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Investment - Correlation
Another approach of using and interpreting data is detecting relationships between data sets. The degree of a relationship between two variables, such as growth in gross domestic product (GDP) and stock market returns, can be quantified by employing correlation. Essentially, two variables are linked when a change in one variable helps predict a change in another one.
When both variables fluctuate in the same direction, the variables are positively linked. If we take the example of traders at an investment bank, salary and age are positively connected if salaries increase as age increases. If the variables move in the opposite direction, then they are negatively linked.
For example, the size of a transaction and the fees stated as a percentage of the transaction are negatively connected if the greater the transaction, the smaller the associated fees. When there is no evident tendency for one variable to move in a particular direction (up or down) relative to changes in the other variable, then the variables are near to being uncorrelated. In practice, it is difficult to identify two variables that have absolutely no relationship.
Correlation Coefficient
Correlation is assessed by the correlation coefficient, which has a scale of –1 to +1. When two variables move exactly in step with each other in the same direction — if one goes up and the other goes up in the same proportion — the variables are said to be fully positively linked. In that situation, the correlation coefficient is at its maximum of +1. When the two variables move exactly in step in opposite directions, they are perfectly negatively correlated, and the correlation coefficient is –1. Variables with no relationship to each other will have a correlation coefficient close to 0.
Degree of Correlation
Correlation evaluates both the direction of the association between two variables (negative or positive) and the strength of that relationship (that is, the closer to +1 or –1, the stronger the relationship). In practice, it is unusual to find variables that are fully positively or perfectly negatively associated. The greater the association between two variables — the higher the degree of correlation — the more reliably one variable may be predicted given the other value.
For example, there may be a substantial link between stock market index returns and predicted economic growth. In that instance, if economic growth in the future is predicted to be high, then returns on the stock market index are likely to be high too.
It is vital, however, to recognize that correlation does not imply causality. For example, traditionally in the United States, stock market returns and snowfall are both greater in January, and from that you may presume a correlation. But obviously, snowfall does not create an increase in stock market returns, and an increase in stock market returns surely does not cause snowfall.
There are occasions in which a correlation implies some causal relationship. For example, a substantial association has been discovered between power generation and job growth. It may follow that the more workers there are, the more power is consumed, but it does not necessarily follow that an increase in power generation will create jobs.
Correlation and Portfolio Diversification
Correlation is significant in investing because the rise or fall in value of a variable may assist anticipate the growth or fall in value of a security. It is also essential because when two or more securities that are not perfectly positively correlated are pooled together in a portfolio, there is generally a reduction in risk (measured by the portfolio’s standard deviation of returns). The process of blending assets in a portfolio to lessen risk is known as diversification.
An extreme example of an undiversified portfolio is someone holding only one security. This technique is dangerous because it is not rare for a single security to fall down in value by a big amount for a period of time, sometimes permanently. It is significantly less usual for a diversified portfolio of 20 or more different assets to go down by a large amount, even if they are selected at random.
If the assets are selected from a variety of sectors, industries, firm sizes, asset classes, and marketplaces, it is much less likely. One caution is that the benefits of diversity are considerably decreased in periods of financial crises. In such periods, the correlation between returns on different securities (and other asset classes) tends to climb towards +1.
Another approach of using and interpreting data is detecting relationships between data sets. The degree of a relationship between two variables, such as growth in gross domestic product (GDP) and stock market returns, can be quantified by employing correlation. Essentially, two variables are linked when a change in one variable helps predict a change in another one.
When both variables fluctuate in the same direction, the variables are positively linked. If we take the example of traders at an investment bank, salary and age are positively connected if salaries increase as age increases. If the variables move in the opposite direction, then they are negatively linked.
For example, the size of a transaction and the fees stated as a percentage of the transaction are negatively connected if the greater the transaction, the smaller the associated fees. When there is no evident tendency for one variable to move in a particular direction (up or down) relative to changes in the other variable, then the variables are near to being uncorrelated. In practice, it is difficult to identify two variables that have absolutely no relationship.
Correlation Coefficient
Correlation is assessed by the correlation coefficient, which has a scale of –1 to +1. When two variables move exactly in step with each other in the same direction — if one goes up and the other goes up in the same proportion — the variables are said to be fully positively linked. In that situation, the correlation coefficient is at its maximum of +1. When the two variables move exactly in step in opposite directions, they are perfectly negatively correlated, and the correlation coefficient is –1. Variables with no relationship to each other will have a correlation coefficient close to 0.
Degree of Correlation
Correlation evaluates both the direction of the association between two variables (negative or positive) and the strength of that relationship (that is, the closer to +1 or –1, the stronger the relationship). In practice, it is unusual to find variables that are fully positively or perfectly negatively associated. The greater the association between two variables — the higher the degree of correlation — the more reliably one variable may be predicted given the other value.
For example, there may be a substantial link between stock market index returns and predicted economic growth. In that instance, if economic growth in the future is predicted to be high, then returns on the stock market index are likely to be high too.
It is vital, however, to recognize that correlation does not imply causality. For example, traditionally in the United States, stock market returns and snowfall are both greater in January, and from that you may presume a correlation. But obviously, snowfall does not create an increase in stock market returns, and an increase in stock market returns surely does not cause snowfall.
There are occasions in which a correlation implies some causal relationship. For example, a substantial association has been discovered between power generation and job growth. It may follow that the more workers there are, the more power is consumed, but it does not necessarily follow that an increase in power generation will create jobs.
Correlation and Portfolio Diversification
Correlation is significant in investing because the rise or fall in value of a variable may assist anticipate the growth or fall in value of a security. It is also essential because when two or more securities that are not perfectly positively correlated are pooled together in a portfolio, there is generally a reduction in risk (measured by the portfolio’s standard deviation of returns). The process of blending assets in a portfolio to lessen risk is known as diversification.
An extreme example of an undiversified portfolio is someone holding only one security. This technique is dangerous because it is not rare for a single security to fall down in value by a big amount for a period of time, sometimes permanently. It is significantly less usual for a diversified portfolio of 20 or more different assets to go down by a large amount, even if they are selected at random.
If the assets are selected from a variety of sectors, industries, firm sizes, asset classes, and marketplaces, it is much less likely. One caution is that the benefits of diversity are considerably decreased in periods of financial crises. In such periods, the correlation between returns on different securities (and other asset classes) tends to climb towards +1.
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