In this new series, I will interview different investment managers and document their unique strategies. The following content is based on my conversation with Kevin Vandermeer, who manages the Resilient Canadian Equity Trend Fund.
Kevin is an accomplished investment manager who won the Lipper Award and the TopGun Award in the past for his “fundamental approach” to investing. The fundamental approach (or fundamental analysis) is synonymous with value investing; that is, the approach involves trying to understand the actual business underlying each stock. So I was surprised when Kevin told me that he launched a fund utilizing Trend Following.
Trend Following is a rule-based strategy that relies purely on price history to generate buy and sell signals. The strategy is similar to momentum investing with respect to its sole reliance on price history, but the actual rules are different.
Momentum investing works by ranking each stock in the market by their recent performance, and investing in those with the best performances. When virtually all stocks are down (as was the case in 2008), momentum investing will still recommend buying stocks with the best recent performances, even if those performances are negative. You could say that momentum investing is based on the relative performance of each stock.
Trend Following, on the other hand, is based on the absolute performance of each stock. There are different variations of the Trend Following strategy. Kevin’s strategy involves buying stocks if they satisfy the two conditions outlined below.
One, Kevin calculates two moving averages for each stock - one over a shorter period, and the other over a longer period. A moving average is simply the average price over the specific timeframe. For example, if a stock’s price was $18, $19, and $20 over the past three days, then the three day moving average is $19. For Kevin to buy the stock, the moving average over the shorter period has to exceed the moving average over the longer period.
Two, a stock’s price has to be above where it was six months ago. For example, if a stock’s price was $10 six months ago, then Kevin would only buy the stock if the price today was above $10.
When a stock meets both of these conditions, Kevin submits the order to buy it. When he does, he also orders a “trailing stop” at 15% below the stock’s price. A trailing stop is a standing order that tells the broker to sell a stock when the price reaches a predetermined point. For example, let’s say that Kevin buys a stock that’s trading at $100/share, and submits a trailing stop at $85. Then if the stock price goes down below $85, the broker will automatically sell that stock.
If the stock price goes up instead, then the trailing stop price goes up with it. For example, if the aforementioned stock price goes from $100/share to $110/share, then the trailing stop price moves up to $110 * 0.85 = $93.5/share.
When I asked him how he allocated his portfolio, Kevin said he followed a rule that gave more weight towards stocks with lower volatility. (Read my free book to see what “volatility” means.) Owning more lower volatility stocks reduces the overall risk of his portfolio, and it also allows him to benefit from the tendency of lower volatility stocks to outperform the market.
Since every aspect of his investment strategy follows hard rules, Kevin naturally uses a computerized algorithm to execute his strategy. However, there are times when he manually intervenes, such as when a stock gets taken over by another company.
When I asked Kevin why he chose to implement the Trend Following strategy instead of a fundamental strategy, he replied that the “backtested” results looked better for the Trend Following strategy. Backtesting involves looking back at the stock market throughout history and analyzing whether the strategy would have performed well, had it been used.
The backtested results on the Trend Following strategy do look great. According to Kevin’s research, the strategy would have yielded a positive return of 14% per year from 1998 to 2016. This compares favourably to the performance of holding a Canadian stock market index fund, which would have yielded roughly 7% per year. Kevin only invests in Canadian stocks, so the Canadian stock index fund would be an appropriate benchmark.
Also, whereas the investor who persistently held a Canadian stock market fund would have experienced a maximum loss of 43% in a single year, an investor who followed Kevin’s strategy would have lost a maximum of only 15% in a single year instead.
Kevin’s research is also corroborated by academic research. For example, Greyserman & Kaminski documented how effective this strategy would have been using data going all the way back to the 13th century.
Although the backtested results are not in doubt, I wondered if there was a danger that the strategy would inexplicably stop working. When I asked Kevin why he thought the strategy worked, he said he believed it was mainly due to human behaviour.
Humans have a tendency to anchor their perception of a stock’s value around the current price. If a stock trades at $200/share, people may think the price is cheap once it goes to $180/share, and expensive if it goes to $220/share. Thus, humans have the tendency to buy a stock if it dips a little, and sell if it rises a little.
However, news regularly hits the market that affects the intrinsic value of a stock by a larger magnitude. We can see a real example of such behaviour with Valeant Pharmaceuticals.
In its heyday, Valeant was the largest company in Canada, and had a stock price of over $300/share. When U.S. politicians called for Valeant to be subpoenaed over its business practices on September 28, 2015, Valeant’s stock price fell from $200/share to $167/share in a single day, which was a loss of 16%.
If you thought that Valeant was cheap after such a sharp fall, you would have been wrong. The stock continued to slide afterwards, and is priced at under $15/share as of the time of this writing. Clearly, the stock market under-reacted to Valeant’s news on September 28, 2015. Whereas human beings might have held on to Valeant’s stock, an algorithm that followed Kevin’s strategy would have sold out of Valeant soon after it started to fall.
As long as humans are in charge of investment decisions, behaviour biases will, in theory, create opportunities such as one that Kevin is attempting to exploit. But the counter argument to this is that once a large enough number of people become aware of a successful trading strategy and start using the strategy, then the strategy no longer works. When I asked Kevin his thoughts on this argument, he said he wasn’t worried because there’s no obvious way that the strategy would no longer work.
To understand his reasoning on this point, let’s imagine that everybody does follow Kevin’s strategy. In this case, everybody will buy the same stocks at the same time, and sell the same stocks at the same time. Since everybody buys at the same time, the price of the chosen stocks will shoot up to the moon, which will make everybody’s performance look great. Therefore, it’s not clear how more investors using the same strategy will cause the strategy to stop working.
As with momentum, I think there’s a good chance that Kevin’s strategy will continue to work. Although it’s not an approach that I would incorporate into MoneyGeek (I’m too much of a die-hard value investor for that), I can see how it would appeal to some investors. I would like to thank Kevin for taking the time to chat with me, and I wish him all the best.