet Thailand and United Internet at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining Internet Thailand and United Internet into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Internet Thailand PCL and United Internet AG, you can compare the effects of market volatilities on Internet Thailand and United Internet and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in Internet Thailand with a short position of United Internet. Check out your portfolio center. Please also check ongoing floating volatility patterns of Internet Thailand and United Internet.
Diversification Opportunities for Internet Thailand and United Internet
-0.92 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Internet and United is -0.92. Overlapping area represents the amount of risk that can be diversified away by holding Internet Thailand PCL and United Internet AG in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on United Internet AG and Internet Thailand is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on Internet Thailand PCL are associated (or correlated) with United Internet. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of United Internet AG has no effect on the direction of Internet Thailand i.e., Internet Thailand and United Internet go up and down completely randomly.
Pair Corralation between Internet Thailand and United Internet
Assuming the 90 days trading horizon Internet Thailand PCL is expected to generate 2.06 times more return on investment than United Internet. However, Internet Thailand is 2.06 times more volatile than United Internet AG. It trades about 0.15 of its potential returns per unit of risk. United Internet AG is currently generating about -0.09 per unit of risk. If you would invest 12.00 in Internet Thailand PCL on September 17, 2024 and sell it today you would earn a total of 6.00 from holding Internet Thailand PCL or generate 50.0% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Internet Thailand PCL vs. United Internet AG
Performance |
Timeline |
Internet Thailand PCL |
United Internet AG |
Internet Thailand and United Internet Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Internet Thailand and United Internet
The main advantage of trading using opposite Internet Thailand and United Internet positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Internet Thailand position performs unexpectedly, United Internet can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in United Internet will offset losses from the drop in United Internet's long position.Internet Thailand vs. Apple Inc | Internet Thailand vs. Apple Inc | Internet Thailand vs. Apple Inc | Internet Thailand vs. Apple Inc |
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Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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