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Negative Correlation: How It Works, Examples, and FAQ

A key tool in diversifying your portfolio

Definition

A negative correlation me𒐪ans that two variables move in opposite directions from one another: when one rises, the other falls.

What Is Negative Correlation?

It's important for investors to understand the concept of negative correlation since balanced portfolios often include assets that have this relationship with one another. That way, should one part drop in value, others might not.

Negative correlation is also called inverse correlation, which is a relationship beꦑtween two variables in which one increases as the other decreases, and vice versa. A perfectly negative correlation means the relationship that exists between two variables is exactly opposite. In a line graph, you would see a downward slܫope.

In economics, price and quantity are generally negatively correlated on a demand curve. These 🧜are almost always downward-sloping, reflecting the williღngness of consumers to buy more of something as its price goes lower.

Key Takeaways

  • Negative correlation is put to use when constructing diversified portfolios so that investors can benefit from price increases in certain assets when others fall.
  • Correlations between two variables are often unstable and can vary widely over time.
  • Stocks and bonds generally have a negative correlation. Therefore, traditional portfolios tend to hold both.
  • Investing in negatively correlated assets can cut portfolio risk, but it can also minimize potential gains.
Negative Correlation

Investopedia / Ellen Lindner

How Negative Correlation Works

Negative or 澳洲幸运5开奖号码历史查询:inverse correlation indicates that two individual variables have prices that generally move in opposite directions. If, for instance♈, variables X and Y have a negative correlation, as ꦕX increases in value, Y will decrease; similarly, if X decreases in value, Y will increase.

In statistics, a perfectly negative 澳洲幸运5开奖号码历史查询:correlation is represented by the value -1.0, while 0 indicates no correlation, and +1.0 indicates a perfectly positive correlation. The supp🔥ly curve is almost always upward-sloping and represented with a positive correlation, reflecting how producers are willing to bring more of a produ💝ct to the market as prices go higher.

However, in🌊 most real-world scenarios, negative correlations are imperfect, meaning that while the general trend is downward, individual data points might not fit the trend exactly (as in the chart above).

Negative correlations are commonly observed in various fields, such as finance or economics, where there's typically a negative correlation between the supply of a product and its price. As supply increases, prices tend to fall, and vice versa.

A demand curve
A demand curvꦉe is a good example of an often-used chart ﷽that has a negative or inverse correlation.

Investopedia

Negative Correlation and the Correlation Coe𝄹fficient

The 澳洲幸运5开奖号码历史查询:correlation coefficient is how you'll typically get information about the correlations (negative or otherwise) between different things. It's given as a number ranging from -1.0 to +1.0. A coefficient of +1.0 is a perfect positive correlation, indicating that two assets🎶 move in perfect unison.

Meanwhile, a coefficient of -1.0 signifies a perfectly negative correlation, where the assets move in exactly opposite directions. When the coefficient is 0, there is no discernible relationship between the movements of the ꦇtwo assets.

For investors, the𝓡se numbers can be quite meaningful since they can be used to manage and create portfolios and handle risk. A diversified portfolio ofte𒆙n aims to include assets with low or negative correlations with each other. This strategy can help mitigate overall portfolio risk, as losses in one asset might be offset by gains in another.

However, like any tool in finance, the correlation coefficient has its limits. It only measures linear relationships and can be sensitive to outliers in the data. In addition, correlation doesn't imply causation, and historical correlations don't guarantee future results.

Important

The degree of correlation betwee💟n two variables is noᩚᩚᩚᩚᩚᩚ⁤⁤⁤⁤ᩚ⁤⁤⁤⁤ᩚ⁤⁤⁤⁤ᩚ𒀱ᩚᩚᩚt static but can move from positive to negative and vice versa over time.

Watching for Outliers

An outlier in financial data is an extreme value that significantly deviates from other observations in a dataset. Thes🧔e can occur because of exce🐬ptional market events, data errors, or genuinely unusual occurrences.

