Correlation Between Feng Hsin and Kenmec Mechanical
Can any of the company-specific risk be diversified away by investing in both Feng Hsin and Kenmec Mechanical 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 Feng Hsin and Kenmec Mechanical into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Feng Hsin Steel and Kenmec Mechanical Engineering, you can compare the effects of market volatilities on Feng Hsin and Kenmec Mechanical 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 Feng Hsin with a short position of Kenmec Mechanical. Check out your portfolio center. Please also check ongoing floating volatility patterns of Feng Hsin and Kenmec Mechanical.
Diversification Opportunities for Feng Hsin and Kenmec Mechanical
0.81 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Feng and Kenmec is 0.81. Overlapping area represents the amount of risk that can be diversified away by holding Feng Hsin Steel and Kenmec Mechanical Engineering in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kenmec Mechanical and Feng 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 Feng Hsin Steel are associated (or correlated) with Kenmec Mechanical. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kenmec Mechanical has no effect on the direction of Feng Hsin i.e., Feng Hsin and Kenmec Mechanical go up and down completely randomly.
Pair Corralation between Feng Hsin and Kenmec Mechanical
Assuming the 90 days trading horizon Feng Hsin Steel is expected to under-perform the Kenmec Mechanical. But the stock apears to be less risky and, when comparing its historical volatility, Feng Hsin Steel is 1.38 times less risky than Kenmec Mechanical. The stock trades about -0.13 of its potential returns per unit of risk. The Kenmec Mechanical Engineering is currently generating about -0.04 of returns per unit of risk over similar time horizon. If you would invest 9,040 in Kenmec Mechanical Engineering on September 15, 2024 and sell it today you would lose (580.00) from holding Kenmec Mechanical Engineering or give up 6.42% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Feng Hsin Steel vs. Kenmec Mechanical Engineering
Performance |
Timeline |
Feng Hsin Steel |
Kenmec Mechanical |
Feng Hsin and Kenmec Mechanical Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Feng Hsin and Kenmec Mechanical
The main advantage of trading using opposite Feng Hsin and Kenmec Mechanical positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Feng Hsin position performs unexpectedly, Kenmec Mechanical 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 Kenmec Mechanical will offset losses from the drop in Kenmec Mechanical's long position.Feng Hsin vs. Tainan Spinning Co | Feng Hsin vs. Lealea Enterprise Co | Feng Hsin vs. China Petrochemical Development | Feng Hsin vs. Ruentex Development Co |
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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 Headlines Timeline module to stay connected to all market stories and filter out noise. Drill down to analyze hype elasticity.
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