Quant Concepts: Small-Cap Canadian Value Stocks

CPMS's Emily Halverson-Duncan creates a homegrown value strategy 

Emily Halverson-Duncan 7 August, 2020 | 12:48AM
Facebook Twitter LinkedIn



See more episodes of Quant Concepts here

Emily Halverson-Duncan: Welcome to Quant Concepts's virtual office edition. Value investing is a common type of investing where investors search for stocks that are currently undervalued compared to other stocks in the universe. This type of style is typically best suited towards long-term investors who are comfortable with the buy and hold philosophy and would rather avoid excessive trading most commonly associated with growth and momentum strategies. Today's strategy is going to search for value-focused stocks within the CPMS Canadian universe. So, let's take a look at how to build that.

First off, we are going to go ahead and rank our universe of stocks. Today's universe comprises of the 700 stocks covered in the CPMS Canadian universe. In our first step we're going to organize that universe of 700 stocks from most favorable to least favorable. The factors we're going to look at here are mostly value focused. We're looking at trailing price to earnings, price to book and price to cash flow, all of which are considered value metrics and we want them to be on the lower side because that would indicate that they are a lower-valued company compared to peers. The other factor we're going to consider for the ranking is five-year price beta versus the TSX Composite. This is more of a sensitivity metric and again, you want a lower value for this which would indicate that you are less sensitive than the market.

Once we've gone ahead and organized our universe, we're going to go and apply the screens. So, some of the screens we're applying here – we want a market cap greater than or equal to $150 million. That's just to get rid of the really small-cap stocks in the universe. We want to have the three value metrics we looked at – price to trailing earnings, price to book and price to trailing cash flow – to be in the lower half of peers. So, what that would do is screen out the most highly valued or overvalued half of companies for those three particular metrics respectively. We want a dividend payout ratio using earnings to be less than or equal to 80%. The reason for that cap is just to make sure companies aren't paying out too much of their earnings as dividends and still have money left over for other activities. And then, lastly, we want that beta metric to be less than or equal to 1. At a value of 1 it's as sensitive as the market. So, at less than or equal to 1, we're looking for as sensitive or less sensitive than the market.

And then, lastly, once we've done that, we're going to look at our sell criteria. And here, it's very simple. If our dividend payout ratio goes above that 80% level, we're going to sell the stock from the model.

So, once that's all applied, we can go ahead and take a look at how the back-tested returns did. Our back test here is holding 15 stocks and the benchmark we're looking at is the S&P/TSX Composite. So, taking a look here, the back test period was from May 2002 to June 2020. The return across that timeframe was 12.4% which represents an outperformance of 5.6% over the TSX. Turnover was very low at 20%. Again, turnover is looking at how often you are trading a stock from the model. So, at 20% you're really only doing a few trades across the year on average.

A couple of the metrics I always like to look at – downside deviation, which looks at how a strategy manages its returns less than zero. The strategy has a downside deviation of 7.7% whereas the benchmark has a downside deviation of 9.5%. So, it indicates that on a negative basis the strategy is doing better in terms of managing its volatility. And then, lastly, of course, my favorite green and blue chart looks at how the model did in both up and down markets. So, in up markets, this model outperformed 48% of the time, so a little less than the benchmark. But in down markets, it actually outperformed 79% of the time. So, again, reinforcing what we just saw with the downside deviation, it looks like this model is really good at handling downswings in the market. So, this is a way if you wanted to screen for some lower-valued companies, ones that are priced a little bit less than their peers, this is a way that you'd be able to do so.

For Morningstar, I'm Emily Halverson-Duncan.

Facebook Twitter LinkedIn

About Author

Emily Halverson-Duncan  Emily is Director, CPMS Sales at Morningstar

© Copyright 2024 Morningstar, Inc. All rights reserved.

Terms of Use        Privacy Policy       Disclosures        Accessibility