A writer of financial blogs has basically 2 choices. A writer and newsletter author can try to be useful for other investors or he or she can try to write stories that others want to read: entertaining stories. I must confess that I tried to do the latter in the beginning, though not very hard and only as a writer, not as a newsletter author.

Now I write about stocks that are ignored by the large investing public but that still can have great returns. Because these stocks are often unknown, obscure, and sometimes disliked they have low prices. And what is cheap usually has great returns in the stock market.

Consistency in research

The best investors are usually very consistent in their sourcing of investment ideas and in their research. I try to mimic their consistency. My sourcing of investment ideas is very thorough and consistent as I will explain below. Furthermore I write down the same points for each stock I look into: information for judging cheapness, governance related information, balance sheet related information. payouts to shareholders over the last 10 years, and substantial shareholders. Can be boring but it makes it easier to compare stocks.

Unfortunately because my research is often boring to read it can be difficult to publish it on websites like Seeking Alpha and maybe even here. That’s called editor’s selection bias.

But I keep trying to publish “boring stuff” as long as the investment value is there. I hope more and more readers of financial blogs will recognize investment value of financial writings is more important than entertainment value.

Unlike many others I do not invest based on conviction. Instead I hold many small positions. What these positions have in common is they were great stock market bets on a statistical basis when I bought them.

Great stocks on a statistical basis

So what is a good stock market bet on a statistical basis? If you ask this question to 10 different investors you will get 10 different answers. Some people like low P/E stocks, some like low P/B stocks or other favorable metrics such as low P/Free cash flow. Other people like stocks with payouts: high dividend yield stocks, stocks of companies buying back shares, or dividend growth stocks.

Many investors look for earnings quality and growth, which are subjective measures of course. Another approach is looking for the cheap stocks in certain sectors, like insurance companies, real estate, branded consumer staples products, oil, biotechnology, etc, etc.

I do not do any of this. Instead I look for good stocks from quantitative strategies from the literature. So for me a stock is a good bet if it satisfies many properties of a high returning stock strategy published by a scientist.

Because I only want the cheapest stocks I look for stocks globally. I think investing globally helps in reducing risks and also improves returns.

What is a quantitative strategy?

A quantitative strategy combines several metrics in a pretty intuitive way. Because of the combination of several metrics quantitative strategies typically have higher returns and lower risk than simple screening strategies based on a single metric.

Suppose you like low P/E stocks AND high dividend yield stocks. A quantitative strategy first sorts all stocks on P/E. Every stock gets a number. The stock with the lowest P/E gets rank number 1, the stock with the one but lowest P/E gets rank number 2 and so on. Then sort the stocks again, now on yield. Again the stock with the highest yield gets rank number 1. Now add the 2 rank numbers for each stock. So a stock with rank number 3 on P/E and rank number 200 on dividend yield gets assigned 206 as the third number. Then sort the stocks again on this sum. The new sorted list is your quantitative low P/E – high yield strategy. The stocks with the best combinations of P/E and dividend yield have the lowest combined rank numbers.

Better metrics for quantitative strategies

According to scientific literature stocks with high earnings do well. It turns out P/E is not the best metric. A better metric is Enterprise Value/Earnings before Interest and Tax. See here.

Low P/B is used very often in the scientific literature. However low P/Retained Earnings is better. Management of public companies is much more careful investing earned money than investing money from investors. This is also supported by the fact that stocks with a high increase in total assets perform worse. Moreover stocks with a high increase in total assets perform even worse in countries with little restrictions on dilutions: the US and Hong Kong.

For global stocks retained earnings is not available. So I use Market cap/Retained earnings from the last 8 years. I think this is at least as good. Management is more careful with money earned relatively recently, when they managed the company themselves instead of predecessors, than with money earned a long time ago.

Stocks with high payouts perform better than stocks with low payouts. And stocks taking on new debts perform worse than stocks reducing debts. See here. So I put dividend payments, buybacks and debt reduction into a single Total Yield metric. Because I think debt reduction is less predictive for future returns than buybacks and dividends the latter 2 have a heavier weighting in my combined metric.

Not many investors use it but stocks with low trading liquidity perform a lot better than liquid stocks. My metric here is 3-months average trading volume/Public Float.

I believe using the Piotroski score in rankings improves returns. This has been demonstrated for low P/B stocks and for small stocks. Wesley Gray improved the Piotroski score into a Financial Strength score. In his book Quantitative Value with Tobias Carlisle they also showed their score improves returns of low EV/EBIT stocks.

Quantitative Strategies

Here is a summary of the strategies I use.

1. Stocks with significantly lower market cap than current assets net of liabilities. This is the well known net-net strategy.

2. Stocks with a market cap below 30 million USD (nanocaps) with low Market cap/Retained earnings, over the last 8 years and low EV/Revenue. Here I exploit the so-called size effect which is much stronger for the very smallest companies.

3. Stocks with low EV/EBIT and several other basic multiples. Similar strategies are described in What Works on Wall Street and James Montier’s book Value Investing: Tools and Techniques for Intelligent Investment.

4. Stocks with low EV/EBIT, good Financial Strength and several metrics suggesting high earnings quality. This is my global implementation of the strategy described in the book Quantitative Value.

5. Microcaps (market cap below 300 million USD), with low Market cap/Retained earnings and good momentum. This is based on the highest returning strategy in the book What Works on Wall Street. And it has low risk too.

6. Stocks with low EV/EBIT and several other basic multiples and good momentum. This is based on another great returning strategy from What Works on Wall Street. Again with relatively low risk.

7. Falling knives: Stocks having gone down with at least 60% in the last 12 months with low Market cap/Retained earnings and low EV/Revenue. These stocks perform even better when their share price decline occurs in times of high market volatility. See here.

