Correlation Between Internet Thailand and Cass Information
Can any of the company-specific risk be diversified away by investing in both Internet Thailand and Cass Information 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 Cass Information 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 Cass Information Systems, you can compare the effects of market volatilities on Internet Thailand and Cass Information 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 Cass Information. Check out your portfolio center. Please also check ongoing floating volatility patterns of Internet Thailand and Cass Information.
Diversification Opportunities for Internet Thailand and Cass Information
0.51 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Internet and Cass is 0.51. Overlapping area represents the amount of risk that can be diversified away by holding Internet Thailand PCL and Cass Information Systems in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Cass Information Systems 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 Cass Information. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Cass Information Systems has no effect on the direction of Internet Thailand i.e., Internet Thailand and Cass Information go up and down completely randomly.
Pair Corralation between Internet Thailand and Cass Information
Assuming the 90 days trading horizon Internet Thailand PCL is expected to under-perform the Cass Information. In addition to that, Internet Thailand is 2.7 times more volatile than Cass Information Systems. It trades about -0.06 of its total potential returns per unit of risk. Cass Information Systems is currently generating about -0.09 per unit of volatility. If you would invest 4,112 in Cass Information Systems on December 10, 2024 and sell it today you would lose (132.00) from holding Cass Information Systems or give up 3.21% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Internet Thailand PCL vs. Cass Information Systems
Performance |
Timeline |
Internet Thailand PCL |
Cass Information Systems |
Internet Thailand and Cass Information Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Internet Thailand and Cass Information
The main advantage of trading using opposite Internet Thailand and Cass Information positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Internet Thailand position performs unexpectedly, Cass Information 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 Cass Information will offset losses from the drop in Cass Information'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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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