OTC Newsletter Interview With Ruerd Heeg

by: Jan Svenda

Summary

Below, you can read another interview that was conducted for my newsletter.

This time with an SA contributor and quantitative value investor Ruerd Heeg.

We have a chat about his strategies and the way he goes about creating such strategies and maintaining them.

This is only an excerpt and does not include Ruerd's take on OTC.

Dear members of the OTC Bi-Weekly Newsletter,

I am delighted to present another interview. This time with Ruerd Heeg, an SA contributor who focuses on quantitative value strategies. His strategies also venture into the OTC world. We chat about his diversified approach and why does he think the strategies will have a lasting value for his portfolio.

I hope you will enjoy the discussion.

How would you describe your investment strategy and what are the most important factors in it?

I try to find cheap stocks based on favorable fundamental and statistical properties. For instance, favorable fundamental metrics are low EV/EBIT and high NCAV/Market cap. Examples of favorable statistical properties are smooth momentum and low liquidity. A difference with what others do is I do this for global stocks, so across many different stock markets. That provides more diversification.

Another difference is I combine multiple measures into one measure of cheapness. I sort my stocks on this new measure. This is much different from conventional screening for 2 reasons.

First, I compare many more stocks. In total, I compare over 3000 stocks, twice a month. I end up with 6 shorter lists of 10-50 of the cheapest stocks: one for each strategy I implement.

Second, screening highlights stocks doing excellent on one single criterion, such as EV/EBIT. Ranking stocks on multiple combined criteria also compares these partial winners with stocks doing well on multiple criteria. A good analogy is comparing students using their marks. Ranking compares students on their average marks, while screening only compares students on their marks for one particular subject. A student can have a high mark on one particular subject but his average can still be low. And the student with the highest average does not need to have the highest mark in any of the subjects.

So I use ranking to implement 6 quantitative strategies. Four of these strategies are described here.

An important factor is that I have not invented them myself. Each of these strategies is based on scientific publications describing backtesting research.

To be more concrete, an important factor in each strategy is liquidity. On average less liquid stocks have higher returns. Two strategies also use smooth momentum combined with value measures. One of these momentum strategies is restricted to stocks with a market cap below $300 million. Another strategy also uses the market cap as a metric, combined with value metrics. That one is restricted to stocks below a market cap of $30 million. I also implement a “falling knife” strategy focusing on cheap stocks having decreased 60% or more during the last 12 months. And then I have the net-net strategy and a strategy combining moderate to low EV/EBIT with various other value measures.

After generating my lists of ranked stocks the next step is doing basic research on these stocks. I look up and check basic facts, like the type of business, what the company expects going forward, whether there are any multi-year metrics suggesting durable competitive advantages, who owns the company, whether there have been fraud allegations, what the balance sheet looks like (in particular debt and leverage), an estimate of the liquidation value, when the company paid out to shareholders and when they diluted, whether there have been value destroying transactions, whether there could be any hidden assets or liabilities and whether certain assets could be more or less worth than their book value.

Then my research shows certain stocks in my list are even cheaper than my automated comparison suggests, and most stocks are more expensive. In particular, I find the first type of stocks attractive since these companies are often overlooked.

What kind of process do you have in place to ensure the objectivity of your research process?

I do not spend multiple days on each stock. In my research, I limit myself to always the same key facts: those key points I have just mentioned. Also, I investigate many stocks per month. I try to present my research such that it is easy to compare stocks.

What is the position turnover like in your strategies? Do you have a rebalancing process in place?

I do not rebalance. Some positions can only be sold after 2 years while other positions can already be sold after a couple of weeks. Unfortunately, there is hardly any scientific research into optimal selling strategies. Depending on their valuation I sell positions at 50 or 100% gain. Otherwise, I sell after about 2 years. If they are still very cheap compared to other stocks I keep them a little longer. In rare cases there is an adverse event changing the valuation from extremely cheap to expensive. Then I sell as well.

Selling at 50% or 100% gain has the advantage of reducing downside risk. After selling early I can sometimes buy back for less. On the other hand, some stocks can have large outliers, especially in bull markets.

