Volatility is a very general metric of variability in returns, so it’s used in many ways. In this video, we’re going to mention a few possible ways that might be used in trading, but there are many more possibilities. First, you can classify stocks as highly volatile or less volatile. Some strategies might work best on low volatility stocks, so you could use the volatility metric to limit your universe. For example, one type of strategy called a mean reverting strategy is to take a short position on an asset when it appreciates because you think it will depreciate again. If a stock has low volatility, you may think there is less uncertainty in its fair value. So, after a large deviation from its fair value, its price is more likely to revert. Another interesting observation about low volatility stocks is that they actually seem to perform well compared to high volatility stocks. In finance in general, greater risk is thought to accompany greater returns. So, the empirical observation that low volatility stocks outperform high-volatility stocks is considered a mystery. One explanation is that people ignore really boring things in favor of exciting stocks, like the tortoise and the hare. The low volatility tortoise is chugging along, but people are buying the high-volatility hare stocks. Some exchange traded funds specifically optimized for low volatility and seek to include low volatility stocks. We’ll talk more about exchange traded funds in future lessons. Another way to use volatility is to normalize another metric. Let me explain what I mean by that. Say you have a momentum signal consisting of returns over the previous year, say both Netflix and Walmart have a return of 30%. This may seem to mean they’re doing equally well, but in fact, Netflix is much more volatile, it can move 30% in a week. So, you could normalize your momentum signal by dividing by the volatility. The idea is to make the comparison apples to apples. This theme exists all over the place. If you want to compare any signal across your entire universe, you usually get some benefit by standardizing it in this way. You can also use volatility to help you determine position sizes. In general, quant traders utilize smaller position sizes in their strategies when markets are volatile to minimize the volatility of net profits, commonly called profit and loss, or P&L. Moreover, the volatility of overall portfolio, P&L, is often used as a gauge of the professional ability of portfolio managers managing multiple trading strategies. So, the ability to allocate capital, taking volatility into account becomes very important. If you believe you can forecast volatility with a certain level of confidence, you can adjust your portfolio sizing in anticipation of a pending storm in the market. But how would you decide how much money to move? Well, you can include volatility in a formula used to determine the size of your positions. Every trader might do this slightly differently, but as an example, a trader might use a formula like this. R divided by sigma times M times last close equals position size, where r is the dollar amount the trader is willing to lose if an M sigma event occurs against her or his position. Sigma is the annualized volatility of the security or strategy in question, M is a trader to find integer, last close is the last closing price of the security or the equivalent for a portfolio of stocks, and position size is the number of stocks to trade. This will lead to smaller position sizes for more expensive and more volatile assets. If you determine your position using a formula like this, then when you notice market conditions changing, you might decide to recalculate and adjust your position sizes accordingly. Sometimes trading strategies define thresholds at which to automatically sell a stock to take a guaranteed profit or limit losses. An upper threshold is called the take profit level, while a lower threshold is called a stop-loss level. These might be defined as a target price or as a percent change from an entry price. However, if market volatility increases, many stocks might reach these levels more frequently causing more frequent trades. During volatile times, traders might adjust these thresholds accordingly.