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Emily Halverson-Duncan: Welcome to Quant Concepts' virtual office edition. If you were given an offer to buy out a company, you probably wouldn't accept without first doing research, or at least hopefully not. You'd likely want to understand more about the business, any potential competitors, and perhaps most importantly, understand the company's financial strength.
One of the best places to look to evaluate a company's financial strength is their balance sheet. Being able to understand what the company has going for them as well as any obligations they have against them will give you a much clearer numerical picture as to whether this company would make a good purchase. Picking a successful portfolio of stocks requires that same level of discipline, just on a larger scale. Through whatever research tools you have available at your disposal, you need to understand whether the companies you invest in are in good financial shape and decide if they are a worthwhile investment.
Today, I'm going to showcase a Canadian strategy that searches for stocks which have a strong balance sheet while still keeping focus on keeping volatility at a lower level. So, let's take a look at how to build that.
Jumping into our CPMS here, our universe today is the full Canadian universe which holds 701 names. Our first step, we're going to go ahead and organize those 701 names. And the factors we're going to look out for that organization – long-term debt to equity. So, that's a measure of a company's debt compared to its equity. You want to see a lower value of that just to make sure a company doesn't have too much debt on their books. Buyback yield is a measure of how many shares the company has repurchased, and you want to see a higher value there. It indicates the company thinks their own firm is a good investment. And then, five-year price beta. That is a measure of price sensitivity. So, here, we're looking at the price sensitivity of a company versus the S&P/TSX Composite, and you want to see a lower number there.
Once we've gone ahead and ranked our universe, we're going to apply a few screens. So, some of the screens that we're looking at here – working capital, we want that to be greater than or equal to 0. What that's looking at? It's a measure of liquidity, and it compares the current assets to current liabilities. Long-term debt to equity, that same measure we looked at on the last page. We want that to be less than or equal to 1, meaning that they don't have any more debt on their books than they do equity. Debt to cash flow, it's another profitability metric and another debt control. We want that to be less than or equal to 2. That buyback yield, we want that to be in the top roughly half of peers, which has a value of 0.72 or higher. And then, lastly, that five-year beta that we looked at, we want that to be less than or equal to 1, limiting stocks that tend to be as sensitive or less sensitive than the market.
On the sell side, we're going to sell stocks if their long-term debt to equity rises above 1.5, or if that debt to cash flow metric rises above a 3.
Once that's all done, we can go ahead and see how the back test of the model performs to see how these factors did across the long term. So, for our back test, say, we were in 15 stocks across January 2000 until end of October 2020 and compare that to the S&P/TSX Composite. Across that timeframe the strategy had very strong returns at 18%, which is an outperformance over the benchmark of 12.3%. The turnover was very low at 23% and what that's looking at is how often you're going to be trading stocks out of a model. So, at 23% on about 15 stocks, you're only doing a handful of trades per year on average.
Some of my favorite metrics to look at, of course, downside deviation, which looks at the volatility of negative returns for the strategy is 9.3%, but for the benchmark is 10.2%. So, there is a bit of an improvement on the strategy side. And then, in terms of how the model did in both up and down markets, in up markets it outperformed the benchmark 52% of the time and in down markets outperformed to 86% of the time. So, we can see that having those good balance sheet characteristics really helped the model to do well in terms of downside protection and when markets are declining. And overall, that really cumulated in very high annualized returns. So, again, when you're looking for quality companies, definitely a good idea is to take a look at their balance sheet.
For Morningstar, I'm Emily Halverson-Duncan.
