Correlation Between Matahari Department and Kimia Farma
Can any of the company-specific risk be diversified away by investing in both Matahari Department and Kimia Farma 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 Matahari Department and Kimia Farma into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Matahari Department Store and Kimia Farma Persero, you can compare the effects of market volatilities on Matahari Department and Kimia Farma 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 Matahari Department with a short position of Kimia Farma. Check out your portfolio center. Please also check ongoing floating volatility patterns of Matahari Department and Kimia Farma.
Diversification Opportunities for Matahari Department and Kimia Farma
0.86 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Matahari and Kimia is 0.86. Overlapping area represents the amount of risk that can be diversified away by holding Matahari Department Store and Kimia Farma Persero in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kimia Farma Persero and Matahari Department 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 Matahari Department Store are associated (or correlated) with Kimia Farma. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kimia Farma Persero has no effect on the direction of Matahari Department i.e., Matahari Department and Kimia Farma go up and down completely randomly.
Pair Corralation between Matahari Department and Kimia Farma
Assuming the 90 days trading horizon Matahari Department Store is expected to generate 0.82 times more return on investment than Kimia Farma. However, Matahari Department Store is 1.22 times less risky than Kimia Farma. It trades about -0.23 of its potential returns per unit of risk. Kimia Farma Persero is currently generating about -0.32 per unit of risk. If you would invest 151,500 in Matahari Department Store on September 2, 2024 and sell it today you would lose (10,000) from holding Matahari Department Store or give up 6.6% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Matahari Department Store vs. Kimia Farma Persero
Performance |
Timeline |
Matahari Department Store |
Kimia Farma Persero |
Matahari Department and Kimia Farma Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Matahari Department and Kimia Farma
The main advantage of trading using opposite Matahari Department and Kimia Farma positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Matahari Department position performs unexpectedly, Kimia Farma 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 Kimia Farma will offset losses from the drop in Kimia Farma's long position.Matahari Department vs. Japfa Comfeed Indonesia | Matahari Department vs. Charoen Pokphand Indonesia | Matahari Department vs. Erajaya Swasembada Tbk | Matahari Department vs. Indofood Cbp Sukses |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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