Quant Concepts: Value traps

Difficult to discern and potentially dangerous to portfolios - how to scan for value stocks at risk of decline with CPMS's Emily Halverson-Duncan

Emily Halverson-Duncan 7 February, 2020 | 1:50AM

 

 

Emily Halverson-Duncan: Welcome to Quant Concepts. Most of the time we focus on what stocks we should invest in. But in practice, it's also very important to consider what stocks to avoid. One of the most common possible downfalls investors can experience are stocks currently undervalued and that investors expect to increase otherwise known as value stocks, but instead continue to decline in price, more commonly known as value traps. Value traps are both difficult to discern as well as potentially dangerous for your portfolio. Even after they begin losing money, investors may continue to hold on to them in the hopes they will eventually rise, thereby potentially increasing their total loss.

Today, I'm showcasing a strategy that searches for possible value traps within the Canadian CPMS universe. The purpose of the strategy is not to give ideas of stocks to purchase but instead looks for stocks to avoid. So, let's take a look at how to build that.

First off, as always, we're going to rank our universe of stocks, which here is about 704 Canadian stocks. How we're going to rank them are by price to book value. So, that's looking at a value metric. The lower the value metric, the better it is in terms of valuation. Quarterly earnings momentum, which looks at whether or not a company is growing their earnings or not quarter-over-quarter and higher values for that are preferred. Three-month earnings per share estimate revision, which looks at whether or not a company's estimates for their earnings are moving up or down across a short timeframe, as well as quarterly earnings momentum and quarterly earnings surprise, both a momentum metrics.

On the screening side, once we've organized our universe, we're going to screen for stocks that have a low price to book value, so in the bottom half of peers. Today, that's a value of 2.31 times or less. We want a positive quarterly earnings momentum. And then, we want them to have at least some liquidity, so we're putting in a value traded filter as well.

On the sell side, we're going to sell stocks if that quarterly earnings momentum falls below negative 7%, meaning that their earnings are dropping by more than 7% quarter-over-quarter. And now, we want to see how that model did. So, notice here, we used a lot of valuation metrics mixed with momentum metrics, but there's really no quality or risk overlaid on top of that. So, we'll see how that did in our backtest. Here we are running 20 different Canadian stocks. So, what you're looking at are the top 20 stocks that qualified roughly 15 years ago, and then applying those buy and sell metrics we just went through on an ongoing basis.

Okay. So, we can see that backtest here was not very good. Annualized performance was 2.7%, which actually underperformed the benchmark by 4% and had very high turnover, again, turnover is how often you're trading stocks through the model, at about 150%. So, on 20 stocks, you're probably trading about 30 times per year.

Again, one of the interesting things to note about the performance is, we bought a lot of undervalued stocks, which might look like a good idea. They had good momentum indicators. But clearly, compounded together that actually didn't result in any good performance numbers and a good portfolio.

Looking at some of the risk metrics, we can see standard deviation. So, the volatility of returns is 22.9 for the strategy, and for the benchmark is 12.4. So, you're almost doubling there. Downside deviation, which looks at the volatility of negative returns, is 16.3 for the strategy and 8.8 for the benchmark. So, again, almost double the volatility, whether you're looking at all returns or just negative.

My usual favourite chart I like to look at, how did the model does in up and down markets. So, in up markets, it outperformed 69% of the time. So, it's actually not too bad, but in down markets, it only outperformed the benchmark 15% of the time. So, you can see in more volatile markets, this is where the model is doing so poorly and that's compounded into a very low overall return.

For Morningstar, I'm Emily Halverson-Duncan.

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