Correlation Between Himalaya Shipping and WK Kellogg
Can any of the company-specific risk be diversified away by investing in both Himalaya Shipping and WK Kellogg 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 Himalaya Shipping and WK Kellogg into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Himalaya Shipping and WK Kellogg Co, you can compare the effects of market volatilities on Himalaya Shipping and WK Kellogg 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 Himalaya Shipping with a short position of WK Kellogg. Check out your portfolio center. Please also check ongoing floating volatility patterns of Himalaya Shipping and WK Kellogg.
Diversification Opportunities for Himalaya Shipping and WK Kellogg
-0.05 | Correlation Coefficient |
Good diversification
The 3 months correlation between Himalaya and KLG is -0.05. Overlapping area represents the amount of risk that can be diversified away by holding Himalaya Shipping and WK Kellogg Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on WK Kellogg and Himalaya Shipping 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 Himalaya Shipping are associated (or correlated) with WK Kellogg. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of WK Kellogg has no effect on the direction of Himalaya Shipping i.e., Himalaya Shipping and WK Kellogg go up and down completely randomly.
Pair Corralation between Himalaya Shipping and WK Kellogg
Given the investment horizon of 90 days Himalaya Shipping is expected to under-perform the WK Kellogg. But the stock apears to be less risky and, when comparing its historical volatility, Himalaya Shipping is 1.1 times less risky than WK Kellogg. The stock trades about -0.07 of its potential returns per unit of risk. The WK Kellogg Co is currently generating about -0.01 of returns per unit of risk over similar time horizon. If you would invest 1,930 in WK Kellogg Co on October 25, 2024 and sell it today you would lose (244.00) from holding WK Kellogg Co or give up 12.64% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Himalaya Shipping vs. WK Kellogg Co
Performance |
Timeline |
Himalaya Shipping |
WK Kellogg |
Himalaya Shipping and WK Kellogg Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Himalaya Shipping and WK Kellogg
The main advantage of trading using opposite Himalaya Shipping and WK Kellogg positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Himalaya Shipping position performs unexpectedly, WK Kellogg 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 WK Kellogg will offset losses from the drop in WK Kellogg's long position.Himalaya Shipping vs. 51Talk Online Education | Himalaya Shipping vs. Space Communication | Himalaya Shipping vs. Radcom | Himalaya Shipping vs. Playtika Holding Corp |
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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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