Correlation Between Small Company and Large Company
Can any of the company-specific risk be diversified away by investing in both Small Company and Large Company 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 Small Company and Large Company into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Small Pany Value and Large Pany Value, you can compare the effects of market volatilities on Small Company and Large Company 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 Small Company with a short position of Large Company. Check out your portfolio center. Please also check ongoing floating volatility patterns of Small Company and Large Company.
Diversification Opportunities for Small Company and Large Company
0.95 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Small and Large is 0.95. Overlapping area represents the amount of risk that can be diversified away by holding Small Pany Value and Large Pany Value in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Large Pany Value and Small Company 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 Small Pany Value are associated (or correlated) with Large Company. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Large Pany Value has no effect on the direction of Small Company i.e., Small Company and Large Company go up and down completely randomly.
Pair Corralation between Small Company and Large Company
Assuming the 90 days horizon Small Pany Value is expected to generate 1.96 times more return on investment than Large Company. However, Small Company is 1.96 times more volatile than Large Pany Value. It trades about 0.12 of its potential returns per unit of risk. Large Pany Value is currently generating about 0.09 per unit of risk. If you would invest 2,836 in Small Pany Value on August 25, 2024 and sell it today you would earn a total of 131.00 from holding Small Pany Value or generate 4.62% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Small Pany Value vs. Large Pany Value
Performance |
| Timeline |
| Small Pany Value |
| Large Pany Value |
Small Company and Large Company Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Small Company and Large Company
The main advantage of trading using opposite Small Company and Large Company positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Small Company position performs unexpectedly, Large Company 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 Large Company will offset losses from the drop in Large Company's long position.| Small Company vs. Small Pany Growth | Small Company vs. Large Pany Value | Small Company vs. Small Pany Value | Small Company vs. Large Pany Growth |
| Large Company vs. Hennessy Nerstone Mid | Large Company vs. Columbia Small Cap | Large Company vs. Queens Road Small | Large Company vs. Great West Loomis Sayles |
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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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