Correlation Between Lotte Chilsung and Shinhan Financial
Can any of the company-specific risk be diversified away by investing in both Lotte Chilsung and Shinhan Financial 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 Chilsung and Shinhan Financial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Lotte Chilsung Beverage and Shinhan Financial Group, you can compare the effects of market volatilities on Lotte Chilsung and Shinhan Financial 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 Chilsung with a short position of Shinhan Financial. Check out your portfolio center. Please also check ongoing floating volatility patterns of Lotte Chilsung and Shinhan Financial.
Diversification Opportunities for Lotte Chilsung and Shinhan Financial
0.06 | Correlation Coefficient |
Significant diversification
The 3 months correlation between Lotte and Shinhan is 0.06. Overlapping area represents the amount of risk that can be diversified away by holding Lotte Chilsung Beverage and Shinhan Financial Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shinhan Financial and Lotte Chilsung 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 Chilsung Beverage are associated (or correlated) with Shinhan Financial. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shinhan Financial has no effect on the direction of Lotte Chilsung i.e., Lotte Chilsung and Shinhan Financial go up and down completely randomly.
Pair Corralation between Lotte Chilsung and Shinhan Financial
Assuming the 90 days trading horizon Lotte Chilsung Beverage is expected to under-perform the Shinhan Financial. In addition to that, Lotte Chilsung is 1.01 times more volatile than Shinhan Financial Group. It trades about -0.05 of its total potential returns per unit of risk. Shinhan Financial Group is currently generating about -0.02 per unit of volatility. If you would invest 4,875,053 in Shinhan Financial Group on December 23, 2024 and sell it today you would lose (95,053) from holding Shinhan Financial Group or give up 1.95% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Lotte Chilsung Beverage vs. Shinhan Financial Group
Performance |
Timeline |
Lotte Chilsung Beverage |
Shinhan Financial |
Lotte Chilsung and Shinhan Financial Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Lotte Chilsung and Shinhan Financial
The main advantage of trading using opposite Lotte Chilsung and Shinhan Financial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Lotte Chilsung position performs unexpectedly, Shinhan Financial 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 Shinhan Financial will offset losses from the drop in Shinhan Financial's long position.Lotte Chilsung vs. Echomarketing CoLtd | Lotte Chilsung vs. Samwon Steel | Lotte Chilsung vs. NICE Information Service | Lotte Chilsung vs. Fine Besteel 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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