Correlation Between Datasonic Group and KPJ Healthcare
Can any of the company-specific risk be diversified away by investing in both Datasonic Group and KPJ Healthcare 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 Datasonic Group and KPJ Healthcare into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Datasonic Group Bhd and KPJ Healthcare Bhd, you can compare the effects of market volatilities on Datasonic Group and KPJ Healthcare 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 Datasonic Group with a short position of KPJ Healthcare. Check out your portfolio center. Please also check ongoing floating volatility patterns of Datasonic Group and KPJ Healthcare.
Diversification Opportunities for Datasonic Group and KPJ Healthcare
-0.32 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Datasonic and KPJ is -0.32. Overlapping area represents the amount of risk that can be diversified away by holding Datasonic Group Bhd and KPJ Healthcare Bhd in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on KPJ Healthcare Bhd and Datasonic Group 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 Datasonic Group Bhd are associated (or correlated) with KPJ Healthcare. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of KPJ Healthcare Bhd has no effect on the direction of Datasonic Group i.e., Datasonic Group and KPJ Healthcare go up and down completely randomly.
Pair Corralation between Datasonic Group and KPJ Healthcare
Assuming the 90 days trading horizon Datasonic Group Bhd is expected to under-perform the KPJ Healthcare. In addition to that, Datasonic Group is 1.08 times more volatile than KPJ Healthcare Bhd. It trades about -0.04 of its total potential returns per unit of risk. KPJ Healthcare Bhd is currently generating about 0.1 per unit of volatility. If you would invest 210.00 in KPJ Healthcare Bhd on September 25, 2024 and sell it today you would earn a total of 25.00 from holding KPJ Healthcare Bhd or generate 11.9% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 98.44% |
Values | Daily Returns |
Datasonic Group Bhd vs. KPJ Healthcare Bhd
Performance |
Timeline |
Datasonic Group Bhd |
KPJ Healthcare Bhd |
Datasonic Group and KPJ Healthcare Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Datasonic Group and KPJ Healthcare
The main advantage of trading using opposite Datasonic Group and KPJ Healthcare positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Datasonic Group position performs unexpectedly, KPJ Healthcare 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 KPJ Healthcare will offset losses from the drop in KPJ Healthcare's long position.Datasonic Group vs. Computer Forms Bhd | Datasonic Group vs. Central Industrial Corp | Datasonic Group vs. Press Metal Bhd | Datasonic Group vs. Sports Toto Berhad |
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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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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