Correlation Between Arrow Financial and CBOE SP

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Can any of the company-specific risk be diversified away by investing in both Arrow Financial and CBOE SP 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 Arrow Financial and CBOE SP into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Arrow Financial and CBOE SP 500, you can compare the effects of market volatilities on Arrow Financial and CBOE SP 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 Arrow Financial with a short position of CBOE SP. Check out your portfolio center. Please also check ongoing floating volatility patterns of Arrow Financial and CBOE SP.

Diversification Opportunities for Arrow Financial and CBOE SP

0.83
  Correlation Coefficient

Very poor diversification

The 3 months correlation between Arrow and CBOE is 0.83. Overlapping area represents the amount of risk that can be diversified away by holding Arrow Financial and CBOE SP 500 in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on CBOE SP 500 and Arrow Financial 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 Arrow Financial are associated (or correlated) with CBOE SP. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of CBOE SP 500 has no effect on the direction of Arrow Financial i.e., Arrow Financial and CBOE SP go up and down completely randomly.
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Pair Corralation between Arrow Financial and CBOE SP

Given the investment horizon of 90 days Arrow Financial is expected to generate 4.82 times more return on investment than CBOE SP. However, Arrow Financial is 4.82 times more volatile than CBOE SP 500. It trades about 0.06 of its potential returns per unit of risk. CBOE SP 500 is currently generating about 0.23 per unit of risk. If you would invest  2,930  in Arrow Financial on September 18, 2024 and sell it today you would earn a total of  248.00  from holding Arrow Financial or generate 8.46% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthStrong
Accuracy98.44%
ValuesDaily Returns

Arrow Financial  vs.  CBOE SP 500

 Performance 
       Timeline  

Arrow Financial and CBOE SP Volatility Contrast

   Predicted Return Density   
       Returns  

Pair Trading with Arrow Financial and CBOE SP

The main advantage of trading using opposite Arrow Financial and CBOE SP positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Arrow Financial position performs unexpectedly, CBOE SP 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 CBOE SP will offset losses from the drop in CBOE SP's long position.
The idea behind Arrow Financial and CBOE SP 500 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 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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