It seems intuitive that companies with high returns on the capital will perform better than their less efficient peers. I have been spending some time this week researching whether this supposition really holds true and if you can use it to identify stocks that have a high probability of increasing in value.
As usual, I have performed my research using the InvestorsEdge.net trading platform – just click here to see the strategy and more in-depth result and risk information together with the all 36 versions of the model that I researched to come up with the final figures.
You can see from the above graph that annual returns for the 8 years since 2010 have been 22.7%, which would have turned an initial £100,000 investment into £558,000.
More importantly, the high Sharpe and Sortino ratios (measures of risk adjusted returns) follow what can be seen on the drawdowns section of the chart – that apart from the Euro crisis of 2011 and the immediate aftermath of the Brexit vote those returns were achieved with very little in the way of volatility.
The strategy would have lost money in only one year out of eight, again due to the Euro crisis, and predominantly opens positions in Small-Cap and Micro-Cap stocks (those with a market capitalisation of £35m – £1.5bn.
How The Strategy Works
The strategy initially creates a universe of all UK stocks and depository receipts with a market capitalisation greater than GBP 50 million and a dividend yield of 3% or less.
The resulting stocks are then ranked by:
Return On Capital Employed (ROCE)
Return on Invested Capital (ROIC)
Free Cash Flow / Enterprise Value
Gross Income Change Over Previous Quarter
The strategy then picks the 30 stocks with the best combined ranking scores each quarter to invest in, assuming a £7 commission for each transaction.
Our strategy defines ROCE is defined as EBIT / Enterprise Value, and ROIC as EBIT / Average Total Assets – Current Liabilities over the last year.
Why The Strategy Works
The beauty of the UK Return On Capital model is its simplicity – it effectively has four moving parts that fit intuitively together.
The first moving part identifies companies that utilise their capital efficiently by ranking those with the best returns on capital employed and invested capital higher. A company with high returns on capital can reinvest more capital back into the business, which helps to produce higher earnings per share.
Secondly, the strategy selects companies with higher free cash flows when compared to their enterprise values. This may seem counter intuitive as it ranks expensive companies higher than cheaper ones, but in this context the factor says two things about a stock. Cheap stocks are normally cheap for a reason (i.e. they are distressed) so the factor steers us clear of troubled companies, and secondly higher valuations may be a sign of recent price momentum, which the strategy assumes will continue and thus takes advantage of.
Giving weight to the momentum play inherent in the free cash flow factor, the model also selects stocks that have shown the highest recent growth in gross income. As well as pushing us towards investing in the fastest growing companies, this factor adds a timing element to our purchases.
Finally, limiting dividend is another seemingly counter intuitive factor in our model. Dividends are great for investors as you get paid while you watch your capital grow, but a high dividend yield is also a sign of distress, as in all probability the yield is a result of the price going down rather than the returns to shareholders going up.
A key risk that I always examine with mechanical investing strategies is that the data phenomenon that I am exploiting will simply stop working. To combat this, I look to see if a strategy intuitively makes sense – our model invests in companies that make the most efficient use of their capital and also has experienced recent income momentum.
Entering positions in stocks can be easy, but getting out again can be considerably more costly in a downturn. The InvestorsEdge platform automatically checks for liquidity levels during backtests and rejects stocks which I wouldn’t be able to buy in the real world, so this risk is partially mitigated. Dangers still remain on exit however, as the typical company size for positions is £250m – £2bn leading to the potential for high costs if I had to sell these stocks in a hurry.
The macroeconomic climate can have an impact on any strategy and this one was not immune to the 2008 market crash. As seen from the drawdowns chart below the strategy would have bounced back from its lowest point to pre-crash levels within 18 months.
Data anomalies are also a potential risk. Sometimes we data mine and exploit an anomaly that doesn’t repeat itself. I tend to look at the fact the strategy makes sense and has fewer working parts to see if this is the case and I test using different time frames to identify data mining. If I am happy that the strategy is robust we will then run it using a theoretical pot of money to see if it performs in line with the backtests.
My UK Return On Capital strategy is a great example of combining efficiency ratios with momentum factors to create a strategy with highly consistent returns.
I have been so impressed with the strategy’s return profile I will be investing of my own money in it from this month. If you want regular updates on how I am getting on, or would like to take this model and use it yourself, just click on the Start Free Trial button below to test drive the InvestorsEdge platform for 30 days.