Correlation Between Shin Kong and Data International
Can any of the company-specific risk be diversified away by investing in both Shin Kong and Data International 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 Data International 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 Data International Co, you can compare the effects of market volatilities on Shin Kong and Data International 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 Data International. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shin Kong and Data International.
Diversification Opportunities for Shin Kong and Data International
-0.4 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Shin and Data is -0.4. Overlapping area represents the amount of risk that can be diversified away by holding Shin Kong Financial and Data International Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Data International 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 Data International. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Data International has no effect on the direction of Shin Kong i.e., Shin Kong and Data International go up and down completely randomly.
Pair Corralation between Shin Kong and Data International
Assuming the 90 days trading horizon Shin Kong Financial is expected to generate 0.24 times more return on investment than Data International. However, Shin Kong Financial is 4.17 times less risky than Data International. It trades about 0.03 of its potential returns per unit of risk. Data International Co is currently generating about -0.44 per unit of risk. If you would invest 1,195 in Shin Kong Financial on October 11, 2024 and sell it today you would earn a total of 5.00 from holding Shin Kong Financial or generate 0.42% 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. Data International Co
Performance |
Timeline |
Shin Kong Financial |
Data International |
Shin Kong and Data International Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Shin Kong and Data International
The main advantage of trading using opposite Shin Kong and Data International positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shin Kong position performs unexpectedly, Data International 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 Data International will offset losses from the drop in Data International'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 |
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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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