Suppose we're analyzing the correlation between the S&P 500 index and a tech stock over the past year, using daily returns. Most days, the stock moves similarly to the broader market, with returns ranging between -2% and +2%. However, one day🧔 the following occurs:

  • S&P 500 return: +0.5%
  • Tech stock return: +30%

This 30% jump in the tech stock could have come from unexpected news, like a major product breakthrough. This single day's outsized return will pull up the average return for the tech stock, potentially misrepresenting its typical relationship with the market.

Thus, if we calculate the correlation coefficient including this outlier, it would suggest a we🔯aker correlation between the stock and the S&P 500 than actually exists on most trading days. More broadly, this means watching out for times when the data might not be representative of most trading situations.

Negative Correlation and Investing

For investors, negative correlation refers to the relationship between two assets whose prices tend to move in opposite directions. When one asset's value increases, the other tends to decrease, and vice versa. Taking advantage of inverse relationships can be a great way to manage risk and optimize portfolios.

Here are two major 🦄uses in finan💎ce and investing:

  • Managing risk and diversification: By including negatively correlated assets in a portfolio, investors can reduce overall volatility. When one asset declines in value, the other may increase, helping to offset losses.
  • Hedging: Investors often use negatively correlated assets as a hedge against potential losses in their core holdings.

Here are examples of assets tr🎶aditionally said to be negatively correlated:ꦍ

  • Stocks and bonds: When stock prices fall, bond prices often rise as investors seek safer havens.
  • Gold and the U.S. dollar: Gold prices frequently rise when the U.S. dollar weakens (as in the first half of the 2020s and late 1970s), and vice versa.
  • Defensive stocks and cyclical stocks: Defensive stocks (e.g., utilities) often perform better when cyclical stocks (e.g., technology) struggle.

That said, while these are often said to be true, the significance of the correlation over time can change, so it's best to look at the data first before acting. Below, we've put together a correlation table comparing many of the major assets included in portfolios.

As you can see, there are positive correlations, but few that 🔴are negat💟ive, and only moderately so, at least when the asset classes are diversified within a class and compared.

Negative Correlation and Portfolio Di🎃versification

Negative correlation is a key when constructing a portfolio. When found between sectors or assets from different locales, negative correlations can be used to create diversified portfolios that can better withstan🐻d market volatility, smoothing out portfolio returns over 🍌the long term.

The building of large and complex portfolios where the correlations are carefully balanced is called 澳洲幸运5开奖号码历史查询:strategic asset allocation.

Co𝐆nsider the historically long-term negative correlation between stocks and bonds. Stocks generally outperform bonds during periods of str🙈ong economic performance, but as the economy slows down and the U.S. Federal Reserve and other central banks reduce interest rates to stimulate the economy, bonds often outperform stocks.

In the chart above, bonds tend to have a mo🍰derately negative to neutral correlation with stocks.

Note

Equities and bonds generally have a neg🐷ative correlation, but like other asset classes, correlation fl🅺uctuates, and these two assets become more and less correlated during certain circumstances.

Example: Constructing a Po𓃲rtfolio With Negative Correlations

Using the table of asset correlations above, let's see if we can construct a relatively well-balanced portfolio given the data about the relationship among the different assets. This is for informational purposes only.

After all, there are many more reasons for including certain assets and excluding others than their correlations. We would need better information about recent performance, as well as our 澳洲幸运5开奖号码历史查询:risk tolerance🔴, needs for liquidity, time horizon, and othe♑r details that are up to the individual investor and depend on what's happening in the present market. But doing so will help to demonstrate the concept.

A well-balanced portfolio often includes a mix of positively and negatively correlated assets to manage risk and potentially optimize returns over the long term. Let's choose a mix of ETFs representing stocks, bonds, a bit of gold, and commodities from the table above:

  • S&P 500 large-caps (IVV): 20%
  • U.S. mid-caps (IJH): 10%
  • U.S. small-caps (IJR): 10%
  • International (non-U.S.) stocks (EFA): 10%
  • Emerging market stocks (EEM): 10%
  • U.S. investment grade bonds (AGG): 15%
  • Seven-to-10-year Treasuries (IEF): 10%
  • Gold (GLD): 5%
  • Commodities (DBC): 10%

This is a portfolio that isn't well-balanced along other lines (two-thirds of the stock allocation is in more volatile markets), but let's see how we do balancing the correlations.