Each of these strategies also uses other metrics such as trading volume/Public float, Total Yield and Financial Strength.

Examples

A net-net with a favorable ranking is CosmoSteel Holdings trading in Singapore with ticker B9S. The company trades “steel pipes, fittings, flanges, cables and structural” as a wholesaler. The company also provides customization services for adjusting dimensions of products. The company sells products to the energy and marine sectors, mostly to Singapore, Japan and Vietnam.

The company paid out dividends in the past but did also a couple of big dilutions. I think financial distress is a negative for the returns of net-nets, nanocaps and falling knives. So it is with net-nets very important to get an idea of risks associated with too much debts and a weak balance sheet. The problem with this company is that there are significant debts. But because these debts are mostly secured I suppose they can be refinanced. So I think the company is not financially distressed.

Another thing with CosmoSteel are the related party transactions. The company does a lot of business with its Japanese main shareholder. Because there are also other large shareholders, with board positions, I suppose these transactions are conducted on normal terms.

A good nanocap is DnxCorp Se, at least on a statistical basis. In September the stock downlisted from the Euronext Paris to the Euronext Growth Paris. The company is based in Luxembourg. Currently the company describes itself as follows:

“The company specializes in the development and enhancement of audience on the Internet. DNXcorp integrates all the key competences of the web: traffic generation, development of sites and services, multi-country payment solutions, video streaming technology, CRM analysis. This global expertise allows the Group to develop since 2000 its own sites in dynamics such as entertainment or meetings.”

I think they mean with “entertainment” adult entertainment and I also think they operate erotic dating sites. Again, the cheapest companies are often avoided for other reasons than their returns.

The stock trades close to its 5-year low, suggesting better statistical returns as well.

Revenues over the first half of 2019 were much lower than over the first half of 2018. That might have been the reason for a lower stock price. However the company is still almost as profitable as in 2018 because certain loss making businesses have been abandoned.

The company continues to pay dividends: the proposal for the next annual meeting (in 2020) is a dividend of 0.21 EUR over the first half of 2019. Of course a bird in the hand is worth two in the bush, but don’t worry: this company has always paid fat dividends except for in 2015. In 2015 it still paid a dividend, just a smaller one.

This stock is pretty difficult to analyze because annual reports are in French and this report cannot be machine translated with Google Translate. I try to understand it but my school French is not great and very rusty so my apologies if I am wrong.

Based on the balance sheet on page 65 of the annual report over 2018 I think the balance sheet is strong with moderate leverage and few debts.

The dominant shareholder is also the CEO. He got paid over 700k EUR in 2018. That is much more than the about 200k EUR for the second man. There are 2 outsiders with significant positions in the stock.

Ever heard of Transneft? Recently Interactive Brokers allowed trading in certain stocks listed in Moscow. One of them is Transneft, with ticker symbol TRNFP (non-voting shares) and a market cap of about 3.7 billion USD. Transneft PAO mainly transports oil and petroleum products through pipelines within Russia. The Russian state owns all voting shares.

Transneft is the best stock according to my low EV/EBIT strategy, so the third strategy in the list above. It is cheap based on P/Free cash flow, EV/EBIT, EV/Revenue, dividend yield and P/Retained Earnings. It also has a strong balance sheet. A negative is the high interest rate it pays on debt.

There have been governance issues with this company. Among others according to Wikipedia a minority shareholder exposed contracting fraud in 2008. A company spending a lot on many big infrastructure projects is ideal to steal money from, especially in a mafia state. But in the mean time the yield is about 7%.

A well-known but not much loved low EV/EBIT stock is Surgutneftegas or Surgutneftegaz. This Russian oil and gas company is sitting on an enormous pile of cash. There are preferred shares and the ADS of ordinary shares trading in the US with tickers SGTPY and SGTZY respectively. There are also shares trading in London.

Until 2015, when oil prices were high, the yield on the prefs was 20%. But people could have made money on the ordinary shares as well. After oil prices crashed in 2015 the ordinary shares went down along with it. But then suddenly, with no apparent reason, the ordinary shares popped with more than 30%.

I wrote about the company several times for my subscribers. It did not do very well but the returns are still positive, thanks to the recent pop.

You would not expect it from a large Russian oil and gas company but the quality metrics suggest good earnings quality and/or good asset allocation. So this stock also fits my implementation of the strategy from the book Quantitative Value. Moreover it would be easy to improve these metrics: just pay out the damned cash!

Deutsche Real Estate is a microcap trading in Frankfurt with ticker DRE2. This real estate company invests in commercial real estate such as offices, business parks, logistic centers, and locations for retail businesses. In March 2018 I looked into it because it satisfied the conditions of my low EV/EBIT + momentum strategy.

Surprisingly it is still a good momentum stock. Momentum often reflects knowledge of informed investors slowly bidding the stock up. And indeed that happened: the stock went from 3.3 to over 10 EUR.

The stock price increase indeed turned out to be based on improving fundamentals. In March 2018 it was trading below book. Right now it is still trading below book. The P/B has even decreased! The reason is the company booked massive paper gains: fair value increases of its real estate. If the company’s judgment was correct with these fair value gains this is still a very good low valued momentum stock. Chances are small the company books big fair value gains again. So it is a good momentum stock but not as excellent as it was before.

Final words

With quantitative ranking algorithms on global stocks investors have much more choice when investing in cheap stocks. Using several investment strategies is a good way to diversify. Investing globally using ranking algorithms also results in cheaper and smaller stocks in your portfolio. These algorithms also do a much better job in comparing stocks on cheapness than most investors. For small investors I still recommend supplementing information from such algorithms with research into individual stocks. Such research can reduce risks further.