Correlation Between Microfriend and Jahwa Electron
Can any of the company-specific risk be diversified away by investing in both Microfriend and Jahwa Electron 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 Microfriend and Jahwa Electron into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Microfriend and Jahwa Electron, you can compare the effects of market volatilities on Microfriend and Jahwa Electron 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 Microfriend with a short position of Jahwa Electron. Check out your portfolio center. Please also check ongoing floating volatility patterns of Microfriend and Jahwa Electron.
Diversification Opportunities for Microfriend and Jahwa Electron
0.66 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Microfriend and Jahwa is 0.66. Overlapping area represents the amount of risk that can be diversified away by holding Microfriend and Jahwa Electron in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Jahwa Electron and Microfriend 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 Microfriend are associated (or correlated) with Jahwa Electron. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Jahwa Electron has no effect on the direction of Microfriend i.e., Microfriend and Jahwa Electron go up and down completely randomly.
Pair Corralation between Microfriend and Jahwa Electron
Assuming the 90 days trading horizon Microfriend is expected to under-perform the Jahwa Electron. In addition to that, Microfriend is 1.41 times more volatile than Jahwa Electron. It trades about -0.08 of its total potential returns per unit of risk. Jahwa Electron is currently generating about -0.07 per unit of volatility. If you would invest 2,970,000 in Jahwa Electron on November 3, 2024 and sell it today you would lose (1,425,000) from holding Jahwa Electron or give up 47.98% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 99.58% |
| Values | Daily Returns |
Microfriend vs. Jahwa Electron
Performance |
| Timeline |
| Microfriend |
| Jahwa Electron |
Microfriend and Jahwa Electron Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Microfriend and Jahwa Electron
The main advantage of trading using opposite Microfriend and Jahwa Electron positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Microfriend position performs unexpectedly, Jahwa Electron 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 Jahwa Electron will offset losses from the drop in Jahwa Electron's long position.| Microfriend vs. Samyang Foods Co | Microfriend vs. KEPCO Engineering Construction | Microfriend vs. Daewoo Engineering Construction | Microfriend vs. Alton Sports CoLtd |
| Jahwa Electron vs. NICE Information Service | Jahwa Electron vs. Display Tech Co | Jahwa Electron vs. Lotte Rental Co | Jahwa Electron vs. SK Chemicals Co |
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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