Stocks with strong and smooth 1-year momentum I hold shorter. Stocks from my 2 momentum strategies have already a strong and smooth 1-year momentum when I buy them. I think the optimal holding strategy for these stocks is much shorter, maybe about 6 months.

Did you start investing with a quantitative value approach from the beginning or did your style evolve?

When I started my newsletter I researched low EV/EBIT stocks with strong balance sheets and net-nets. I found these stocks with normal screening. That evolved: I applied criteria to filter out dilutive stocks and potential frauds. Only last year I started doing a quantitative ranking of stocks. I started with a quantitative ranking of low EV/EBIT stocks, then did ranking for the net-nets and then developed 4 more quantitative strategies.

If your style evolved, how did that come about?

In practice, a quantitative ranking is not easy to implement. I started doing this with currencies collecting the fundamentals and price information manually. I still do this, see here.

With stocks you cannot do this since there you need to collect 10 thousands of data items per month. But I found a way to get this data and feed it into a computer program. Once I was able to do that for one particular strategy it was relatively easy to find and implement other strategies with returns of more than 20%.

I found 2 of them in the book What Works on Wall Street. Two other strategies described in papers suggested by a subscriber. And I had already discussed with him what science says about important factors for returns of net-nets. Ranking net-nets was just a matter of implementing these conclusions.

A significant amount of academic papers point to interesting performance results within this investing approach. How do the results from the real world compare to the backtests?

I have not been doing this for a long time, certainly not as long as in the back-tests. Also, my strategies use the findings from these back tests, but they are not the same as in the back-tests. For instance, many studies confirm low P/B investing works well but there is also a study telling us you should use low Market cap/Retained earnings. So, it makes sense to use that instead of low P/B. However, retained earnings cannot be retrieved in an automated process so I use 8 years of earnings minus pay-outs instead.

But I can confirm key findings, especially for investing in net-nets which is something I am doing now for 5 years. Net-nets indeed perform great, but it makes sense to avoid financially distressed companies. Therefore, I have designed my rankings to do this.

It also makes sense not to look for much comfort: loss-making net-nets are better than profitable net-nets. Especially failed biotechs can be great investments. The price of a profitable net-net also typically declines much as soon as the company is not profitable anymore.

Note by Jan: I suggest readers check out this presentation by Tobias Carlisle. Tobias provides further support for this assertion.

Other findings I can confirm is that very small stocks have higher returns, on average, and stocks with a low Market cap/Retained Earnings as well.

I also think it is best to avoid stocks with a growth story or a widely expected favourable event in the near future. The fact that growth stocks on average do not perform as well as other stocks is well established in the literature. In 2011 someone also found event stocks probably perform sub-optimal, see the frog-in-the-pan paper.

Why do you think the strategies have been working so far?

Many stocks I research are pretty obscure. Not many people want to invest in them. Most people prefer to invest in Tesla or in dotcoms, like I did many years ago. My stocks typically do not have attractive events attached to them either. Not so many people can compare them with other stocks. And they are too illiquid for the big funds.

But usually in 1-3 years something happens that allows me to grab my returns. That can be a fundamental change but it can also be a reaction to a favourable event.

How do you think about the long-term feasibility of strategies? Is there anything other than performance that you are tracking in order to ensure that the strategies remain profitable?

If the history of value investing has taught us anything it is that it can be applied during any period. So I am not worried about the long-term feasibility of strategies. It could be that a particular strategy, sector or country performs worse for some time. That would not be a problem because I use 6 different strategies, without much overlap. Therefore, apart from the performance I track sector, geographic and strategy diversification of my portfolio.

Do you actively employ new strategies or are you spending more time fine-tuning the existing ones?

Neither one. I now have 6 strategies, more or less fine-tuned. I think that is enough, especially since these strategies have little overlap. For the next couple of years I will stick with these, if not for the rest of my life. Of course, I will stay flexible and I may change or fine-tune if new scientific research suggests improvements. Another reason for change might be the availability of better data.

Do you think that there any obstacles left for the average investor in employing the strategies? I.e. is there something that could hurt the performance of the strategies over the long run?