How To Cal⛄culate the We♓ighted Average Correlation

To determine the overall correlation of the portfolio, we compute the weighted average correlation, which means first figuring out each ETF's correlation with the others in the portfolio.

Step 1: For each ETF, we identified its correlation with all other ETFs in the correlation table. For example, for the S&P 500 large-cap ETF (IVV), we have to find its correlation with the others. Here's IVV with the others:

  • IJH: 0.91
  • IJR: 0.85
  • EFA: 0.86
  • EEM: 0.76
  • AGG: 0.07
  • IEF: -0.20
  • GLD: 0.02
  • DBC: 0.35

You'll need to do the same with each of the ETFs in the portfolio: IJH, IJ, etc. This can be done quickly in a spreadsheet.

Step 2: Sum the correlations above. Quickly doing so with the sum formula in our Google Sheet, 💫we get 3.62.

Step 3: Divide by eight to get the average, which𒐪 i▨s about 0.45.

Step 4: Now, do the same for each of the other ETFs in the portfolio and their correlations. We've calculated the average correlations for them with each other ETF as below:

  • IVV: 0.45
  • IJH: 0.46
  • IJR: 0.44
  • EFA: 0.47
  • EEM: 0.43
  • AGG: 0.20
  • IEF: 0.02
  • GLD: 0.16
  • DBC: 0.25

Step 5: Now, we can calculate the weighted correlations. That's because each one represents a specific portion of the portfolio, and we want to ensure we capture that. So, we multiply the averages above by the percentage share of the portfolio for each:

  • IVV: 0.45×0.20=0.09
  • IJH: 0.46×0.10=0.046
  • IJR: 0.44×0.10=0.044
  • EFA: 0.47×0.10=0.047
  • EEM: 0.43×0.10=0.043
  • AGG: 0.20×0.15=0.03
  • IEF: 0.02×0.10=0.002
  • GLD: 0.16×0.05=0.008
  • DBC: 0.25×0.10=0.025

Step 6: Now we add these together to get 0.34 (rounded to two decimal places). This is the weighted average correlation for the portfolio.

Why the Weighted Average Correlation Matters

The example above gives a weighted average correlation of 0.34, which indicates that, on average, the assets in our portfolio have a moderate tendency to move in the same direction. While our portfolio is somewhat diversified, it's not entirely insulated from market trends.

The correlation is not so high that all assets will move together in lockstep, but there is enough positive correlation th🤪at the portfolio is likely to experience the effects of market change🐷s together.

A 澳洲幸运5开奖号码历史查询:weighted average correlation in this range reflects a portfoli🌊o that co𝐆mbines assets with varying degrees of correlation:

  • High-correlation assets (such as IVV, IJH, and EFA) are more likely to move together, offering the potential for higher returns when markets are strong but also the risk of dropping together.
  • Low-correlation or modestly negative-correlation assets (such as AGG and IEF) provide stability since they've tended to behave differently from the equities in our portfolio, reducing overall volatility.
  • Neutral assets (such as GLD and DBC) add further diversification, not being heavily influenced in the past decade by movements in stocks.

The portfolio’s relatively moderate average correlation will hopefully allow us to capture gro✱wth while managing risk. During a market upswing, the positively correlated assets may drive portfolio gains. Meanwhile, should there be a downturn, the assets with low or negative correlations should help mitiౠgate losses, providing a cushion against volatility.

Note

When assets that are often negatively correlated move in the same direction, this is an example of 澳洲幸运5开奖号码历史查询:systematic risk. Systematic risk can't be diversified away; it will exist in financial markets 𒁃and 🍬is the inherent risk present in investing.

