Correlation Between Shin Kong and Acbel Polytech
Can any of the company-specific risk be diversified away by investing in both Shin Kong and Acbel Polytech 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 Shin Kong and Acbel Polytech into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Shin Kong Financial and Acbel Polytech, you can compare the effects of market volatilities on Shin Kong and Acbel Polytech 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 Shin Kong with a short position of Acbel Polytech. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shin Kong and Acbel Polytech.
Diversification Opportunities for Shin Kong and Acbel Polytech
-0.48 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Shin and Acbel is -0.48. Overlapping area represents the amount of risk that can be diversified away by holding Shin Kong Financial and Acbel Polytech in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Acbel Polytech and Shin Kong 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 Shin Kong Financial are associated (or correlated) with Acbel Polytech. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Acbel Polytech has no effect on the direction of Shin Kong i.e., Shin Kong and Acbel Polytech go up and down completely randomly.
Pair Corralation between Shin Kong and Acbel Polytech
Assuming the 90 days trading horizon Shin Kong Financial is expected to generate 0.3 times more return on investment than Acbel Polytech. However, Shin Kong Financial is 3.37 times less risky than Acbel Polytech. It trades about 0.18 of its potential returns per unit of risk. Acbel Polytech is currently generating about -0.05 per unit of risk. If you would invest 1,140 in Shin Kong Financial on October 26, 2024 and sell it today you would earn a total of 90.00 from holding Shin Kong Financial or generate 7.89% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Shin Kong Financial vs. Acbel Polytech
Performance |
| Timeline |
| Shin Kong Financial |
| Acbel Polytech |
Shin Kong and Acbel Polytech Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Shin Kong and Acbel Polytech
The main advantage of trading using opposite Shin Kong and Acbel Polytech positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shin Kong position performs unexpectedly, Acbel Polytech 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 Acbel Polytech will offset losses from the drop in Acbel Polytech's long position.| Shin Kong vs. Cathay Financial Holding | Shin Kong vs. Taishin Financial Holding | Shin Kong vs. Fubon Financial Holding | Shin Kong vs. CTBC Financial Holding |
| Acbel Polytech vs. Cheng Uei Precision | Acbel Polytech vs. Gemtek Technology Co | Acbel Polytech vs. Kinpo Electronics | Acbel Polytech vs. Darfon Electronics Corp |
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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