Correlation Between Power Finance and Samhi Hotels
Can any of the company-specific risk be diversified away by investing in both Power Finance and Samhi Hotels 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 Power Finance and Samhi Hotels into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Power Finance and Samhi Hotels Limited, you can compare the effects of market volatilities on Power Finance and Samhi Hotels 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 Power Finance with a short position of Samhi Hotels. Check out your portfolio center. Please also check ongoing floating volatility patterns of Power Finance and Samhi Hotels.
Diversification Opportunities for Power Finance and Samhi Hotels
0.76 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Power and Samhi is 0.76. Overlapping area represents the amount of risk that can be diversified away by holding Power Finance and Samhi Hotels Limited in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Samhi Hotels Limited and Power Finance 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 Power Finance are associated (or correlated) with Samhi Hotels. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Samhi Hotels Limited has no effect on the direction of Power Finance i.e., Power Finance and Samhi Hotels go up and down completely randomly.
Pair Corralation between Power Finance and Samhi Hotels
Assuming the 90 days trading horizon Power Finance is expected to generate 1.47 times more return on investment than Samhi Hotels. However, Power Finance is 1.47 times more volatile than Samhi Hotels Limited. It trades about 0.15 of its potential returns per unit of risk. Samhi Hotels Limited is currently generating about 0.04 per unit of risk. If you would invest 44,735 in Power Finance on August 28, 2024 and sell it today you would earn a total of 3,705 from holding Power Finance or generate 8.28% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 95.24% |
Values | Daily Returns |
Power Finance vs. Samhi Hotels Limited
Performance |
Timeline |
Power Finance |
Samhi Hotels Limited |
Power Finance and Samhi Hotels Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Power Finance and Samhi Hotels
The main advantage of trading using opposite Power Finance and Samhi Hotels positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Power Finance position performs unexpectedly, Samhi Hotels 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 Samhi Hotels will offset losses from the drop in Samhi Hotels' long position.Power Finance vs. Shemaroo Entertainment Limited | Power Finance vs. Hindustan Media Ventures | Power Finance vs. JGCHEMICALS LIMITED | Power Finance vs. Thirumalai Chemicals Limited |
Samhi Hotels vs. MMTC Limited | Samhi Hotels vs. Kingfa Science Technology | Samhi Hotels vs. Rico Auto Industries | Samhi Hotels vs. GACM Technologies Limited |
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 Price Transformation module to use Price Transformation models to analyze the depth of different equity instruments across global markets.
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