Correlation Between Fukuyama Transporting and Saia
Can any of the company-specific risk be diversified away by investing in both Fukuyama Transporting and Saia 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 Fukuyama Transporting and Saia into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Fukuyama Transporting Co and Saia Inc, you can compare the effects of market volatilities on Fukuyama Transporting and Saia 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 Fukuyama Transporting with a short position of Saia. Check out your portfolio center. Please also check ongoing floating volatility patterns of Fukuyama Transporting and Saia.
Diversification Opportunities for Fukuyama Transporting and Saia
-0.66 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Fukuyama and Saia is -0.66. Overlapping area represents the amount of risk that can be diversified away by holding Fukuyama Transporting Co and Saia Inc in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Saia Inc and Fukuyama Transporting 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 Fukuyama Transporting Co are associated (or correlated) with Saia. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Saia Inc has no effect on the direction of Fukuyama Transporting i.e., Fukuyama Transporting and Saia go up and down completely randomly.
Pair Corralation between Fukuyama Transporting and Saia
Assuming the 90 days horizon Fukuyama Transporting is expected to generate 1.89 times less return on investment than Saia. But when comparing it to its historical volatility, Fukuyama Transporting Co is 1.44 times less risky than Saia. It trades about 0.05 of its potential returns per unit of risk. Saia Inc is currently generating about 0.07 of returns per unit of risk over similar time horizon. If you would invest 35,800 in Saia Inc on September 4, 2024 and sell it today you would earn a total of 17,700 from holding Saia Inc or generate 49.44% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Fukuyama Transporting Co vs. Saia Inc
Performance |
| Timeline |
| Fukuyama Transporting |
| Saia Inc |
Fukuyama Transporting and Saia Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Fukuyama Transporting and Saia
The main advantage of trading using opposite Fukuyama Transporting and Saia positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Fukuyama Transporting position performs unexpectedly, Saia 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 Saia will offset losses from the drop in Saia's long position.| Fukuyama Transporting vs. Old Dominion Freight | Fukuyama Transporting vs. Saia Inc | Fukuyama Transporting vs. SCHNEIDER NATLINC CLB | Fukuyama Transporting vs. Seino Holdings Co |
| Saia vs. Old Dominion Freight | Saia vs. SCHNEIDER NATLINC CLB | Saia vs. Seino Holdings Co | Saia vs. Fukuyama Transporting Co |
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 Insider Screener module to find insiders across different sectors to evaluate their impact on performance.
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