Correlation Between KCE Electronics and Singer Thailand
Can any of the company-specific risk be diversified away by investing in both KCE Electronics and Singer Thailand 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 KCE Electronics and Singer Thailand into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between KCE Electronics Public and Singer Thailand Public, you can compare the effects of market volatilities on KCE Electronics and Singer Thailand 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 KCE Electronics with a short position of Singer Thailand. Check out your portfolio center. Please also check ongoing floating volatility patterns of KCE Electronics and Singer Thailand.
Diversification Opportunities for KCE Electronics and Singer Thailand
0.21 | Correlation Coefficient |
Modest diversification
The 3 months correlation between KCE and Singer is 0.21. Overlapping area represents the amount of risk that can be diversified away by holding KCE Electronics Public and Singer Thailand Public in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Singer Thailand Public and KCE Electronics 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 KCE Electronics Public are associated (or correlated) with Singer Thailand. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Singer Thailand Public has no effect on the direction of KCE Electronics i.e., KCE Electronics and Singer Thailand go up and down completely randomly.
Pair Corralation between KCE Electronics and Singer Thailand
Assuming the 90 days trading horizon KCE Electronics Public is expected to under-perform the Singer Thailand. In addition to that, KCE Electronics is 1.47 times more volatile than Singer Thailand Public. It trades about -0.38 of its total potential returns per unit of risk. Singer Thailand Public is currently generating about -0.31 per unit of volatility. If you would invest 1,090 in Singer Thailand Public on August 25, 2024 and sell it today you would lose (185.00) from holding Singer Thailand Public or give up 16.97% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
KCE Electronics Public vs. Singer Thailand Public
Performance |
Timeline |
KCE Electronics Public |
Singer Thailand Public |
KCE Electronics and Singer Thailand Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with KCE Electronics and Singer Thailand
The main advantage of trading using opposite KCE Electronics and Singer Thailand positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if KCE Electronics position performs unexpectedly, Singer Thailand 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 Singer Thailand will offset losses from the drop in Singer Thailand's long position.KCE Electronics vs. AP Public | KCE Electronics vs. Jasmine International Public | KCE Electronics vs. Asia Plus Group | KCE Electronics vs. Bangkok Aviation Fuel |
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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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