Correlation Between KL Technology and SPASX Dividend
Can any of the company-specific risk be diversified away by investing in both KL Technology and SPASX Dividend 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 KL Technology and SPASX Dividend into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between KL Technology and SPASX Dividend Opportunities, you can compare the effects of market volatilities on KL Technology and SPASX Dividend 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 KL Technology with a short position of SPASX Dividend. Check out your portfolio center. Please also check ongoing floating volatility patterns of KL Technology and SPASX Dividend.
Diversification Opportunities for KL Technology and SPASX Dividend
0.7 | Correlation Coefficient |
Poor diversification
The 3 months correlation between KLTE and SPASX is 0.7. Overlapping area represents the amount of risk that can be diversified away by holding KL Technology and SPASX Dividend Opportunities in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SPASX Dividend Oppor and KL Technology 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 KL Technology are associated (or correlated) with SPASX Dividend. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of SPASX Dividend Oppor has no effect on the direction of KL Technology i.e., KL Technology and SPASX Dividend go up and down completely randomly.
Pair Corralation between KL Technology and SPASX Dividend
Assuming the 90 days trading horizon KL Technology is expected to under-perform the SPASX Dividend. In addition to that, KL Technology is 2.55 times more volatile than SPASX Dividend Opportunities. It trades about -0.26 of its total potential returns per unit of risk. SPASX Dividend Opportunities is currently generating about 0.0 per unit of volatility. If you would invest 167,470 in SPASX Dividend Opportunities on January 3, 2025 and sell it today you would lose (560.00) from holding SPASX Dividend Opportunities or give up 0.33% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 92.06% |
Values | Daily Returns |
KL Technology vs. SPASX Dividend Opportunities
Performance |
Timeline |
KL Technology and SPASX Dividend Volatility Contrast
Predicted Return Density |
Returns |
KL Technology
Pair trading matchups for KL Technology
SPASX Dividend Opportunities
Pair trading matchups for SPASX Dividend
Pair Trading with KL Technology and SPASX Dividend
The main advantage of trading using opposite KL Technology and SPASX Dividend positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if KL Technology position performs unexpectedly, SPASX Dividend 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 SPASX Dividend will offset losses from the drop in SPASX Dividend's long position.KL Technology vs. Genetec Technology Bhd | KL Technology vs. CPE Technology Berhad | KL Technology vs. Coraza Integrated Technology | KL Technology vs. Cosmos Technology International |
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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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