Correlation Between Lian Hwa and Datavan International
Can any of the company-specific risk be diversified away by investing in both Lian Hwa and Datavan International 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 Lian Hwa and Datavan International into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Lian Hwa Foods and Datavan International, you can compare the effects of market volatilities on Lian Hwa and Datavan International 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 Lian Hwa with a short position of Datavan International. Check out your portfolio center. Please also check ongoing floating volatility patterns of Lian Hwa and Datavan International.
Diversification Opportunities for Lian Hwa and Datavan International
0.53 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Lian and Datavan is 0.53. Overlapping area represents the amount of risk that can be diversified away by holding Lian Hwa Foods and Datavan International in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Datavan International and Lian Hwa 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 Lian Hwa Foods are associated (or correlated) with Datavan International. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Datavan International has no effect on the direction of Lian Hwa i.e., Lian Hwa and Datavan International go up and down completely randomly.
Pair Corralation between Lian Hwa and Datavan International
Assuming the 90 days trading horizon Lian Hwa Foods is expected to generate 0.44 times more return on investment than Datavan International. However, Lian Hwa Foods is 2.29 times less risky than Datavan International. It trades about 0.25 of its potential returns per unit of risk. Datavan International is currently generating about 0.03 per unit of risk. If you would invest 10,650 in Lian Hwa Foods on September 16, 2024 and sell it today you would earn a total of 2,500 from holding Lian Hwa Foods or generate 23.47% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Lian Hwa Foods vs. Datavan International
Performance |
Timeline |
Lian Hwa Foods |
Datavan International |
Lian Hwa and Datavan International Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Lian Hwa and Datavan International
The main advantage of trading using opposite Lian Hwa and Datavan International positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Lian Hwa position performs unexpectedly, Datavan International 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 Datavan International will offset losses from the drop in Datavan International's long position.Lian Hwa vs. Standard Foods Corp | Lian Hwa vs. Uni President Enterprises Corp | Lian Hwa vs. Great Wall Enterprise | Lian Hwa vs. Ruentex Development Co |
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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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