Correlation Between Unilever Indonesia and Dow Jones
Can any of the company-specific risk be diversified away by investing in both Unilever Indonesia and Dow Jones 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 Unilever Indonesia and Dow Jones into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Unilever Indonesia Tbk and Dow Jones Industrial, you can compare the effects of market volatilities on Unilever Indonesia and Dow Jones 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 Unilever Indonesia with a short position of Dow Jones. Check out your portfolio center. Please also check ongoing floating volatility patterns of Unilever Indonesia and Dow Jones.
Diversification Opportunities for Unilever Indonesia and Dow Jones
-0.59 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Unilever and Dow is -0.59. Overlapping area represents the amount of risk that can be diversified away by holding Unilever Indonesia Tbk and Dow Jones Industrial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dow Jones Industrial and Unilever Indonesia 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 Unilever Indonesia Tbk are associated (or correlated) with Dow Jones. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dow Jones Industrial has no effect on the direction of Unilever Indonesia i.e., Unilever Indonesia and Dow Jones go up and down completely randomly.
Pair Corralation between Unilever Indonesia and Dow Jones
Assuming the 90 days trading horizon Unilever Indonesia Tbk is expected to under-perform the Dow Jones. In addition to that, Unilever Indonesia is 2.71 times more volatile than Dow Jones Industrial. It trades about -0.12 of its total potential returns per unit of risk. Dow Jones Industrial is currently generating about 0.26 per unit of volatility. If you would invest 4,238,757 in Dow Jones Industrial on August 28, 2024 and sell it today you would earn a total of 234,900 from holding Dow Jones Industrial or generate 5.54% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Unilever Indonesia Tbk vs. Dow Jones Industrial
Performance |
Timeline |
Unilever Indonesia and Dow Jones Volatility Contrast
Predicted Return Density |
Returns |
Unilever Indonesia Tbk
Pair trading matchups for Unilever Indonesia
Dow Jones Industrial
Pair trading matchups for Dow Jones
Pair Trading with Unilever Indonesia and Dow Jones
The main advantage of trading using opposite Unilever Indonesia and Dow Jones positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Unilever Indonesia position performs unexpectedly, Dow Jones 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 Dow Jones will offset losses from the drop in Dow Jones' long position.Unilever Indonesia vs. PT Indofood Sukses | Unilever Indonesia vs. Astra International Tbk | Unilever Indonesia vs. Telkom Indonesia Tbk | Unilever Indonesia vs. Bank Central Asia |
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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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