Quant Concepts: Canadian Picks for a Second Wave

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Emily Halverson-Duncan: Welcome to Quant Concepts' virtual office edition. With rising concern around a possible second wave of the COVID-19 coronavirus, the future of the economy across the next several months is highly uncertain. Local governments have begun tightening restrictions amongst both individuals and businesses in the hopes of preventing further virus transmission.

From investors' perspectives, all these actions and uncertainty may cause concern regarding their investments. Earlier this year, most would have experienced fairly hefty declines in their portfolio, which naturally may be at the forefront of their thoughts as they consider the potential impact of a second wave. Today's video will look for a strategy that is searching for low volatility companies to help reduce portfolio risk. So, let's take a look at how to build that.

First off, as always, we're going to rank our universe of stocks, and today's universe is all the stocks in the CPMS Canadian database, which has 698 names as of today. The factors here that we're going to look at – we're going to look at both 5 and 3-year price beta. So, beta, as you recall, looks at a company's sensitivity compared to the market. The different timeframes here are 5 and 3-year are just the different timeframes that we're looking at that sensitivity to compare, and in both cases, we want lower values. Cash flow to debt – this is a ratio metric for the balance sheet, and we want to have higher values there, so more cash flow per units of debt. Industry relative earnings per share variability – what that's looking at is a company's earnings per share variability, or in other words, looking at their earnings per share metrics for last five years and seeing how variable or volatile those earnings are and then, comparing that to the same metric but for that company's industry. In this case, you want lower values, meaning that the company has less variability compared to its industry median. And then, lastly, we're looking at the average return on equity for the last five years. This is a profitability metric. And again, we want to have higher values here.

On the screening side, the screens that we will apply here, for both those 5 and 3-year price betas, we want to have them less than or equal to 1, meaning that the companies are as sensitive or less sensitive than the market. Cash flow to debt, we want that to be in the top third of peers, which today has a value of 0.34 or higher. The 5-year average return on equity, we also want that to be in the top third of peers and that has a value of 8.44% or higher as of today.

And then, our last two factors, we're looking at 5-year normalized sales growth. This is an annualized metric. We want that to be positive, indicating a company has been growing their sales over the last five years. And then, lastly, market cap, we want that to be in the top half of peers, roughly half, and that has a value of $393 million roughly or higher. So, that's just to get rid of any of the really small cap stocks that would be less liquid.

On the sell side, we've got a couple of sell factors here, one of which is on that 5-year price beta. We will sell if that rises above a value of 1.2. And then, on that 5-year ROE, we will sell if it falls into the bottom roughly two-thirds of peers, or below about 4.03% as of today.

So, now that's done, we can go ahead and take a look at our back test. So, just pulling that up here. Our back test today is going to be running 15 different stocks. And our benchmark will be the S&P/TSX Composite. So, the time period that we're looking at is from December 1990 until August of 2020. Across that timeframe, the model performed at 12.8%, which is an outperformance of 4.5% over the TSX. Turnover was very low at 14%. Again, recall, turnover is looking at how often the model is trading across the back test time period. So, at 14% and on about 15 stocks, you're looking at just a few trades a year.

In terms of risk metrics, which is of course what we're going to focus on with something that's meant to be a low-vol strategy. If we look at downside deviation, which is the volatility of negative returns, the strategy has a downside deviation of 6.4 and the benchmark of 9.8. So, definitely an improvement there. And then, of course, my favourite green and blue chart here, how did the model do in both up and down markets. In up markets, it only outperformed 48% of the time. But in down markets, it outperformed 84% of the time. So, actually, very, very high in terms of down market outperformance. So, if that's something that you're trying to position yourself for, if you think there's going to be more volatility and you want to be better positioned to protect your portfolio, having characteristics like that is exactly what you're looking for.

For Morningstar, I'm Emily Halverson-Duncan.