Correlation Between BIT Computer and Seoul Food
Can any of the company-specific risk be diversified away by investing in both BIT Computer and Seoul Food 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 BIT Computer and Seoul Food into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between BIT Computer Co and Seoul Food Industrial, you can compare the effects of market volatilities on BIT Computer and Seoul Food 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 BIT Computer with a short position of Seoul Food. Check out your portfolio center. Please also check ongoing floating volatility patterns of BIT Computer and Seoul Food.
Diversification Opportunities for BIT Computer and Seoul Food
0.71 | Correlation Coefficient |
Poor diversification
The 3 months correlation between BIT and Seoul is 0.71. Overlapping area represents the amount of risk that can be diversified away by holding BIT Computer Co and Seoul Food Industrial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Seoul Food Industrial and BIT Computer 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 BIT Computer Co are associated (or correlated) with Seoul Food. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Seoul Food Industrial has no effect on the direction of BIT Computer i.e., BIT Computer and Seoul Food go up and down completely randomly.
Pair Corralation between BIT Computer and Seoul Food
Assuming the 90 days trading horizon BIT Computer Co is expected to generate 1.4 times more return on investment than Seoul Food. However, BIT Computer is 1.4 times more volatile than Seoul Food Industrial. It trades about -0.06 of its potential returns per unit of risk. Seoul Food Industrial is currently generating about -0.11 per unit of risk. If you would invest 496,611 in BIT Computer Co on December 22, 2024 and sell it today you would lose (21,611) from holding BIT Computer Co or give up 4.35% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
BIT Computer Co vs. Seoul Food Industrial
Performance |
Timeline |
BIT Computer |
Seoul Food Industrial |
BIT Computer and Seoul Food Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with BIT Computer and Seoul Food
The main advantage of trading using opposite BIT Computer and Seoul Food positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if BIT Computer position performs unexpectedly, Seoul Food 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 Seoul Food will offset losses from the drop in Seoul Food's long position.BIT Computer vs. Mobileleader CoLtd | BIT Computer vs. Bookook Steel | BIT Computer vs. Korea Information Communications | BIT Computer vs. Nable Communications |
Seoul Food vs. Samlip General Foods | Seoul Food vs. Lotte Rental Co | Seoul Food vs. Value Added Technology | Seoul Food vs. Digital Imaging Technology |
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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