Investors can use changes in correlation statistics to identify new trends in the financial markets, the economy, and stock prices. The correlation between two variables is particularly helpful when investing in the financial markets. For example, a correlation can be helpful in determining how well a mutual fund performs relative to its benchmark index, or another fund or asset class.
By adding a low or negatively correlated mutual fund to an existing portfolio, the investor gains diversification benefits.
In other words, investors can use negatively correlated assets or securities to hedge their portfolios and reduce market risk due to volatility or wild price fluctuations. Many investors hedge the price risk of a portfolio, which effectively reduces any capital gains or losses because they want the dividend income or yield from the stock or security. Correlation statistics also allow investors to determine when the correlation between two variables changes.
For example, bank stocks typically have a highly positive correlation to interest rates, since loan rates are often calculated based on market interest rates. If the stock price of a certain bank is falling while interest rates are rising, investors can glean that something's askew with that particular bank.
If the stock prices of other banks in the sector are also rising, investors can conclude that the decline of the outlier bank's stock is not due to interest rates. Instead, the poorly performing bank is likely dealing with an internal, fundamental issue.
To calculate the Pearson product-moment correlation, one must first determine the covariance of the two variables in question. Next, one must calculate each variable's standard deviation. The correlation coefficient is determined by dividing the covariance by the product of the two variables' standard deviations. Standard deviation is a measure of the dispersion of data from its average. Covariance is a measure of how two variables change together, but its magnitude is unbounded, so it is difficult to interpret.
By dividing covariance by the product of the two standard deviations, one can calculate the normalized version of the statistic. This is the correlation coefficient. The correlation coefficient describes how one variable moves in relation to another. A negative correlation coefficient tells you that they instead move in opposite directions. A correlation of zero suggests no correlation at all. Correlation coefficients are a widely-used statistical measure in investing.
They play a very important role in areas such as portfolio composition, quantitative trading, and performance evaluation. For example, some portfolio managers will monitor the correlation coefficients of individual assets in their portfolios in order to ensure that the total volatility of their portfolios is maintained within acceptable limits. Similarly, analysts will sometimes use correlation coefficients to predict how a particular asset will be impacted by a change to an external factor, such as the price of a commodity or an interest rate.
Laerd Statistics. Kent State University. Fundamental Analysis. Portfolio Management. Financial Ratios. Financial Analysis.
Your Privacy Rights. To change or withdraw your consent choices for Investopedia. In a curvilinear relationship, variables are correlated in a given direction until a certain point, where the relationship changes. For example, imagine that we looked at our campsite elevations and how highly campers rate each campsite, on average. Perhaps at first, elevation and campsite ranking are positively correlated, because higher campsites get better views of the park.
But at a certain point, higher elevations become negatively correlated with campsite rankings, because campers feel cold at night! We can get even more insight by adding shaded density ellipses to our scatterplot. A density ellipse illustrates the densest region of the points in a scatterplot, which in turn helps us see the strength and direction of the correlation. Density ellipses can be various sizes. What is correlation? How is correlation measured? What are some limitations of correlation analysis?
Correlations describe data moving together Correlations are useful for describing simple relationships among data. What do correlation numbers mean? The closer r is to zero, the weaker the linear relationship. Positive r values indicate a positive correlation, where the values of both variables tend to increase together. Negative r values indicate a negative correlation, where the values of one variable tend to increase when the values of the other variable decrease.
Skip to main content. Single Accounts Corporate Solutions Universities. Definition Correlation A correlation measures the strength of a statistical link between two variables. Entries starting with C. Cross-sectional data Correlation Conjoint analysis Confidence level Conditional probability Competitor analysis Coefficient of correlation Cluster sample Cluster analysis Central limit theorem Causality Categorical.
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