# Basics of Investing & Portfolio Management

Hello there! Welcome to Your BullGlobe Lesson

# Part II: Risk Metrics

### Lesson 12

In this section, we will discover some of the best ways to quantify investment risk and compare it to your returns.

# Part II: Risk Metrics

In the previous section, we saw two of the basic risk measures, Standard Deviation, and Beta. In this section, we will discover some of the best ways to quantify investment risk and compare it to your returns.

## Sharpe Ratio

The Sharpe ratio is our favorite metric for analyzing risk-adjusted returns of an investment. To clarify, this ratio tells us how effective our investments are at generating returns for a specific level of risk. Since it includes a risk-free factor, it can help us determine whether our portfolio’s excess return (return above the risk-free rate) comes from good strategic investments or if it was due to a significant increase in investment risk.

This ratio can be calculated using any timeframe you want; however, we prefer consistently using yearly measures to give a clearer picture of the outcomes.

The Sharpe ratio is ultimately a measure of expected return (in excess of any riskless returns) per unit of risk; hence we call it a measure of risk-adjusted returns. Therefore, the higher the Sharpe ratio the better, as higher returns will be generated for a relatively low amount of risk. Generally, a Sharpe ratio below 1.0 is considered low, 1.0 or above is good and 2.0 or above is very good.

At BullGlobe, we use this measure in our optimization algorithm to identify the perfect asset mix in a specific portfolio. However, it is not a perfect metric since it accounts for both positive and negative volatility and it assumes the distribution of returns is normal, which are inherent flaws in the standard deviation element. In order to work around those flaws, we analyze other key risk metrics.

We will dig deeper into this really important metric in future segments.

## Sortino Ratio

Earlier, we saw that the standard deviation accounts for both positive (upside) and negative (downside) volatility; and since the Sharpe ratio uses the standard deviation as a proxy for risk, it makes no distinction between “good” and “bad” volatility.

The Sortino ratio is a modification of the Sharpe ratio that only focuses on the negative (bad) volatility. It uses the standard deviation of only those returns that fall under a predetermined, acceptable return target. That is why it is useful for investors to determine the rate of return needed to reach a specific financial goal. Generally, that acceptable return target is set to 0% in order to encompass all negative returns; however, investors often use whatever their MAR (minimum acceptable return) may be—think of it as the minimum return investors will accept, their threshold. For instance, if you are investing in order to put a downpayment on a house in a couple of years, you may want to achieve at the very least a yearly return of 3%, so the Sortino ratio will work with the standard deviation of an investment’s returns that are under that 3% threshold to help you assess its viability.

We will dig deeper into this really important metric in future segments.

## Drawdown

In investing, a drawdown is a measure of downside risk. It is a simple metric that tells us (on a percentage basis) how much an investment declined from its peak (its highest value) to its trough (its lowest value).

By analyzing an asset’s drawdown history we can see how it has been affected by economic downturns, which can help us understand what to anticipate in the next downturn. It can also be helpful to compare various investments’ historical risks. It is also used to measure the inherent investment risk of an investment.

As we have seen, drawdown is the percentage difference from any peak to its subsequent trough. But people tend to pay more attention to the maximum drawdown, which is the largest drawdown of an investment’s lifetime.

Drawdowns can be measured by their depth and by their length. If compared with other investments, by analyzing the depth you can identify the less risky options simply by seeing which ones decreased the least or even increased. This relates to the asset correlation topic, which we will cover in future segments. By analyzing the length, you can also identify the possibly less risky options by seeing which ones picked up faster.

A typical drawdown graph displays the history of percentage losses from the previous highest points. In other words, it shows how far the value has fallen from its previous high point. From the drawdown chart, we can get a sense of what assets are inherently more volatile and have historically suffered more in economic downturns. The blue line represents an asset and the further down it moves, the higher the downside volatility (bad risk).

It is important to note that a drawdown is still in place as long as the asset price remains below the previous peak. The end of a drawdown (a trough) is not registered until the value is fully recovered. In the chart, we can see a trough was identified at \$55 in 2018 because the value fully recovered past its previous peak of 2015, now being at \$72.

