Correlation Between Shih Kuen and Baotek Industrial
Can any of the company-specific risk be diversified away by investing in both Shih Kuen and Baotek Industrial 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 Shih Kuen and Baotek Industrial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Shih Kuen Plastics and Baotek Industrial Materials, you can compare the effects of market volatilities on Shih Kuen and Baotek Industrial 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 Shih Kuen with a short position of Baotek Industrial. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shih Kuen and Baotek Industrial.
Diversification Opportunities for Shih Kuen and Baotek Industrial
0.69 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Shih and Baotek is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding Shih Kuen Plastics and Baotek Industrial Materials in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Baotek Industrial and Shih Kuen 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 Shih Kuen Plastics are associated (or correlated) with Baotek Industrial. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Baotek Industrial has no effect on the direction of Shih Kuen i.e., Shih Kuen and Baotek Industrial go up and down completely randomly.
Pair Corralation between Shih Kuen and Baotek Industrial
Assuming the 90 days trading horizon Shih Kuen Plastics is expected to under-perform the Baotek Industrial. But the stock apears to be less risky and, when comparing its historical volatility, Shih Kuen Plastics is 1.9 times less risky than Baotek Industrial. The stock trades about -0.17 of its potential returns per unit of risk. The Baotek Industrial Materials is currently generating about -0.05 of returns per unit of risk over similar time horizon. If you would invest 4,800 in Baotek Industrial Materials on August 30, 2024 and sell it today you would lose (315.00) from holding Baotek Industrial Materials or give up 6.56% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Shih Kuen Plastics vs. Baotek Industrial Materials
Performance |
Timeline |
Shih Kuen Plastics |
Baotek Industrial |
Shih Kuen and Baotek Industrial Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Shih Kuen and Baotek Industrial
The main advantage of trading using opposite Shih Kuen and Baotek Industrial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shih Kuen position performs unexpectedly, Baotek Industrial 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 Baotek Industrial will offset losses from the drop in Baotek Industrial's long position.Shih Kuen vs. Chain Chon Industrial | Shih Kuen vs. Camellia Metal Co | Shih Kuen vs. Sunspring Metal Corp | Shih Kuen vs. Insyde Software |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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