Correlation Between KB Home and Xiaomi
Can any of the company-specific risk be diversified away by investing in both KB Home and Xiaomi 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 KB Home and Xiaomi into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between KB Home and Xiaomi, you can compare the effects of market volatilities on KB Home and Xiaomi 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 KB Home with a short position of Xiaomi. Check out your portfolio center. Please also check ongoing floating volatility patterns of KB Home and Xiaomi.
Diversification Opportunities for KB Home and Xiaomi
Poor diversification
The 3 months correlation between KBH and Xiaomi is 0.78. Overlapping area represents the amount of risk that can be diversified away by holding KB Home and Xiaomi in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Xiaomi and KB Home 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 KB Home are associated (or correlated) with Xiaomi. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Xiaomi has no effect on the direction of KB Home i.e., KB Home and Xiaomi go up and down completely randomly.
Pair Corralation between KB Home and Xiaomi
Assuming the 90 days trading horizon KB Home is expected to under-perform the Xiaomi. But the stock apears to be less risky and, when comparing its historical volatility, KB Home is 2.03 times less risky than Xiaomi. The stock trades about -0.22 of its potential returns per unit of risk. The Xiaomi is currently generating about 0.02 of returns per unit of risk over similar time horizon. If you would invest 7,400 in Xiaomi on September 3, 2024 and sell it today you would earn a total of 0.00 from holding Xiaomi or generate 0.0% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 95.0% |
| Values | Daily Returns |
KB Home vs. Xiaomi
Performance |
| Timeline |
| KB Home |
| Xiaomi |
KB Home and Xiaomi Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with KB Home and Xiaomi
The main advantage of trading using opposite KB Home and Xiaomi positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if KB Home position performs unexpectedly, Xiaomi 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 Xiaomi will offset losses from the drop in Xiaomi's long position.The idea behind KB Home and Xiaomi 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.| Xiaomi vs. GMxico Transportes SAB | Xiaomi vs. First Majestic Silver | Xiaomi vs. UnitedHealth Group Incorporated | Xiaomi vs. Verizon Communications |
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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