Correlation Between Ford and SPASX Dividend

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Can any of the company-specific risk be diversified away by investing in both Ford and SPASX Dividend 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 Ford and SPASX Dividend into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ford Motor and SPASX Dividend Opportunities, you can compare the effects of market volatilities on Ford and SPASX Dividend 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 Ford with a short position of SPASX Dividend. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ford and SPASX Dividend.

Diversification Opportunities for Ford and SPASX Dividend

0.09
  Correlation Coefficient

Significant diversification

The 3 months correlation between Ford and SPASX is 0.09. Overlapping area represents the amount of risk that can be diversified away by holding Ford Motor and SPASX Dividend Opportunities in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SPASX Dividend Oppor and Ford 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 Ford Motor are associated (or correlated) with SPASX Dividend. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of SPASX Dividend Oppor has no effect on the direction of Ford i.e., Ford and SPASX Dividend go up and down completely randomly.
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Pair Corralation between Ford and SPASX Dividend

Taking into account the 90-day investment horizon Ford Motor is expected to under-perform the SPASX Dividend. In addition to that, Ford is 2.74 times more volatile than SPASX Dividend Opportunities. It trades about -0.09 of its total potential returns per unit of risk. SPASX Dividend Opportunities is currently generating about -0.03 per unit of volatility. If you would invest  170,380  in SPASX Dividend Opportunities on December 1, 2024 and sell it today you would lose (2,250) from holding SPASX Dividend Opportunities or give up 1.32% of portfolio value over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthInsignificant
Accuracy98.36%
ValuesDaily Returns

Ford Motor  vs.  SPASX Dividend Opportunities

 Performance 
       Timeline  

Ford and SPASX Dividend Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with Ford and SPASX Dividend

The main advantage of trading using opposite Ford and SPASX Dividend positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ford position performs unexpectedly, SPASX Dividend 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 SPASX Dividend will offset losses from the drop in SPASX Dividend's long position.
The idea behind Ford Motor and SPASX Dividend Opportunities 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.
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.

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