Correlation Between SFP Tech and KL Technology

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Can any of the company-specific risk be diversified away by investing in both SFP Tech and KL Technology 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 SFP Tech and KL Technology into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SFP Tech Holdings and KL Technology, you can compare the effects of market volatilities on SFP Tech and KL Technology 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 SFP Tech with a short position of KL Technology. Check out your portfolio center. Please also check ongoing floating volatility patterns of SFP Tech and KL Technology.

Diversification Opportunities for SFP Tech and KL Technology

0.74
  Correlation Coefficient

Poor diversification

The 3 months correlation between SFP and KLTE is 0.74. Overlapping area represents the amount of risk that can be diversified away by holding SFP Tech Holdings and KL Technology in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on KL Technology and SFP Tech 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 SFP Tech Holdings are associated (or correlated) with KL Technology. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of KL Technology has no effect on the direction of SFP Tech i.e., SFP Tech and KL Technology go up and down completely randomly.
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Pair Corralation between SFP Tech and KL Technology

Assuming the 90 days trading horizon SFP Tech Holdings is expected to generate 1.73 times more return on investment than KL Technology. However, SFP Tech is 1.73 times more volatile than KL Technology. It trades about 0.1 of its potential returns per unit of risk. KL Technology is currently generating about 0.14 per unit of risk. If you would invest  66.00  in SFP Tech Holdings on September 29, 2024 and sell it today you would earn a total of  9.00  from holding SFP Tech Holdings or generate 13.64% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthSignificant
Accuracy100.0%
ValuesDaily Returns

SFP Tech Holdings  vs.  KL Technology

 Performance 
       Timeline  

SFP Tech and KL Technology Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with SFP Tech and KL Technology

The main advantage of trading using opposite SFP Tech and KL Technology positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SFP Tech position performs unexpectedly, KL Technology 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 KL Technology will offset losses from the drop in KL Technology's long position.
The idea behind SFP Tech Holdings and KL Technology pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.
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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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.

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