Correlation Between Kosdaq Composite and Settlebank

Can any of the company-specific risk be diversified away by investing in both Kosdaq Composite and Settlebank 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 Kosdaq Composite and Settlebank into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between Kosdaq Composite Index and Settlebank, you can compare the effects of market volatilities on Kosdaq Composite and Settlebank 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 Kosdaq Composite with a short position of Settlebank. Check out your portfolio center. Please also check ongoing floating volatility patterns of Kosdaq Composite and Settlebank.

Diversification Opportunities for Kosdaq Composite and Settlebank

0.52

Correlation Coefficient

Kosdaq Composite  Settlebank

Very weak diversification

The 3 months correlation between Kosdaq and Settlebank is 0.52. Overlapping area represents the amount of risk that can be diversified away by holding Kosdaq Composite Index and Settlebank in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Settlebank and Kosdaq Composite 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 Kosdaq Composite Index are associated (or correlated) with Settlebank. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Settlebank has no effect on the direction of Kosdaq Composite i.e., Kosdaq Composite and Settlebank go up and down completely randomly.

Pair Corralation between Kosdaq Composite and Settlebank

Assuming the 90 days trading horizon Kosdaq Composite Index is expected to generate 0.59 times more return on investment than Settlebank. However, Kosdaq Composite Index is 1.69 times less risky than Settlebank. It trades about -0.13 of its potential returns per unit of risk. Settlebank is currently generating about -0.1 per unit of risk. If you would invest  76,921  in Kosdaq Composite Index on August 31, 2024 and sell it today you would lose (8,872) from holding Kosdaq Composite Index or give up 11.53% of portfolio value over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Weak
Accuracy	98.31%
Values	Daily Returns

Kosdaq Composite Index  vs.  Settlebank

Pair Trading with Kosdaq Composite and Settlebank

The main advantage of trading using opposite Kosdaq Composite and Settlebank positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Kosdaq Composite position performs unexpectedly, Settlebank 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 Settlebank will offset losses from the drop in Settlebank's long position.

The idea behind Kosdaq Composite Index and Settlebank 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.