When a strategy shows bad performance for one or two years most, if not almost all, investors quit. That is the biggest obstacle. That could also happen with my strategies. For instance, US-listed net-nets have been performing badly in the 70-ties for 2 years in a row and at the beginning of the financial crisis. But if you diversify internationally with my 6 different strategies I think it is possible to increase portfolio robustness.

How much time do you spend cleaning fundamental data for the stocks picked for the strategies?

My algorithms take the data as it is. Only when I research a particular stock I try to find data suggesting that stock is cheaper or more expensive than my fundamental data suggests.

BTW, I do not really “pick” stocks. I describe the highest listed stocks from each of my 6 quantitative strategies and let my subscribers take their own decisions. I provide basic research on many stocks: 20 per month. As a result, my subscribers can find research for most stocks that are high on my quantitative lists.

You actually wrote two books about investing in Dutch. The first one was published in 2013 and the other in 2014. Was there anything that you would change in the publications?

They are both great books. They contain lots of facts and examples of valuation, stock statistics, investment psychology, accounting and investment strategies. If I would write another book I would focus on “maximum pessimism” though. I do touch it briefly in both books but it deserves to be in the spotlights since investors can make so much money from it.

What exactly do you mean by maximum pessimism?

Maximum pessimism describes emotions of other investors. It is difficult to describe, so ideal for discussing in a book. However here I will try to say something about it in a few sentences.

Obviously, it is the opposite of maximum optimism at bubbles. Often it occurs at a new and unexpected negative event happening after a series of other negative events. Humans are good at extrapolating trends, so almost everybody is too pessimistic after a series of negative events. If most of these negative events cause a price decline then we might have maximum pessimism when the latest, unexpected negative event also causes a new price decline.

For example, think of oil in 2015/2016. The Saudi’s said it would go to $50 per barrel. It did, and then it went to $25. When oil was around $30 I bought 3 oil exploration companies that were net-nets. I could sell each stock for 50% gain within a year.

What was the most obscure quantitative strategy that you have encountered?

I try to stay away from quantitative strategies that do not make sense intuitively. For instance, recently, I saw a paper on excess returns after big trading liquidity. That result seemed questionable to me and after careful inspection it turned out the effect was not so big, and the study period was not long enough. Another example is for instance the turn-of-the-month effect. This effect is well researched but I think it is difficult to make money from it. The effect is not so big and exploiting involves much trading.

What is your opinion of ETF based on broad market indices (S&P500 etc.)?

I like ETFs with low P/E and ETFs at 5-year lows. Think of country ETFs. I think these perform much better than the broad market. I guess these are difficult to find right now, if there are any at all. I cannot recommend investing in the S&P 500, since the US stock market is at such a high level.

Do you think that one should time entry into your strategies?

I do not think that is necessary. I expect a mix of these 6 strategies to do well within every 5-year period. Of course, returns will be lower for the unfortunate getting in just before a market crash. And my individual strategies have their own timing issues. For example, after a market crash it is better to hold stocks from the momentum lists longer than 6 months. Also, I expect falling knives originating from a market crash to perform very well.

Why do you think that quantitative approach is not more common in investing?

Most investors cannot do it themselves but have to rely on services from others like my newsletter on Seeking Alpha. That is the first barrier. Many of these services focus on quantitative lists of stocks. But that is not what people like to use when they decide which stock to trade. They like to trade on stock research. So I do not think there are many of these services where investors exactly get what they want. I hope mine is the exception. At least, while I provide 6 quantitative lists twice a month the focus is still on stock research.

Could you recommend some books that you feel have influenced your thinking about investing?

A great book on investing is “What Works on Wall Street” from James O’Shaughnessy. This is based on very thorough research. I also recommend it because it presents so many statistical findings in one book.

Great, thank you Ruerd for the insightful interview! I hope our readers have enjoyed it as well.

Feel free to leave any comments below and don’t forget to check out Ruerd’s Seeking Alpha profile.

If you liked this interview do not hesitate to check out my website and learn about my newsletter and the OTC database that I am building.

Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.