| AMP.MC | UPL.MC | XCOP.MC | FRS.MC | REY.MC | IBG.MC | VWG.MC | ENG.MC | ISUR.MC | NTC.MC | PRM.MC | ITX.MC |
| High positive correlations | | Recommended Pairs  | | High negative correlations |
Why correlation coefficient is important?
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.
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...
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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.
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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
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Efficient Frontier  15511 global portfolios
1 |  | Slick | 65 | 2 | | fullo | 52 | 3 | | Tobby23748 | 41 | 4 | | Camden18579 | 38 | 5 | | MyBhavesh | 38 |
1 |  | AAPL | 226 | 2 | | GOOG | 220 | 3 | | GE | 192 | 4 | | MSFT | 148 | 5 | | C | 134 |
1 | | SPY | 123 | 2 | | EEM | 119 | 3 | | GLD | 110 | 4 | | EFA | 103 | 5 | | VWO | 96 |
1 | | VFINX | 59 | 2 | | FCNTX | 58 | 3 | | VTSMX | 54 | 4 | | DODFX | 53 | 5 | | VBMFX | 52 |
1 | Shepherd Kaplan | 5 | 2 | Hilltop Advisor | 5 | 3 | Babson Capital | 3 | 4 | Eldridge Financ | 3 | 5 | Vest Assured I | 3 |
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