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An investor can reduce portfolio risk simply by holding instruments which are not perfectly correlated. In other words, investors can reduce their exposure to individual asset risk by holding a diversified portfolio of assets. Diversification will allow for the same portfolio return with reduced risk.

About correlation table

Correlation table is a two-dimensional matrix that shows correlation coefficient between pairs of securities. The cells in the table are color-coded to highlight significantly positive and negative relationships.

About correlation cloud

Correlation cloud is a flat representation of correlation coefficients between pairs of securities. The links in the cloud are color-coded to highlight significantly positive and negative relationships.

To create correlation table or cloud specify valid comma-separated symbols and hit Build It button.

Please note, the New York Stock Exchange (NYSE) and American Stock Exchange (AMEX) have recently merged. Although Macroaxis has implemented solutions to handle this transition gracefully, you may still find some securities that may not be fully transferred from one exchange to another.
       

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      Hover over cells for correlations between assets, or click to compare fundamentalsVolatilityBacktest

Correlation Matchups

    

High positive correlations

TABCF   + 0.44   COSN
TABCF   + 0.27   MTN
COSN   + 0.22   ORIENTPPR
FRFHF   + 0.2   COSN
MTN   + 0.18   MCKE
MTN   + 0.11   COSN
TABCF   + 0.08   ORIENTPPR
EAF   + 0.07   MCKE
TABCF   + 0.04   FRFHF
COSN   + 0.01   MCKE

Insignificant Correlation

EAF   - 0.02   FRFHF
FRFHF   - 0.03   ORIENTPPR
TABCF   - 0.03   MCKE
EAF   - 0.04   MTN
MTN   - 0.06   FRFHF
EAF   - 0.07   COSN
MCKE   - 0.14   ORIENTPPR
COSN   + 0.01   MCKE
TABCF   + 0.04   FRFHF
EAF   + 0.07   MCKE
TABCF   + 0.08   ORIENTPPR
MTN   + 0.11   COSN
MTN   + 0.18   MCKE
FRFHF   + 0.2   COSN

High negative correlations

EAF   - 0.69   ORIENTPPR
MTN   - 0.36   ORIENTPPR
TABCF   - 0.34   EAF
FRFHF   - 0.34   MCKE
MCKE   - 0.14   ORIENTPPR
EAF   - 0.07   COSN
MTN   - 0.06   FRFHF
EAF   - 0.04   MTN
TABCF   - 0.03   MCKE
FRFHF   - 0.03   ORIENTPPR
EAF   - 0.02   FRFHF
    
       

Why correlation coefficient is important?

If all the assets of a portfolio have a correlation of 1, i.e., perfect correlation, the portfolio volatility (standard deviation) will be equal to the weighted sum of the individual asset volatilities. Hence the portfolio variance will be equal to the square of the total weighted sum of the individual asset volatilities. If all the assets have a correlation of 0, i.e., perfectly uncorrelated, the portfolio variance is the sum of the individual asset weights squared times the individual asset variance (and volatility is the square root of this sum). If correlation is less than zero, i.e., the assets are inversely correlated, the portfolio variance and hence volatility will be less than if the correlation is 0. Learn more...


References

Modern Portfolio Theory From Wikipedia, the free encyclopedia Learn About Modern Portfolio Theory (MPT)
Markowitz, Harry M. (1952). Portfolio Selection, Journal of Finance, 7 (1)
Sharpe, William F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk, Journal of Finance, 19(3)
Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets, The Review of Economics and Statistics, 47 (1), 13-39
Burmeister E and Wall KD., The arbitrage pricing theory and macroeconomic factor measures, The Financial Review, 21:1-20, 1986
Chen, N.F, and Ingersoll, E., Exact pricing in linear factor models with finitely many assets: A note, Journal of Finance June 1983
Fama, E. and French, K. (1992). The Cross-Section of Expected Stock Returns, Journal of Finance, June 1992, 427-466
Black, F., Jensen, M., and Scholes, M. The Capital Asset Pricing Model: Some Empirical Tests, in M. Jensen ed., Studies in the Theory of Capital Markets. (1972)
French, C. W. (2003). "The Treynor Capital Asset Pricing Model", Journal of Investment Management, 1 (2), 60-72
Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets, Review of Economics and Statistics, 47 (1), 13-37
Markowitz, Harry M. (1999). The early history of portfolio theory: 1600-1960, Financial Analysts Journal, 55 (4)
Tobin, James (1958). Liquidity preference as behavior towards risk, The Review of Economic Studies, 25 Treynor, J. L. (1961). "Market Value, Time, and Risk." Unpublished manuscript.
Treynor, J. L. (1962). "Toward a Theory of Market Value of Risky Assets." Unpublished manuscript.

Other Resources

Robust Portfolio Optimization and Management by Frank J. Fabozzi, Petter N. Kolm, Dessislava Pachamanova, Sergio M. Focardi
Portfolio Optimization and Performance Analysis by Jean-Luc Prigent
Option Pricing and Portfolio Optimization by Ralf Korn, Elke Korn
Portfolio optimizations in incomplete financial markets by Walter Schachermayer
Bond Portfolio Optimization by Michael Puhle
An MCDM approach to portfolio optimization by M. Ehrgott, K. Klamroth, C. Schwehm
    
Efficient Frontier
Efficient Frontier
31049  global portfolios
Add positions and diversify yours portfolios
    

Sharpe Ratios 

   
 Macroaxis: United States NAMER 0.00 Price Moved None
   
 Macroaxis: United States WELLSTREAM 0.00 Price Moved None
   
 Macroaxis: United States OMNITECH 0.00 Price Moved None
   
 Macroaxis: United States Farmer 0.43 Price Moved Up
   
 Macroaxis: United States MICROHELIX 0.00 Price Moved None
   
 Macroaxis: United States Virginia 0.07 Price Moved Down
   
 Macroaxis: United States Ausmon 0.18 Price Moved Up
   
 Macroaxis: United States Vector 0.04 Price Moved Up
   
 Macroaxis: United States Mizuho 0.39 Price Moved Up
   
 Macroaxis: United States COMPANIA 0.00 Price Moved None
   
 Macroaxis: United States Riverbed 0.06 Price Moved Up
   
 Macroaxis: United States Farmworks 0.10 Price Moved Down
    
        
       

Top Advisors 

 1 
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 4 
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 5 
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Top Owned Stocks 

 1 
 Macroaxis: United StatesAAPL  464 
 2 
 Macroaxis: United StatesGOOG  373 
 3 
 Macroaxis: United StatesGE  335 
 4 
 Macroaxis: United StatesMSFT  278 
 5 
 Macroaxis: United States 263 

Top Owned ETFs 

 1 
 Macroaxis: United StatesSPY  314 
 2 
 Macroaxis: United StatesGLD  307 
 3 
 Macroaxis: United StatesVWO  242 
 4 
 Macroaxis: United StatesEEM  235 
 5 
 Macroaxis: United StatesVNQ  200 

Top Owned Funds 

 1 
 Macroaxis: United StatesFCNTX  121 
 2 
 Macroaxis: United StatesVFINX  107 
 3 
 Macroaxis: United StatesPRPFX  105 
 4 
 Macroaxis: United StatesDODFX  102 
 5 
 Macroaxis: United StatesVGSIX  94