Though asset classes may traditionally be negatively correlate🦂d, macroec🐻onomic conditions may result in asset classes acting similarly due to broader impacts on the market.

Fine-Tuning the Portfolio

If our goal is to further reduce the portfolio's correlation and enhance diversification, we could increase the allocation of assets with lower or negative correlations, such as bonds (AGG, IEF) or commodities (GLD, DBC).

Meanwhile, if we're looking to capture more in potential gains should the market go up, we might increase our exposure to high-correlation assets like large-cap stocks (IVV) or international equities (EFA).

Limits of Using Correlationsꦰ to Build a Portfolio🍷

While correlation is essential in 澳洲幸运5开奖号码历史查询:constructing a balanced portfolio, a well-rounded strategy also considers asset allocation, risk tolerance, time horizon, and financial goals. For example, while low or negative correlations can help cut portfolio volatility, they may also limit potential returns should the assets with🌱 correlations closer to zero underperform.

In addition, liquidity, market conditions, and economic outlook often play a bigger role in determining which assets to include in a portfolioꦫ. Here arℱe some other drawbacks to using asset correlations in isolation from others:

  1. Markets change: Correlations aren't static, given shifts in market conditions, economic cycles, or geopolitical events. An asset pair that has shown a low or negative correlation in the past may become more correlated during market stress periods, reducing its effectiveness. Hence, it's essential to look at recent data on asset correlations and not just accept old truisms about stocks and bonds when building a portfolio.
  2. Assumes a linear relationship: Correlation measures only the linear relationship between two assets. However, many assets may have complex, nonlinear relationships that correlation fails to capture, potentially missing important aspects of their interaction.
  3. Doesn't capture volatility: Correlation doesn't give you any information about the magnitude of asset price movements. Two assets might have a low correlation, but if one is highly volatile, it could still contribute significantly to portfolio risk. Therefore, correlation and volatility measures, such as 澳洲幸运5开奖号码历史查询:standard deviation, should be used as well.
  4. History isn't the future: Correlation is usually calculated based on historical data, which may not accurately predict future relationships.
  5. Asset class diversification: Correlation focuses on the relationship between and among sets of assets, but it doesn't account for diversification across different asset classes, sectors, or geographic regions. A portfolio could have low correlations within a single asset class but still be under-diversified if it lacks exposure to other asset types.

How Is Correlation Calculated?

While you can use online calculators, as we have above, to calculate these figures for you, you first need to find the covariance of each variable. Then, the correlation coefficient is determined by dividing the covariance by the product of the variables' standard deviations.

What Are the Types of Correlation?

Correlation measures the relationship between two variables, and there are three main types: positive, negative, and no correlation. Beyond these, there are several methoꦓds for calculating correlation, each suited to different kinds of data.

The Pearson correlation measures the linear relationship between two continuous variables, while the Kendall rank correlation and Spearman correlation capture how one variable consistently increases or decreases with the other, even if the relationship isn't perfectly linear.

The Point-Biserial correlation is used when one variable is continuous and t🅰he other is binary. These different methods provide various insights into how assets might interact, emphasizing that correlation, though a useful tool, has its limits in portfolio analy🎃sis.

Is Negative Correlation Better Than Positive Correlation?

For 🥃some investors, negative correlation is better than positive correlation. This means that investors are exposed to less risk, have the chance to invest in different types of securities, and often experience less portfolio volatility. For others, negative correlation means hedging their investment, which minimizes potential gains.

The Bottom Line

Negative correlation can be used for a strategicꦿ approach to risk management and portfolio♊ diversification. By pairing assets that tend to move in opposite directions, investors could mitigate overall portfolio volatility and create more lasting strategies.

This approach challenges the typical move of seeking only positively correlated assets, opening up new ways to navigate the financiꩵal markets.🔜

However, correlation coefficients can shift over time, especially during extreme market events. Succꦉessfully using negative correlation strategies requires ongoing analysis, an up-to-date and subtle understanding of the market, and a willingness to adapt꧙ to changing conditions.

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