Correlation Between Nien Hsing and Shinkong Synthetic
Can any of the company-specific risk be diversified away by investing in both Nien Hsing and Shinkong Synthetic 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 Nien Hsing and Shinkong Synthetic into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Nien Hsing Textile and Shinkong Synthetic Fiber, you can compare the effects of market volatilities on Nien Hsing and Shinkong Synthetic 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 Nien Hsing with a short position of Shinkong Synthetic. Check out your portfolio center. Please also check ongoing floating volatility patterns of Nien Hsing and Shinkong Synthetic.
Diversification Opportunities for Nien Hsing and Shinkong Synthetic
-0.18 | Correlation Coefficient |
Good diversification
The 3 months correlation between Nien and Shinkong is -0.18. Overlapping area represents the amount of risk that can be diversified away by holding Nien Hsing Textile and Shinkong Synthetic Fiber in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shinkong Synthetic Fiber and Nien Hsing 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 Nien Hsing Textile are associated (or correlated) with Shinkong Synthetic. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shinkong Synthetic Fiber has no effect on the direction of Nien Hsing i.e., Nien Hsing and Shinkong Synthetic go up and down completely randomly.
Pair Corralation between Nien Hsing and Shinkong Synthetic
Assuming the 90 days trading horizon Nien Hsing Textile is expected to generate 0.43 times more return on investment than Shinkong Synthetic. However, Nien Hsing Textile is 2.31 times less risky than Shinkong Synthetic. It trades about 0.03 of its potential returns per unit of risk. Shinkong Synthetic Fiber is currently generating about -0.01 per unit of risk. If you would invest 2,035 in Nien Hsing Textile on September 2, 2024 and sell it today you would earn a total of 45.00 from holding Nien Hsing Textile or generate 2.21% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Nien Hsing Textile vs. Shinkong Synthetic Fiber
Performance |
Timeline |
Nien Hsing Textile |
Shinkong Synthetic Fiber |
Nien Hsing and Shinkong Synthetic Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Nien Hsing and Shinkong Synthetic
The main advantage of trading using opposite Nien Hsing and Shinkong Synthetic positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Nien Hsing position performs unexpectedly, Shinkong Synthetic 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 Shinkong Synthetic will offset losses from the drop in Shinkong Synthetic's long position.Nien Hsing vs. Chaintech Technology Corp | Nien Hsing vs. Avision | Nien Hsing vs. Clevo Co | Nien Hsing vs. Elitegroup Computer Systems |
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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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