Correlation Between LG Display and Fast Retailing
Can any of the company-specific risk be diversified away by investing in both LG Display and Fast Retailing 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 LG Display and Fast Retailing into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between LG Display Co and Fast Retailing Co, you can compare the effects of market volatilities on LG Display and Fast Retailing 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 LG Display with a short position of Fast Retailing. Check out your portfolio center. Please also check ongoing floating volatility patterns of LG Display and Fast Retailing.
Diversification Opportunities for LG Display and Fast Retailing
-0.45 | Correlation Coefficient |
Very good diversification
The 3 months correlation between LGA and Fast is -0.45. Overlapping area represents the amount of risk that can be diversified away by holding LG Display Co and Fast Retailing Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Fast Retailing and LG Display 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 LG Display Co are associated (or correlated) with Fast Retailing. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Fast Retailing has no effect on the direction of LG Display i.e., LG Display and Fast Retailing go up and down completely randomly.
Pair Corralation between LG Display and Fast Retailing
Assuming the 90 days horizon LG Display Co is expected to generate 1.0 times more return on investment than Fast Retailing. However, LG Display Co is 1.0 times less risky than Fast Retailing. It trades about 0.11 of its potential returns per unit of risk. Fast Retailing Co is currently generating about -0.03 per unit of risk. If you would invest 248.00 in LG Display Co on April 25, 2025 and sell it today you would earn a total of 34.00 from holding LG Display Co or generate 13.71% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
LG Display Co vs. Fast Retailing Co
Performance |
Timeline |
LG Display |
Fast Retailing |
LG Display and Fast Retailing Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with LG Display and Fast Retailing
The main advantage of trading using opposite LG Display and Fast Retailing positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if LG Display position performs unexpectedly, Fast Retailing 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 Fast Retailing will offset losses from the drop in Fast Retailing's long position.LG Display vs. Monster Beverage Corp | LG Display vs. CAL MAINE FOODS | LG Display vs. Moneysupermarket Group PLC | LG Display vs. Collins Foods Limited |
Fast Retailing vs. Applied Materials | Fast Retailing vs. WANDA HOTEL DEVEL | Fast Retailing vs. Sunstone Hotel Investors | Fast Retailing vs. InterContinental Hotels Group |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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