Correlation Between Taiwan Styrene and Nien Hsing
Can any of the company-specific risk be diversified away by investing in both Taiwan Styrene and Nien Hsing 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 Taiwan Styrene and Nien Hsing into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Taiwan Styrene Monomer and Nien Hsing Textile, you can compare the effects of market volatilities on Taiwan Styrene and Nien Hsing 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 Taiwan Styrene with a short position of Nien Hsing. Check out your portfolio center. Please also check ongoing floating volatility patterns of Taiwan Styrene and Nien Hsing.
Diversification Opportunities for Taiwan Styrene and Nien Hsing
-0.56 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Taiwan and Nien is -0.56. Overlapping area represents the amount of risk that can be diversified away by holding Taiwan Styrene Monomer and Nien Hsing Textile in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Nien Hsing Textile and Taiwan Styrene 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 Taiwan Styrene Monomer are associated (or correlated) with Nien Hsing. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Nien Hsing Textile has no effect on the direction of Taiwan Styrene i.e., Taiwan Styrene and Nien Hsing go up and down completely randomly.
Pair Corralation between Taiwan Styrene and Nien Hsing
Assuming the 90 days trading horizon Taiwan Styrene Monomer is expected to under-perform the Nien Hsing. In addition to that, Taiwan Styrene is 1.56 times more volatile than Nien Hsing Textile. It trades about -0.03 of its total potential returns per unit of risk. Nien Hsing Textile is currently generating about -0.01 per unit of volatility. If you would invest 2,100 in Nien Hsing Textile on September 21, 2024 and sell it today you would lose (100.00) from holding Nien Hsing Textile or give up 4.76% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Taiwan Styrene Monomer vs. Nien Hsing Textile
Performance |
Timeline |
Taiwan Styrene Monomer |
Nien Hsing Textile |
Taiwan Styrene and Nien Hsing Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Taiwan Styrene and Nien Hsing
The main advantage of trading using opposite Taiwan Styrene and Nien Hsing positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Taiwan Styrene position performs unexpectedly, Nien Hsing 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 Nien Hsing will offset losses from the drop in Nien Hsing's long position.Taiwan Styrene vs. Tainan Spinning Co | Taiwan Styrene vs. Lealea Enterprise Co | Taiwan Styrene vs. China Petrochemical Development | Taiwan Styrene vs. Ruentex Development Co |
Nien Hsing vs. Tainan Enterprises Co | Nien Hsing vs. De Licacy Industrial | Nien Hsing vs. Taiwan Styrene Monomer | Nien Hsing vs. Kaulin Mfg |
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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