# Positive Correlation – Definition

### Positive Correlation Definition

Positive correlation refers to the relationship formed between two variables where both of them move in a similar direction. When the increase in one variable causes an increase in the second variable, and decrease in one variable causes a decrease in the other, it is a sign of positive correlation.

In statistical terms, a perfectly positive correlation signifies the correlation coefficient value of +1.0, no correlation signifies the CV of zero, and negative or a perfect inverse correlation signifies -1.0.

If there lies a correlation among variables, it doesn’t mean causation.

### A Little More on What is Positive Correlation

A perfectly positive correlation refers to the situation that states that all variables in picture move together in the same direction and with the same percentage. The product demand and the related price can represent a positive correlation. The price of a product with an increase in demand, provided the supply doesn’t change.

Also, profits and losses in specific markets may cause similar trends in the related markets. With the increase in prices of fuel, airline tickets get expensive as well. As airplanes work on fuel, such raise in cost is ultimately allocated to be borne by the consumers, hence creating a positive correlation between airline ticket price and fuel prices.

A positive correlation is not an indicator of advantage or growth. Instead, it just signifies a direct relationship between two variables. So, if there is an increase in one variable, there would be an increase in another variable too. It is not necessary for causation to take place if correlation exists. Therefore, there can be movement among specific variables, the cause of such movements taking place may be hard to find.

Correlation is a kind of dependency where a change in one variable represents a change in the other variable too. For example, complementary demand of product. The rise in demand of vehicles would lead to an increase in related demand of their services. The hike or rise experienced in one sector would have an effect on complementary sectors.

There can be cases when positive changes in a sector can be because of positive psychological responses. For example, an organization with better financial performance boosts investors’ confidence to make investments in their stocks, thereby increasing its stock price.

Key points to remember

• Positive correlation refers to a link between two variables moving in the similar direction.
• When one variable increases, there will be an increase in another variable, and a fall in one variable would cause a fall in another variable.
• There can be a positive correlation between stocks, or with the whole market.
• Beta measures the correlation of the price of a stock with the bigger market, by using the S&P 500 index as a standard.

### Positive correlation in Finance

In the finance industry, the savings account and the interest rate thereon can be a fine example of positive correlation. As more money in the savings account is added irrespective of the source, the account holder will receive more interest on the underlying amount. Also, if the interest rate of a bank increases from 3% to 3.5%, the amount of interest will increase, and similarly, if the interest rate is lowered, the amount of interest will be reduced too.

Financial analysts and investors observe the movement of stocks, and try to strike a correlation with one another as well as in the market. Mostly, there is a correlation between the movement of stock prices in the mid-range having zero as coefficient. When coefficient is zero, it means that there exists no relation between the securities. There will be less probable correlation between the stocks of an e-store and a tire store. However, it will be higher when evaluating stocks of the two e-stores. This is so because two different companies will manufacture and deal in different products and services utilizing distinct resources.

On the other side, a retailer having a physical store will showcase a negative correlation with the official website of Amazon store because of the latter’s popularity. However, there would be a positive correlation between the PayPal’s stock and those of e-retailers that utilize its payment-based services. In case, the stocks of giant online platforms like Amazon, eBay and Best Buy rise, there are high chances that the stock of PayPal will increase on a similar level. This will happen due to increase in its fee-based income and favorable financial reports.

### Beta and Correlation

Beta measures the correlation between the stock price of an individual stock and the larger market. Usually, S&P 500 index is used as a primary benchmark for measuring this correlation. A stock having a beta value of 1.0 signifies a strong correlation of price movement with the market. Such stocks involve systematic risk. However, the measurement of beta cannot observe any kind of unsystematic risk. If a stock is included to a portfolio carrying a beta value of 1.0, it signifies that there won’t be any risks involved. However, it also doesn’t ensure that the investor will receive any additional returns on their investment.

If the value of beta is less than 1.0, it signifies a less sense of volatility in the security, thereby reducing the portfolio risks overall. For instance, utility stocks carry less beta values because of their comparatively slower movement than market average.

On the other side, a stock having a beta value of more than 1.0 makes security more volatile than the market. For instance, a stock having a beta of 1.3 makes the security 30% more volatile and riskier as compared to the market. For example: technology stocks and small cap securities. Adding stock with a higher beta makes a portfolio riskier, and increases the chances of receiving prospective returns.

There can be some stocks accompanied with negative betas too. A beta having value of -1.0 states inverse correlation of a stock with the market standards. Negative betas can be seen in put options and inverse ETFs. It is usual for sectors such as mining to show negative beta.

### The Difference between positive correlation and inverse correlation

In statistical language, positive correlation is about stating the relationship between two variables moving or changing in the similar way, while an inverse correlation talks about the relationship between two variables moving in different or opposite directions. Inverse correlation is sometimes referred to as negative correlation. There are many instances related to positive correlation that we can relate to in our day-to-day lives. If an employee workers for 25 hours instead of 20 hours, his or her salary will increase accordingly. If the marketing agency spends more on its promotions, it will result in more sales of products and services.

Inverse correlation analyzes two variables that are opposite to each other. For example, a person spending more will see a decline in his or her bank balance, or the more a person drives a car, the less will be its gas mileage. In the financial world, stocks and bonds show inverse correlation. With the increase in stock prices, there will be a fall in the bond market. In case, bond markets outperform when stocks are not performing well.

Correlation is not always based on causation. There can be cases when two variables either increase or decrease together, or a variable increases when the other one falls, but it doesn’t mean that the rise or fall in one factor is caused by the other factor’s movement. Such movements in both variables can be because of some other factors like price of commodities, or it can just be a mere coincidence.

For example, the gradual increase in the number of internet users and increase in petrol prices doesn’t have any specific correlation between them. In this case, it is again a coincidence that there is a rise in online users and petrol prices.

### References for “Positive Correlation”

https://www.investopedia.com › Investing › Financial Analysis

https://examples.yourdictionary.com/positive-correlation-examples.html