Correlation Between StarTek and Innodata
Can any of the company-specific risk be diversified away by investing in both StarTek and Innodata 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 StarTek and Innodata into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between StarTek and Innodata, you can compare the effects of market volatilities on StarTek and Innodata 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 StarTek with a short position of Innodata. Check out your portfolio center. Please also check ongoing floating volatility patterns of StarTek and Innodata.
Diversification Opportunities for StarTek and Innodata
Good diversification
The 3 months correlation between StarTek and Innodata is -0.19. Overlapping area represents the amount of risk that can be diversified away by holding StarTek and Innodata in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Innodata and StarTek 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 StarTek are associated (or correlated) with Innodata. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Innodata has no effect on the direction of StarTek i.e., StarTek and Innodata go up and down completely randomly.
Pair Corralation between StarTek and Innodata
Considering the 90-day investment horizon StarTek is expected to generate 0.62 times more return on investment than Innodata. However, StarTek is 1.61 times less risky than Innodata. It trades about 0.21 of its potential returns per unit of risk. Innodata is currently generating about 0.09 per unit of risk. If you would invest 284.00 in StarTek on August 28, 2024 and sell it today you would earn a total of 38.00 from holding StarTek or generate 13.38% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 3.95% |
| Values | Daily Returns |
StarTek vs. Innodata
Performance |
| Timeline |
| StarTek |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
| Innodata |
StarTek and Innodata Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with StarTek and Innodata
The main advantage of trading using opposite StarTek and Innodata positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if StarTek position performs unexpectedly, Innodata 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 Innodata will offset losses from the drop in Innodata's long position.The idea behind StarTek and Innodata pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.| Innodata vs. ASGN Inc | Innodata vs. Formula Systems 1985 | Innodata vs. FiscalNote Holdings | Innodata vs. International Business Machines |
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 Headlines Timeline module to stay connected to all market stories and filter out noise. Drill down to analyze hype elasticity.
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