Beta and Covariance - 2 of the best tools to assess risk in your portfolio

Improve your Portfolio Risk Management with these 2 applicable tools

Managing risk is one of the most challenging tasks when constructing an Optimal Portfolio, however, there are certain tools that can help us to achieve a balance among our different assets in our portfolio. In this article, we are going to learn about 2 of them, which are very useful variables to measure the correlation of assets towards other assets and the market overall, looking at real market examples and understanding where can we get this data. 

Let's start talking about Beta, in simple terms it consists of a variable that usually goes from -2 to 3, it measures the expected move of a stock relative to movements in the overall market. For example, if a stock has a Beta of 2, it means that when the overall market goes up by 2%, this stock will increase by 4%. Some analysts consider the "market overall" related to the sector in which the stock is, for example, if we are talking about a Technology Stock such as NVIDIA, we should consider looking at the Nasdaq, or if the stock is from the Industry Sector such as Walmart, we should look at the Dow 30. Nonetheless, other analysts opt for just using the VTI index, which is basically composed of the total US stock market. Now, what happens if a stock has a negative Beta? It's simple, if the market overall performs +1% in a day, this stock will lose -1% in the day. Hence, if the market overall loses -4% in a day, this stock will increase by approximately +4%. Even tho, there are very few assets that have a negative beta, many economists believe that they are the key to having a successful portfolio because when market crises come, and everyone starts panicking, your portfolio will achieve a balance and avoid big losses. One of the most known assets with a negative Beta is Gold, moreover, fast-growing tech stocks tend to be the ones with the highest + Betas, such as Block (SQ) which has 2.32, while blue-chip stocks such as Berkshire Hathaway (BRK) tend to have a beta less than 1. Consequently, Beta Could help us to classify the volatility of different assets.

To deeply understand this concept, check out the following graph:

In this Scatter Diagram, we can see clearly how Beta works.

• Each dot represents a year.
• As you can see the diagram has a positive correlation since its Beta is positive and the Best Fit Line (Regression line) is going right towards the upright corner.
• The slope of the Regression Line (Best Fit Line) = Beta
• Consequently, if Apple has a Beta of 1.26 approx, it means that when the market moves 5% up, Apple moves 6.3% up, and vice versa.
• (In this case, the graph is using the S&P500 as the "market overall")

Understanding this will let us use this tool to measure the risk of our portfolio overall, by looking at each of the Betas of each of the assets we own. Our aim should be to have assets with Betas according to our Investor Profile, if you consider yourself an aggressive investor which is high-risk tolerant, then choose the stocks with the highest Betas since a higher Beta basically means +Return or +Losses. If your objective is to have a more Optimal Portfolio, then try to have a portfolio with assets that have different Betas, remember to include some with negative Betas to remain calm during market panics. 

To check the Beta of a specific asset, go to finance.yahoo.com or another finance platform and search for the asset you are interested in, for example in this case I am searching for the Beta of Tesla stock.

You will find the Beta in the "Summary" section. As you can see, in the yellow highlighted part, the Beta of Tesla is 2.05, which is very high. (Remember that Beta can change over time)

Secondly, there is also another tool that instead of measuring the relationship of an asset with the market overall, it measures the relationship of an asset towards another asset, and this is Covariance. Covariance is basically the relationship between 2 different stocks, meaning how does "A" stock react to B". For example, if the covariance between A and B is really low, it means that when A stock goes down, the other one would not necessarily follow the same pattern. Hence, to have an optimal portfolio you usually want to have a low covariance among your different assets, better if 0, because in this way the two stocks move in random directions from each other. If in your portfolio you only have gaming stocks, then your covariance will be very high, and if a market correction appears on the sector suddenly, your portfolio will definitely be hugely affected. To show a real market example, let's look at the covariance between Block (SQ) and Paypal (PYPL), which are 2 companies in the online payment sector.

As you may have anticipated, they have a high covariance, 0.79, it was expectable since we are talking about almost 2 competitors. The interpretation of this result is that if you have a portfolio with only these 2 stocks, it will have a very poor diversification, meaning that for example if Paypal stock goes down by 2%, Block will also decrease by 1.58%, and vice-versa. In the hypothetical case that 2 stocks have a covariance of 1, it basically means that they move perfectly together, which is something very hard to find in the market, and of course, same as with the Beta, if you have 2 stocks that have a -1 covariance it implies that they move perfectly opposite, when one goes up by 4%, the other goes down by 4% as well. 

Now let's look at the covariance between Wells Fargo (WFC), which is from the Banking Industry, and Block (SQ):

As you can see we have a negative covariance, -0.38. Even tho these 2 may have some similarities in their businesses and even in their clients (remember that Block accomplishes almost all of the same functions of a Bank; it makes short-term loans to small businesses, many people deposit directly their salary into their account, it has investment tools in its cash app, etc.), there is a discrepancy in their stock movement. Hence, if you would desire to invest some money into the Banking industry, and want to have a diversified portfolio, these 2 are very good examples of the kind of stocks you should have. 

A point to highlight in this issue is that covariance can change over time, if 2 stocks aren't currently correlated, they may be in 1 or 2 years. Therefore is something that must be reviewed historically every year. Remember that volatility increases correlation among assets, if the stock market overall gets at high levels of volatility due to an imminent crisis, then the correlation of the assets will increase, as we saw during 2008 when Crude Oil increased in price following the trend of the S&P 500, which is an untypical phenomenon. Many analysts believe that the ruin of the stock market would be when totally different assets of extremely different sectors, start correlating between them, for example, if Software/Technology companies start correlating with commodities such as Gold or Soy prices, nonetheless, with globalization rising each year, it's going to progressively be more laborious to have a diversified portfolio. 

Finally, in order to check the covariance of the assets you are interested in, go to macroaxis.com and input the symbols of the 2 assets you are interested in, then click Apply and the website will not only give you the covariance rather also a ton of other valuable information you can use to decide if the 2 assets are good candidates for optimal diversification.

You can even check the covariance of more than 2 assets in the same website, using the cross correlation tool, which will give the following result:

In conclusion, these 2 practical and advantageous tools will give you insight into whether your portfolio is correctly diversified or not. Remember that the intelligent investor reduces risk by allocating investments among various financial instruments, industries, and other categories. Aiming to maximize return by investing in different areas that should each react differently to changes in market conditions, and this is only possible with Diversification.