Correlation Between Hyundai and Alliance Data
Can any of the company-specific risk be diversified away by investing in both Hyundai and Alliance Data 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 Hyundai and Alliance Data into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Hyundai Motor and Alliance Data Systems, you can compare the effects of market volatilities on Hyundai and Alliance Data 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 Hyundai with a short position of Alliance Data. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hyundai and Alliance Data.
Diversification Opportunities for Hyundai and Alliance Data
-0.5 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Hyundai and Alliance is -0.5. Overlapping area represents the amount of risk that can be diversified away by holding Hyundai Motor and Alliance Data Systems in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Alliance Data Systems and Hyundai 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 Hyundai Motor are associated (or correlated) with Alliance Data. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Alliance Data Systems has no effect on the direction of Hyundai i.e., Hyundai and Alliance Data go up and down completely randomly.
Pair Corralation between Hyundai and Alliance Data
Assuming the 90 days trading horizon Hyundai Motor is expected to under-perform the Alliance Data. But the stock apears to be less risky and, when comparing its historical volatility, Hyundai Motor is 1.66 times less risky than Alliance Data. The stock trades about -0.1 of its potential returns per unit of risk. The Alliance Data Systems is currently generating about 0.2 of returns per unit of risk over similar time horizon. If you would invest 4,896 in Alliance Data Systems on August 26, 2024 and sell it today you would earn a total of 933.00 from holding Alliance Data Systems or generate 19.06% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Hyundai Motor vs. Alliance Data Systems
Performance |
| Timeline |
| Hyundai Motor |
| Alliance Data Systems |
Hyundai and Alliance Data Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Hyundai and Alliance Data
The main advantage of trading using opposite Hyundai and Alliance Data positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hyundai position performs unexpectedly, Alliance Data 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 Alliance Data will offset losses from the drop in Alliance Data's long position.| Hyundai vs. MG Plc | Hyundai vs. Admiral Group PLC | Hyundai vs. Anglo American PLC | Hyundai vs. Vodafone Group PLC |
| Alliance Data vs. Samsung Electronics Co | Alliance Data vs. Samsung Electronics Co | Alliance Data vs. Hyundai Motor | Alliance Data vs. Toyota Motor Corp |
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 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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