Correlation Between Chung Hsin and Hsin Kuang
Can any of the company-specific risk be diversified away by investing in both Chung Hsin and Hsin Kuang 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 Chung Hsin and Hsin Kuang into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Chung Hsin Electric Machinery and Hsin Kuang Steel, you can compare the effects of market volatilities on Chung Hsin and Hsin Kuang 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 Chung Hsin with a short position of Hsin Kuang. Check out your portfolio center. Please also check ongoing floating volatility patterns of Chung Hsin and Hsin Kuang.
Diversification Opportunities for Chung Hsin and Hsin Kuang
0.69 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Chung and Hsin is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding Chung Hsin Electric Machinery and Hsin Kuang Steel in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Hsin Kuang Steel and Chung Hsin 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 Chung Hsin Electric Machinery are associated (or correlated) with Hsin Kuang. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Hsin Kuang Steel has no effect on the direction of Chung Hsin i.e., Chung Hsin and Hsin Kuang go up and down completely randomly.
Pair Corralation between Chung Hsin and Hsin Kuang
Assuming the 90 days trading horizon Chung Hsin Electric Machinery is expected to generate 0.81 times more return on investment than Hsin Kuang. However, Chung Hsin Electric Machinery is 1.23 times less risky than Hsin Kuang. It trades about 0.01 of its potential returns per unit of risk. Hsin Kuang Steel is currently generating about -0.28 per unit of risk. If you would invest 15,950 in Chung Hsin Electric Machinery on October 10, 2024 and sell it today you would earn a total of 0.00 from holding Chung Hsin Electric Machinery or generate 0.0% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Chung Hsin Electric Machinery vs. Hsin Kuang Steel
Performance |
Timeline |
Chung Hsin Electric |
Hsin Kuang Steel |
Chung Hsin and Hsin Kuang Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Chung Hsin and Hsin Kuang
The main advantage of trading using opposite Chung Hsin and Hsin Kuang positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Chung Hsin position performs unexpectedly, Hsin Kuang 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 Hsin Kuang will offset losses from the drop in Hsin Kuang's long position.Chung Hsin vs. Hota Industrial Mfg | Chung Hsin vs. Sinbon Electronics Co | Chung Hsin vs. Tong Hsing Electronic | Chung Hsin vs. Flexium Interconnect |
Hsin Kuang vs. Basso Industry Corp | Hsin Kuang vs. Chung Hsin Electric Machinery | Hsin Kuang vs. TECO Electric Machinery |
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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