Value Investing With Equities Lab


A very simple stocker is presented that very effectively produces value-based stock returns. The screener, with realistic assumptions, produces 25% annualized returns with a standard deviation of 26% from 1995 to 2015, as backtested in Equities Lab.


Value investing is a framework of investing that selects stocks that have low prices compared to their intrinsic value [1]. The hope is that the stock price comes to reflect the intrinsic value after the investment is made . A number of metrics are available to the general public and through Equities Lab that help identify potential value stocks. These are ratios such as:

  • price / earnings
  • price / book value
  • price / sales
  • price / free cash flow
  • price / cash flow
  • enterprise value / EBITDA

The debate is open about how to best identify value, but this blog post does not attempt to resolve this. Value investing requires the mettle to invest in out-of-favor companies whose stock price has suffered unjustifiably. Value investing also requires the mettle and patience to endure large swings in portfolio value. This year (2015) has been a particularly mettle-testing year for value investors, but Equities Lab and a mountain of academic and applied research ([2] for instance) gives one the confidence in the method for the long term. The engine behind value-based stock returns appears to be taking advantage of over-reaction to discouraging outlooks, where mean reversion provides subsequent reward to the value investor.

The screener

Optimizing an investment strategy on a limited data set carries the very real risk of data mining, therefore Occam’s razor is applied to determine a very simple screener where less is more.

The screener requires that a stock be sufficiently liquid with the following definition

  • Market cap > 50 M$
  • minimum average volume within X days > 250k
  • minimum price / share > $2.5

The top 20 stocks are chosen from a score defined by the sum of:

  • rank of free cash flow yield
  • rank of earning yield
  • rank of sales per share / price
  • rank of EBITDA / Enterprise Value

That’s it! Absolutely no quality or momentum based evaluation is employed.  Simply the top 20 low-priced stocks are selected based on their relationship to price. The optimum number of stocks to hold seems to be near 20.

The screenshot in Equities Lab is shown as follows:

Reality factors

  • slippage: I have assumed a conservative value of 0.5% rebalancing loss due to bid/ask spread and commissions.
  • rebalancing: Monthly at the beginning of the month. Similar returns are had from quarterly rebalancing (CAGR 24%).
  • delay: the performance assumes a delay of 1 day from Equities Lab screen results to updating the portfolio. (this delay costs about 1% of annualized returns).


The CAGR (compound annualized growth rate) is about 25% with a standard deviation of 26%). I like to see the standard deviation of portfolio performance annual returns be equal to or less than the CAGR. That is nearly true in this case. Visually, on a long enough time scale, one can see that the log-returns are fairly linear. This tells us that this is a healthy and robust screen method.


A simple value-based screener in Equities Lab is capable of producing very attractive returns if enough patience and diligence is employed.

You will not like every company this screener uncovers. Carlisle [3] cautions that investors filtering model-based stock selection with their own judgment can easily destroy the advantage that value-based investing offers.


[1] David N. Dreman and Eric A. Lufkin, Investor Overreaction: Evidence That Its Basis Is Psychological, The Journal of Psychology and Financial Markets, 2000, Vol. 1, No. 1, 61–75

[2] Wesley R. Gray, Value Investing: Never Buy Expensive Stocks. Period. White PaperJuly 1, 2014

[3] Toby Carlisle, Simple But not Easy.

1 Comment

  1. Eric Darnel Eric Darnel says:

    Adding a term with a -1 coefficient for total debt to market cap in the ranking score boosts performance significantly!

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.