Correlation Between Taiwan Styrene and Taita Chemical
Can any of the company-specific risk be diversified away by investing in both Taiwan Styrene and Taita Chemical 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 Taita Chemical 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 Taita Chemical Co, you can compare the effects of market volatilities on Taiwan Styrene and Taita Chemical 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 Taita Chemical. Check out your portfolio center. Please also check ongoing floating volatility patterns of Taiwan Styrene and Taita Chemical.
Diversification Opportunities for Taiwan Styrene and Taita Chemical
0.66 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Taiwan and Taita is 0.66. Overlapping area represents the amount of risk that can be diversified away by holding Taiwan Styrene Monomer and Taita Chemical Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Taita Chemical 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 Taita Chemical. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Taita Chemical has no effect on the direction of Taiwan Styrene i.e., Taiwan Styrene and Taita Chemical go up and down completely randomly.
Pair Corralation between Taiwan Styrene and Taita Chemical
Assuming the 90 days trading horizon Taiwan Styrene Monomer is expected to under-perform the Taita Chemical. But the stock apears to be less risky and, when comparing its historical volatility, Taiwan Styrene Monomer is 1.96 times less risky than Taita Chemical. The stock trades about -0.07 of its potential returns per unit of risk. The Taita Chemical Co is currently generating about 0.02 of returns per unit of risk over similar time horizon. If you would invest 1,565 in Taita Chemical Co on August 31, 2024 and sell it today you would earn a total of 50.00 from holding Taita Chemical Co or generate 3.19% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Taiwan Styrene Monomer vs. Taita Chemical Co
Performance |
Timeline |
Taiwan Styrene Monomer |
Taita Chemical |
Taiwan Styrene and Taita Chemical Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Taiwan Styrene and Taita Chemical
The main advantage of trading using opposite Taiwan Styrene and Taita Chemical positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Taiwan Styrene position performs unexpectedly, Taita Chemical 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 Taita Chemical will offset losses from the drop in Taita Chemical's long position.Taiwan Styrene vs. Basso Industry Corp | Taiwan Styrene vs. Chung Hsin Electric Machinery | Taiwan Styrene vs. TYC Brother Industrial | Taiwan Styrene vs. TECO Electric Machinery |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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