Correlation Between Stock Exchange and Applied DB

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

By analyzing existing cross correlation between Stock Exchange Of and Applied DB Public, you can compare the effects of market volatilities on Stock Exchange and Applied DB 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 Stock Exchange with a short position of Applied DB. Check out your portfolio center. Please also check ongoing floating volatility patterns of Stock Exchange and Applied DB.

Diversification Opportunities for Stock Exchange and Applied DB

0.28

Correlation Coefficient

Stock Exchange  Applied DB

Modest diversification

The 3 months correlation between Stock and Applied is 0.28. Overlapping area represents the amount of risk that can be diversified away by holding Stock Exchange Of and Applied DB Public in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Applied DB Public and Stock Exchange 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 Stock Exchange Of are associated (or correlated) with Applied DB. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Applied DB Public has no effect on the direction of Stock Exchange i.e., Stock Exchange and Applied DB go up and down completely randomly.

Pair Corralation between Stock Exchange and Applied DB

Assuming the 90 days trading horizon Stock Exchange Of is expected to under-perform the Applied DB. But the index apears to be less risky and, when comparing its historical volatility, Stock Exchange Of is 6.25 times less risky than Applied DB. The index trades about -0.08 of its potential returns per unit of risk. The Applied DB Public is currently generating about -0.01 of returns per unit of risk over similar time horizon. If you would invest  96.00  in Applied DB Public on September 27, 2024 and sell it today you would lose (8.00) from holding Applied DB Public or give up 8.33% of portfolio value over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Very Weak
Accuracy	100.0%
Values	Daily Returns

Stock Exchange Of  vs.  Applied DB Public

Pair Trading with Stock Exchange and Applied DB

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

The idea behind Stock Exchange Of and Applied DB Public 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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.