Correlation Between Arrow Financial and Kaltura
Can any of the company-specific risk be diversified away by investing in both Arrow Financial and Kaltura 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 Arrow Financial and Kaltura into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Arrow Financial and Kaltura, you can compare the effects of market volatilities on Arrow Financial and Kaltura 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 Arrow Financial with a short position of Kaltura. Check out your portfolio center. Please also check ongoing floating volatility patterns of Arrow Financial and Kaltura.
Diversification Opportunities for Arrow Financial and Kaltura
-0.4 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Arrow and Kaltura is -0.4. Overlapping area represents the amount of risk that can be diversified away by holding Arrow Financial and Kaltura in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kaltura and Arrow Financial 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 Arrow Financial are associated (or correlated) with Kaltura. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kaltura has no effect on the direction of Arrow Financial i.e., Arrow Financial and Kaltura go up and down completely randomly.
Pair Corralation between Arrow Financial and Kaltura
Given the investment horizon of 90 days Arrow Financial is expected to under-perform the Kaltura. But the stock apears to be less risky and, when comparing its historical volatility, Arrow Financial is 3.06 times less risky than Kaltura. The stock trades about -0.07 of its potential returns per unit of risk. The Kaltura is currently generating about -0.02 of returns per unit of risk over similar time horizon. If you would invest 230.00 in Kaltura on November 29, 2024 and sell it today you would lose (11.00) from holding Kaltura or give up 4.78% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Arrow Financial vs. Kaltura
Performance |
| Timeline |
| Arrow Financial |
| Kaltura |
Arrow Financial and Kaltura Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Arrow Financial and Kaltura
The main advantage of trading using opposite Arrow Financial and Kaltura positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Arrow Financial position performs unexpectedly, Kaltura 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 Kaltura will offset losses from the drop in Kaltura's long position.| Arrow Financial vs. Heritage Commerce Corp | Arrow Financial vs. Westamerica Bancorporation | Arrow Financial vs. Heritage Financial | Arrow Financial vs. National Bankshares |
| Kaltura vs. Evertec | Kaltura vs. Consensus Cloud Solutions | Kaltura vs. Global Blue Group | Kaltura vs. Lesaka Technologies |
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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