How to find quality stocks at reasonable prices

How many stocks are there in the US stock market that are reasonably priced, earn a return on their invested capital greater than the long-term stock market return, and have very little debt? The answer is 57 (14/10/2014).

You can download a spreadsheet with my results HERE.

So why then do many mutual funds hold hundreds of stocks? My guess is that it has something to do with the principal and agent problem discussed in an earlier post.

In any case, there is only one way that a fundamental (not a quant) investor can create a portfolio containing hundreds of stocks and that is to be indiscriminate about what they invest in! Perhaps indiscriminate is too strong a word, but I couldn’t think of a better one.

It makes me wonder, can a manager have an equally strong level of knowledge and conviction about hundreds of stocks? Probably not. In that case, what are all of these stocks doing in many portfolios? And why is anyone surprised that portfolios, with hundreds of stocks that are frequently turned over, generating transaction costs and taxes – underperform the stock market?

Zacks has an online screening tool for US stocks that covers over 7000 stocks. I used it to create a screen that would identify stocks with the following characteristics.

  • Avoid stocks with small market capitalizations as they may be difficult and more expensive to trade (i.e. wide buy and sell spreads) due to lower market liquidity.
  • Look for stocks with very modest levels of debt. Companies with too much debt carry a higher risk as they may lose control of the business to debt holders if they unable to make interest and principal payments or satisfy loan covenants.
  • Identify stocks that are cheap on a price-to-cash-flow basis. Cash flow is harder for management to manipulate (as opposed to earnings) and is a better representation of what’s actually available to pay dividends or reinvest in the business.
  • Avoid stocks are “cheap for a reason”, by focusing on companies that are profitable and that earn a return on invested capital at or above the long-term return on US shares.

I decided to identify stocks with these characteristics by creating a custom screen using the following 5 variables and constraints:

  • Market capitalization ≥ $2 billion
  • Debt to total capital ≤ 25%
  • Price-to-cash-flow ≤ 10×
  • Current return on investment (ROI) ≥ 10%
  • 5-year return on investment (ROI) ≥ 10%

My screen returned a list of 57 stocks that met the criteria above, which I downloaded as a CSV file. I sorted the stocks by price-to-cash flow (PCF) and current (ROI), assigning a score to each company from #1 = best through to #57 = worst*.

5 fairly straightforward screening criteria was all that it took to whittle over 7000 stocks down to 57. This is interesting for 2 reasons.

  1. It demonstrates that there aren’t that many good investments (companies that are reasonably priced, profitable and with little debt) on the market at any one time. In fact, in a future post I will demonstrate how the majority of US stocks are actively destroying value for their shareholders.
  2. It helps to expose the myths that successful investing requires a large team of MBAs, CFAs and PhDs individually valuing hundreds of companies. The fact is that there are usually only a modest number of companies worthy of further research at any given time, a number small enough for even a well-organized individual investor to handle.

I added the two scores together to get a combined PCF and ROI score.

This the same method of combining two factors that Joel Greenblatt uses in his Magic Formula (although the two factors used by Greenblatt are different**). You can read about the formula in Greenblatt’s excellent book The Little Book That Still Beats the Market and you can use the Magic Formula for free on the Magic Formula Investing website.

The idea of my screen is to create a list of companies for further research.

I wouldn’t suggest using the screen as an investment portfolio, that is simply investing in each of the 57 stocks, for the following reasons.

  • It includes American Depositary Receipts (ADRs), or non-US stocks that trade on the US share market. You may want to exclude these.
  • Industry concentration. The screen may-over represent a particular industry, which may reduce the diversification benefit of spreading your money across 57 stocks.
  • Price-to-cash flow doesn’t factor in capital expenditure and changes to working capital. In other words it’s not the same as price divided by free cash flow to firm. An investor will need to factor these items into their analysis.
  • Company accounts may need to be adjusted before an accurate ROI can be calculated. For example, off-balance sheet liabilities such as leases, pensions and warranties need to be considered. Some expenses such as research and development or advertising may need to be capitalized, depending on the company. Then there are intangibles which also need to be considered.
  • Portfolio construction. Should an investor simply equal weight each of the stocks that they select? Or should they invest different amounts in each stock? If so, how will they chose which stocks to invest more in? How frequently will they rebalance? When will they sell?

You can download a spreadsheet with my results HERE.

A screen such as the one that I’ve described above is a good first step; however more work is required to select a portfolio of suitable companies.

An excellent selection of stock-picking screens can also be found at:

The American Association of Individual Investors



* “Worst” isn’t really an accurate description, as all 57 stocks are relatively cheap, have low-debt and are reasonably profitable, otherwise they wouldn’t have made the shortlist.

** I compared the output of the magic formula and my screen by asking the magic formula to find me 50 stocks with a market capitalization above $2 billion. There were 6 stocks in common, they were:

  • Apollo Group (APOL)
  • Babcock & Wilcox (BWC)
  • CA Inc (CA)
  • Coach (COH)
  • Gamestop Corp (GME)
  • Nu Skin (NUS)


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