Correlation Between Lotte Chemical and Bukit Uluwatu
Can any of the company-specific risk be diversified away by investing in both Lotte Chemical and Bukit Uluwatu 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 Lotte Chemical and Bukit Uluwatu into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Lotte Chemical Titan and Bukit Uluwatu Villa, you can compare the effects of market volatilities on Lotte Chemical and Bukit Uluwatu 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 Lotte Chemical with a short position of Bukit Uluwatu. Check out your portfolio center. Please also check ongoing floating volatility patterns of Lotte Chemical and Bukit Uluwatu.
Diversification Opportunities for Lotte Chemical and Bukit Uluwatu
-0.05 | Correlation Coefficient |
Good diversification
The 3 months correlation between Lotte and Bukit is -0.05. Overlapping area represents the amount of risk that can be diversified away by holding Lotte Chemical Titan and Bukit Uluwatu Villa in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bukit Uluwatu Villa and Lotte Chemical 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 Lotte Chemical Titan are associated (or correlated) with Bukit Uluwatu. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bukit Uluwatu Villa has no effect on the direction of Lotte Chemical i.e., Lotte Chemical and Bukit Uluwatu go up and down completely randomly.
Pair Corralation between Lotte Chemical and Bukit Uluwatu
Assuming the 90 days trading horizon Lotte Chemical Titan is expected to under-perform the Bukit Uluwatu. But the stock apears to be less risky and, when comparing its historical volatility, Lotte Chemical Titan is 2.39 times less risky than Bukit Uluwatu. The stock trades about -0.06 of its potential returns per unit of risk. The Bukit Uluwatu Villa is currently generating about 0.05 of returns per unit of risk over similar time horizon. If you would invest 5,400 in Bukit Uluwatu Villa on September 13, 2024 and sell it today you would earn a total of 500.00 from holding Bukit Uluwatu Villa or generate 9.26% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Lotte Chemical Titan vs. Bukit Uluwatu Villa
Performance |
Timeline |
Lotte Chemical Titan |
Bukit Uluwatu Villa |
Lotte Chemical and Bukit Uluwatu Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Lotte Chemical and Bukit Uluwatu
The main advantage of trading using opposite Lotte Chemical and Bukit Uluwatu positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Lotte Chemical position performs unexpectedly, Bukit Uluwatu 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 Bukit Uluwatu will offset losses from the drop in Bukit Uluwatu's long position.Lotte Chemical vs. Kedaung Indah Can | Lotte Chemical vs. Kabelindo Murni Tbk | Lotte Chemical vs. Champion Pacific Indonesia | Lotte Chemical vs. Bhuwanatala Indah Permai |
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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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