Quant Concepts: Earnings at the Right Price

Building a strategy that pays a better price for performance, with CPMS’s Emily Halverson-Duncan

Emily Halverson-Duncan 14 August, 2020 | 1:29AM

 

 

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Emily Halverson-Duncan: Welcome to Quant Concepts' virtual office edition. Value strategies typically search for stocks trading below their intrinsic value rendering an undervalue compared to peers. A stock's intrinsic value is its perceived true value and represents the price that the stock should eventually achieve. A common way for these types of stocks is to search for companies with a low price to earnings multiple compared to peers signalling that that company is currently undervalued relative to other stocks. The difficult part, however, can be finding undervalued companies that are going to start increasing in price and avoid companies that are going to continue to decline. Today, we're going to be searching for a strategy that looks for small-cap companies in the CPMS Canadian universe. So, let's take a look at how to build that.

So, jumping in here into CPMS Canada, our universe here is the 699 stocks in the CPMS Canadian database. But again, we're going to focus more so on small-cap stocks to pop up some names that we're not as familiar seeing. So, first off, in our ranking step we're going to organize our universe by looking at four different factors, the first of which is price-to-trailing earnings. As mentioned there, this is a value metric and we want to see that on the lower side to find undervalued stocks. Price to book and price to cash flow are both similar as well. They are looking for, again, lower metrics for those particular factors and would signal that a stock is undervalued compared to peers. And then, lastly, five-year price beta – this is looking at a stock's sensitivity compared to the index which in this case is the TSX Composite and we want to see lower values here as well.

Once we've organized our universe, we're going to go ahead and apply some screens here. So, just going through a few of them – at the top, we've got a market cap screen where we want our market cap to be in the bottom half of peers. So, this is different than what we usually do. We're setting it up this way so that we're eliminating the large or large mid-cap stocks and are left with more of a small-cap space. And in this case, that would mean a market cap below roughly $400 million. Our three value metrics here – price to trailing earnings, price to book and price to trailing cash flow – we want those all to be in the bottom half of peers and the reason again is to eliminate those high valued top 50% of peers and leave you with that lower-valued segment. Lastly, then we've got a payout ratio and beta. For our payout ratio, that's looking at how much a company is paying out in dividends relative to their earnings per share and we are capping that at 60% just to make sure that they are not paying out too much of their earnings as dividends. And lastly, that beta that we talked about earlier, we want that to be less than or equal to 1.

On the sell side, we're very simply going to sell if that payout ratio rises above 80%, meaning that a company has increased how much they are paying in dividends compared to their earnings or their earnings have declined, and that ratio has increased.

So, once this is all done, of course, we want to see how the model did across the long term. Our backtest here is going to hold 15 stocks and is running from January 2000 until the end of June 2020. So, let's take a look and see how that performance did.

All right. So, looking here, we've got an annualized performance of 13.1% across that timeframe, which is an outperformance of 7.2% over the benchmark which in this case is the S&P/TSX. Turnover is 42%. So, again, turnover is looking at how often you are trading stocks from the model. At 42% you're going to be trading under half of the stocks, so call it maybe six or seven names on average in a year which isn't too bad in terms of trades on an annual basis.

A couple of other metrics I always like to look at – downside deviation which looks at the volatility of negative returns for the strategy is 10.2% and the benchmark is also 10.2%. So, that indicates they manage their downside volatility in a similar manner and a similar success rate. And then, lastly, of course, my favourite green and blue charts – how did the model do in both up and down markets. So, in up markets, the model outperformed 53% of the time, so a little bit more than the benchmark did. But in down markets, it outperformed 68% of the time. So, despite the fact that we're dealing with all these small-cap names, there still is some good downside protection compared to just owning names in the broad-based TSX which of course is more a large-cap set of names. So, even though in small-cap you may not know the names that you're looking at, there can definitely still be some value there. So, it might be worthwhile expanding your search to include companies that you haven't yet heard of.

For Morningstar, I'm Emily Halverson-Duncan.

About Author

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

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