Correlation Between Salesforce and Saigon Machinery
Can any of the company-specific risk be diversified away by investing in both Salesforce and Saigon Machinery 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 Salesforce and Saigon Machinery into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Salesforce and Saigon Machinery Spare, you can compare the effects of market volatilities on Salesforce and Saigon Machinery 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 Salesforce with a short position of Saigon Machinery. Check out your portfolio center. Please also check ongoing floating volatility patterns of Salesforce and Saigon Machinery.
Diversification Opportunities for Salesforce and Saigon Machinery
-0.05 | Correlation Coefficient |
Good diversification
The 3 months correlation between Salesforce and Saigon is -0.05. Overlapping area represents the amount of risk that can be diversified away by holding Salesforce and Saigon Machinery Spare in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Saigon Machinery Spare and Salesforce 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 Salesforce are associated (or correlated) with Saigon Machinery. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Saigon Machinery Spare has no effect on the direction of Salesforce i.e., Salesforce and Saigon Machinery go up and down completely randomly.
Pair Corralation between Salesforce and Saigon Machinery
Considering the 90-day investment horizon Salesforce is expected to generate 7.13 times less return on investment than Saigon Machinery. But when comparing it to its historical volatility, Salesforce is 1.75 times less risky than Saigon Machinery. It trades about 0.13 of its potential returns per unit of risk. Saigon Machinery Spare is currently generating about 0.54 of returns per unit of risk over similar time horizon. If you would invest 1,475,000 in Saigon Machinery Spare on October 30, 2024 and sell it today you would earn a total of 125,000 from holding Saigon Machinery Spare or generate 8.47% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 33.33% |
| Values | Daily Returns |
Salesforce vs. Saigon Machinery Spare
Performance |
| Timeline |
| Salesforce |
| Saigon Machinery Spare |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Market Crasher
Salesforce and Saigon Machinery Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Salesforce and Saigon Machinery
The main advantage of trading using opposite Salesforce and Saigon Machinery positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Salesforce position performs unexpectedly, Saigon Machinery 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 Saigon Machinery will offset losses from the drop in Saigon Machinery's long position.| Salesforce vs. Zoom Video Communications | Salesforce vs. C3 Ai Inc | Salesforce vs. Shopify | Salesforce vs. Workday |
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 Equity Valuation module to check real value of public entities based on technical and fundamental data.
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