Correlation Between Dan Hotels and Blender Financial
Can any of the company-specific risk be diversified away by investing in both Dan Hotels and Blender Financial 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 Dan Hotels and Blender Financial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Dan Hotels and Blender Financial Technologies, you can compare the effects of market volatilities on Dan Hotels and Blender Financial 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 Dan Hotels with a short position of Blender Financial. Check out your portfolio center. Please also check ongoing floating volatility patterns of Dan Hotels and Blender Financial.
Diversification Opportunities for Dan Hotels and Blender Financial
0.89 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Dan and Blender is 0.89. Overlapping area represents the amount of risk that can be diversified away by holding Dan Hotels and Blender Financial Technologies in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Blender Financial and Dan Hotels 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 Dan Hotels are associated (or correlated) with Blender Financial. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Blender Financial has no effect on the direction of Dan Hotels i.e., Dan Hotels and Blender Financial go up and down completely randomly.
Pair Corralation between Dan Hotels and Blender Financial
Assuming the 90 days trading horizon Dan Hotels is expected to generate 0.5 times more return on investment than Blender Financial. However, Dan Hotels is 2.01 times less risky than Blender Financial. It trades about -0.1 of its potential returns per unit of risk. Blender Financial Technologies is currently generating about -0.14 per unit of risk. If you would invest 231,800 in Dan Hotels on September 1, 2024 and sell it today you would lose (6,800) from holding Dan Hotels or give up 2.93% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Dan Hotels vs. Blender Financial Technologies
Performance |
| Timeline |
| Dan Hotels |
| Blender Financial |
Dan Hotels and Blender Financial Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Dan Hotels and Blender Financial
The main advantage of trading using opposite Dan Hotels and Blender Financial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dan Hotels position performs unexpectedly, Blender Financial 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 Blender Financial will offset losses from the drop in Blender Financial's long position.| Dan Hotels vs. El Al Israel | Dan Hotels vs. Magic Software Enterprises | Dan Hotels vs. Neto Malinda | Dan Hotels vs. Orbit Technologies |
| Blender Financial vs. Isracard | Blender Financial vs. Jacob Finance And | Blender Financial vs. Bull Trading And |
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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