The above is a real, interactive drawdown chart that compares the metric among the assets in a portfolio.

We will dig deeper into this really important metric in future segments.

## Value at Risk (VaR)

Value at Risk (or VaR) is a measure that is used to statistically quantify the level of risk of a certain investment over a specific period of time. It essentially estimates the potential losses you may encounter in a given timeframe (days, months, years, etc.) for a given probability. VaR can be a really powerful tool to manage your investment risk. For instance, an investment’s monthly VaR could tell you that there is a 95% chance that you will not lose more than 5% of your investment in a month.

This metric has three basic components that you need to understand:

1. The confidence level is the statistical probability element. In the previous example, we used 95% as our confidence level. This is essentially saying that only in 5% of the cases analyzed (100% of cases – 95% confidence level), the estimated loss was more than 5% of your investment.
2. Possible loss is the value that can be lost (hence Value at Risk) in a given period of time. In the previous example, we found 5% as the maximum possible loss with a 95% confidence level.
3. The timeframe is the period of time in which we predict the losses can happen. In the previous example, the maximum possible losses to be incurred were in one month’s time. VaR can analyze daily, monthly, or yearly potential losses.

If we combine these three components we can form the following critical question:

“With a certain level of confidence (1), what is the most amount of money (2) that I can expect to lose in a given timeframe (3)?”

The above chart is an example of how we display a portfolio’s Value at Risk. We analyze the VaR at three levels of confidence (95%99%, and 99.9%) and compare a specific portfolio with the market benchmark and with its optimized version (which we create).

We will dig deeper into this really important metric in future segments.

## Skewness & Kurtosis

These two statistical metrics are used to define the shape of the distribution of an investment’s returns. In order to understand these two measures, we first have to understand what a histogram of returns is:

### Histogram of returns

A histogram is a type of graph that plots the frequency distribution of your returns. In simpler words, it captures the frequency (number of times) in which certain returns happen and it separates them into different buckets (bins or ranges).

Let’s build a quick example in order to bring the point home:

Below we see the list of monthly returns you got in your SmartPie Co. investment for the last 12 months.

The above graph is known as a histogram of monthly returns. For instance, we can see that returns that were between 0% and 0.5% (that is the bucket) happened 3 times (that is the frequency) in the year we analyzed.

Below is a real, interactive histogram that compares all assets in a portfolio.

The histogram is really useful in determining the volatility of an asset. Typically, the more spread-out an asset’s returns are, the higher its volatility. So, we need measures to determine the spread and also the symmetry of the returns. Here is where kurtosis and skewness come in:

### Kurtosis

This measures the spread of the returns in a histogram. Higher kurtosis implies our returns are likely to be more extreme, as outliers (extreme returns, positive or negative) happen often.

The normal kurtosis is 3.0; so, any number higher than that indicates that our return distribution has heavier tails and vice versa (tails are the positive and negative extremes of the data, and heavier or lighter tails refers to a high or low amount of extreme returns).

From our above histogram, we can easily find an outlier, we had one return that was between 2% and 2.5%. We found that the kurtosis in the above example is 0.85, which means that our returns are not very spread-out and there may be a low risk of seeing many extreme returns.

### Skewness

Skewness represents the level of symmetry of your returns in a histogram, or more accurately, the lack of symmetry. A histogram is symmetric if it looks the same to the left and right of the center point (which is the average return).

The skewness of a normal distribution is 0; so, if we have a skewness that is higher than 0, we say that our returns are skewed to the right. This means that the right tail is longer than the left tail, and we are likely to see a few very high returns, but a lot of small losses. Conversely, having a negative skewness means that your left tail will be longer than your right, which implies that you are likely to see a few big losses, but many small gains.

In our histogram above, we can see how the right side of the returns extends longer than the left (due to the outlier we previously identified). Therefore, we found that our skewness is 0.70. This tells us that we are likely to see a few high gains (like the 2.5%) and many smaller losses (like -0.5% or -1%).

We hope that, by now, you got an idea of the relevance of risk in investing. We are done with discussing risk, for now; we will learn how to truly incorporate a full risk analysis in your investment process in future sections. In the next segment, we will discover how to actually build your portfolio.

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