Correlation Between Nan Ya and DingZing Advanced
Can any of the company-specific risk be diversified away by investing in both Nan Ya and DingZing Advanced 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 Nan Ya and DingZing Advanced into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Nan Ya Plastics and DingZing Advanced Materials, you can compare the effects of market volatilities on Nan Ya and DingZing Advanced 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 Nan Ya with a short position of DingZing Advanced. Check out your portfolio center. Please also check ongoing floating volatility patterns of Nan Ya and DingZing Advanced.
Diversification Opportunities for Nan Ya and DingZing Advanced
0.45 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Nan and DingZing is 0.45. Overlapping area represents the amount of risk that can be diversified away by holding Nan Ya Plastics and DingZing Advanced Materials in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on DingZing Advanced and Nan Ya 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 Nan Ya Plastics are associated (or correlated) with DingZing Advanced. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of DingZing Advanced has no effect on the direction of Nan Ya i.e., Nan Ya and DingZing Advanced go up and down completely randomly.
Pair Corralation between Nan Ya and DingZing Advanced
Assuming the 90 days trading horizon Nan Ya Plastics is expected to under-perform the DingZing Advanced. But the stock apears to be less risky and, when comparing its historical volatility, Nan Ya Plastics is 1.9 times less risky than DingZing Advanced. The stock trades about -0.18 of its potential returns per unit of risk. The DingZing Advanced Materials is currently generating about 0.03 of returns per unit of risk over similar time horizon. If you would invest 13,098 in DingZing Advanced Materials on September 28, 2024 and sell it today you would earn a total of 1,302 from holding DingZing Advanced Materials or generate 9.94% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Nan Ya Plastics vs. DingZing Advanced Materials
Performance |
Timeline |
Nan Ya Plastics |
DingZing Advanced |
Nan Ya and DingZing Advanced Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Nan Ya and DingZing Advanced
The main advantage of trading using opposite Nan Ya and DingZing Advanced positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Nan Ya position performs unexpectedly, DingZing Advanced 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 DingZing Advanced will offset losses from the drop in DingZing Advanced's long position.Nan Ya vs. Formosa Plastics Corp | Nan Ya vs. Formosa Chemicals Fibre | Nan Ya vs. China Steel Corp | Nan Ya vs. Formosa Petrochemical Corp |
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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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