Correlation Between DOW and Q3 All-Weather

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Can any of the company-specific risk be diversified away by investing in both DOW and Q3 All-Weather 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 DOW and Q3 All-Weather into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between DOW and Q3 All-Weather Tactical, you can compare the effects of market volatilities on DOW and Q3 All-Weather 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 DOW with a short position of Q3 All-Weather. Check out your portfolio center. Please also check ongoing floating volatility patterns of DOW and Q3 All-Weather.

Diversification Opportunities for DOW and Q3 All-Weather

0.53
  Correlation Coefficient

Very weak diversification

The 3 months correlation between DOW and QAITX is 0.53. Overlapping area represents the amount of risk that can be diversified away by holding DOW and Q3 All-Weather Tactical in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Q3 All-Weather Tactical and DOW 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 DOW are associated (or correlated) with Q3 All-Weather. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Q3 All-Weather Tactical has no effect on the direction of DOW i.e., DOW and Q3 All-Weather go up and down completely randomly.
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Pair Corralation between DOW and Q3 All-Weather

Given the investment horizon of 90 days DOW is expected to generate 1.89 times more return on investment than Q3 All-Weather. However, DOW is 1.89 times more volatile than Q3 All-Weather Tactical. It trades about 0.12 of its potential returns per unit of risk. Q3 All-Weather Tactical is currently generating about 0.06 per unit of risk. If you would invest  3,126,190  in DOW on May 20, 2022 and sell it today you would earn a total of  271,842  from holding DOW or generate 8.7% return on investment over 90 days.
Time Period3 Months [change]
DirectionMoves Together 
StrengthWeak
Accuracy100.0%
ValuesDaily Returns

DOW  vs.  Q3 All-Weather Tactical

 Performance (%) 
       Timeline  

DOW and Q3 All-Weather Volatility Contrast

   Predicted Return Density   
       Returns  

DOW

Pair trading matchups for DOW

Walker Dunlop vs. DOW
Global Clean vs. DOW
GM vs. DOW
Salesforce vs. DOW
SPDR SP vs. DOW
Schwab US vs. DOW
JP Morgan vs. DOW
Vici Properties vs. DOW
Visa vs. DOW
Vmware vs. DOW
Citigroup vs. DOW
Ford vs. DOW
The effect of pair diversification on risk is to reduce it, but we should note this doesn't apply to all risk types. When we trade pairs against DOW as a counterpart, there is always some inherent risk that will never be diversified away no matter what. This volatility limits the effect of tactical diversification using pair trading. DOW's systematic risk is the inherent uncertainty of the entire market, and therefore cannot be mitigated even by pair-trading it against the equity that is not highly correlated to it. On the other hand, DOW's unsystematic risk describes the types of risk that we can protect against, at least to some degree, by selecting a matching pair that is not perfectly correlated to DOW.

Q3 All-Weather Tactical

Pair trading matchups for Q3 All-Weather

Schwab US vs. Q3 All-Weather
Alibaba Group vs. Q3 All-Weather
GM vs. Q3 All-Weather
Alps Clean vs. Q3 All-Weather
JP Morgan vs. Q3 All-Weather
Visa vs. Q3 All-Weather
Vmware vs. Q3 All-Weather
Walker Dunlop vs. Q3 All-Weather
Twitter vs. Q3 All-Weather
Ford vs. Q3 All-Weather
SPDR SP vs. Q3 All-Weather
The effect of pair diversification on risk is to reduce it, but we should note this doesn't apply to all risk types. When we trade pairs against Q3 All-Weather as a counterpart, there is always some inherent risk that will never be diversified away no matter what. This volatility limits the effect of tactical diversification using pair trading. Q3 All-Weather's systematic risk is the inherent uncertainty of the entire market, and therefore cannot be mitigated even by pair-trading it against the equity that is not highly correlated to it. On the other hand, Q3 All-Weather's unsystematic risk describes the types of risk that we can protect against, at least to some degree, by selecting a matching pair that is not perfectly correlated to Q3 All-Weather Tactical.

Pair Trading with DOW and Q3 All-Weather

The main advantage of trading using opposite DOW and Q3 All-Weather positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if DOW position performs unexpectedly, Q3 All-Weather 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 Q3 All-Weather will offset losses from the drop in Q3 All-Weather's long position.

DOW

Pair trading matchups for DOW

Paypal Holdings vs. DOW
GM vs. DOW
Vmware vs. DOW
Alps Clean vs. DOW
Ford vs. DOW
Alibaba Group vs. DOW
SP 500 vs. DOW
Schwab US vs. DOW
Salesforce vs. DOW
JP Morgan vs. DOW
Global Clean vs. DOW
The effect of pair diversification on risk is to reduce it, but we should note this doesn't apply to all risk types. When we trade pairs against DOW as a counterpart, there is always some inherent risk that will never be diversified away no matter what. This volatility limits the effect of tactical diversification using pair trading. DOW's systematic risk is the inherent uncertainty of the entire market, and therefore cannot be mitigated even by pair-trading it against the equity that is not highly correlated to it. On the other hand, DOW's unsystematic risk describes the types of risk that we can protect against, at least to some degree, by selecting a matching pair that is not perfectly correlated to DOW.
The idea behind DOW and Q3 All-Weather Tactical 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.

Q3 All-Weather Tactical

Pair trading matchups for Q3 All-Weather

Schwab US vs. Q3 All-Weather
Ford vs. Q3 All-Weather
Paypal Holdings vs. Q3 All-Weather
Salesforce vs. Q3 All-Weather
Global Clean vs. Q3 All-Weather
Alps Clean vs. Q3 All-Weather
Alibaba Group vs. Q3 All-Weather
Vmware vs. Q3 All-Weather
Citigroup vs. Q3 All-Weather
Twitter vs. Q3 All-Weather
The effect of pair diversification on risk is to reduce it, but we should note this doesn't apply to all risk types. When we trade pairs against Q3 All-Weather as a counterpart, there is always some inherent risk that will never be diversified away no matter what. This volatility limits the effect of tactical diversification using pair trading. Q3 All-Weather's systematic risk is the inherent uncertainty of the entire market, and therefore cannot be mitigated even by pair-trading it against the equity that is not highly correlated to it. On the other hand, Q3 All-Weather's unsystematic risk describes the types of risk that we can protect against, at least to some degree, by selecting a matching pair that is not perfectly correlated to Q3 All-Weather Tactical.
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 Fundamental Analysis module to view fundamental data based on most recent published financial statements.

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