Correlation Between Malayan Banking and SSF Home
Can any of the company-specific risk be diversified away by investing in both Malayan Banking and SSF Home 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 Malayan Banking and SSF Home into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Malayan Banking Bhd and SSF Home Group, you can compare the effects of market volatilities on Malayan Banking and SSF Home 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 Malayan Banking with a short position of SSF Home. Check out your portfolio center. Please also check ongoing floating volatility patterns of Malayan Banking and SSF Home.
Diversification Opportunities for Malayan Banking and SSF Home
-0.07 | Correlation Coefficient |
Good diversification
The 3 months correlation between Malayan and SSF is -0.07. Overlapping area represents the amount of risk that can be diversified away by holding Malayan Banking Bhd and SSF Home Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SSF Home Group and Malayan Banking 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 Malayan Banking Bhd are associated (or correlated) with SSF Home. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of SSF Home Group has no effect on the direction of Malayan Banking i.e., Malayan Banking and SSF Home go up and down completely randomly.
Pair Corralation between Malayan Banking and SSF Home
Assuming the 90 days trading horizon Malayan Banking is expected to generate 13.84 times less return on investment than SSF Home. But when comparing it to its historical volatility, Malayan Banking Bhd is 3.35 times less risky than SSF Home. It trades about 0.05 of its potential returns per unit of risk. SSF Home Group is currently generating about 0.2 of returns per unit of risk over similar time horizon. If you would invest 35.00 in SSF Home Group on November 2, 2024 and sell it today you would earn a total of 3.00 from holding SSF Home Group or generate 8.57% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Malayan Banking Bhd vs. SSF Home Group
Performance |
| Timeline |
| Malayan Banking Bhd |
| SSF Home Group |
Malayan Banking and SSF Home Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Malayan Banking and SSF Home
The main advantage of trading using opposite Malayan Banking and SSF Home positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Malayan Banking position performs unexpectedly, SSF Home 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 SSF Home will offset losses from the drop in SSF Home's long position.| Malayan Banking vs. Press Metal Bhd | Malayan Banking vs. Computer Forms Bhd | Malayan Banking vs. Melewar Industrial Group | Malayan Banking vs. Kluang Rubber |
| SSF Home vs. Aeon Credit Service | SSF Home vs. Kawan Food Bhd | SSF Home vs. CPE Technology Berhad | SSF Home vs. ES Ceramics Technology |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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