Quant Concepts: Steady Momentum

Emily Halverson-Duncan filters for a portfolio of companies that can outperform in both up and down markets

Emily Halverson-Duncan 18 December, 2020 | 4:38AM
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Emily Halverson-Duncan: Welcome to Quant Concepts virtual office edition. Traditional momentum strategies are often associated with extremes, extreme ups and downs, extreme trading and extreme volatility. Typically, the investors of these types of strategies have a higher risk tolerance and are comfortable being active with their investing. While the long-term results can be quite attractive, the accompanying risk level may make these strategies perceived to be too much for the average investor to handle.

Today, I'm building a strategy that incorporates some key momentum factors while placing a cap on each stock's market sensitivity in the hopes of reducing volatility on the downside. The hope here is to create a model that benefits from the upside of momentum strategy while still having strong downside protection. So let's take a look at how to build that.

Jumping into CPMS. The first thing that we're going to do is rank our universe of stocks. And here we're using the CPMS Canadian universe. And today that has 703 names. In this ranking set, we're going to organize those 703 names and the factors we're looking at quarterly earnings momentum, which looks at the quarter-over-quarter growth of earnings, and you want higher values there. Five-year cash flow growth, again, we're looking for higher values. Return on equity, which is a profitability metric, and we're comparing that to the industry median. And then quarterly earnings surprise, which looks at whether or not a company has beat or missed on their expected earnings.

Once that's done, we move on to some of the screens that we're going to apply to the universe. And here we can see we've got a screen on again, quarterly earnings momentum, we want it to be greater than or equal to zero, meaning they're at least maintaining the same level of earnings or growing them. That five-year cash flow growth, we want to be in the roughly the top half of peers which has a value of 5.44% or higher. Quarterly earnings surprise, we want that to be greater than or equal to zero. So again, making sure they've either met expectations or beat expectations. We also have a couple of price change metrics worked in so one is the one day price change which we want to be greater than or equal to zero, meaning that their price from yesterday to today or across the last one day is increasing. And then their month-to-date price change also the same screen greater than or equal to zero. And the hope here is to catch stocks that are on the trend upward.

And then one of the key factors that we've got in here to help reduce sensitivity is the five year beta, which looks at a stock sensitivity compared to the S&P/TSX Composite at a value of 1 that's as sensitive as the market. And so here we have it capped at 1.1. So it's just allowing it to be a little bit more sensitive than the market, but overall trying to cap that and making sure that we're hopefully going to be reducing downside protection.

On the sell side, the factors we've got worked in are relative to those price changes that we talked about. So the one day and the one-month price change, if either of those declined below negative 15%, then the stock would be sold from the model. So we've got all of our rules and criteria set in and we can see how the model did across the long term.

Looking at the back test here, we ran a back test of 15 stocks from October 2002 until end of November 2020. And the benchmark we compared to is the S&P/TSX Composite. The strategy produced an annualized return of 21.4%. So very, very high return. And that was an outperformance of the benchmark of about 12.7% annualized. Turnover is a little on the higher side, again remember turnover is looking at how often you're trading from the model. So at an 80% turnover of those 15 stocks, you're probably going to be placing around 12 trades or so a year on average, it's a little bit higher than some of the ones we've looked at in the past. But you can see where that's come in in terms of the higher overall return as well. And then of course, we want to look at the volatility because we want to see if adding that sensitivity cap made this momentum strategy less aggressive and less risky in nature. So, I like to look always at downside deviation, the volatility of negative returns. For the strategy it had a value of 8.4%, and for the benchmark of 9.2%, so a little bit better for the strategy.

And then the other set I like to look at is this green and blue chart here, which looks at how the models done in both up and down markets. In up markets the model's outperformed 60% of the time, and in down market it's actually outperformed 83% of the time. So we can see that the combination of factors, both momentum and a little bit of risk reduction with beta have actually culminated in a very high returning strategy, but with very strong downside protection, which is ideally the goal. So this goes to show if you're interested in momentum investing, it doesn't mean that you have to take on excessive risks or excessive trading. There are ways to pare it down and ideally have a well-balanced strategy that you can use long term.

For Morningstar, I'm Emily Halverson-Duncan.

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

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