Correlation Between Microfriend and I Components
Can any of the company-specific risk be diversified away by investing in both Microfriend and I Components 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 I Components into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Microfriend and i Components Co, you can compare the effects of market volatilities on Microfriend and I Components 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 I Components. Check out your portfolio center. Please also check ongoing floating volatility patterns of Microfriend and I Components.
Diversification Opportunities for Microfriend and I Components
0.0 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Microfriend and 059100 is 0.0. Overlapping area represents the amount of risk that can be diversified away by holding Microfriend and i Components Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on i Components 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 I Components. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of i Components has no effect on the direction of Microfriend i.e., Microfriend and I Components go up and down completely randomly.
Pair Corralation between Microfriend and I Components
Assuming the 90 days trading horizon Microfriend is expected to generate 2.78 times less return on investment than I Components. In addition to that, Microfriend is 1.23 times more volatile than i Components Co. It trades about 0.1 of its total potential returns per unit of risk. i Components Co is currently generating about 0.34 per unit of volatility. If you would invest 470,000 in i Components Co on October 13, 2024 and sell it today you would earn a total of 68,000 from holding i Components Co or generate 14.47% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Flat |
| Strength | Insignificant |
| Accuracy | 95.0% |
| Values | Daily Returns |
Microfriend vs. i Components Co
Performance |
| Timeline |
| Microfriend |
| i Components |
Microfriend and I Components Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Microfriend and I Components
The main advantage of trading using opposite Microfriend and I Components positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Microfriend position performs unexpectedly, I Components 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 I Components will offset losses from the drop in I Components' long position.| Microfriend vs. Samsung Publishing Co | Microfriend vs. InfoBank | Microfriend vs. BGF Retail Co | Microfriend vs. Osang Healthcare Co,Ltd |
| I Components vs. Atinum Investment Co | I Components vs. NICE Information Service | I Components vs. NH Investment Securities | I Components vs. Lotte Data Communication |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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