Introducing Urbem Quantitative Investing

This article was originally published on this site

To generate consistent risk-adjusted alpha, intelligent stock investors should buy wonderful businesses at reasonable prices and hold them for the long run. Based on this philosophy, I detailed my strategy and considerations in my Investment Strategy Statement.

Meanwhile, I am always enthusiastic about a scalable systematic approach in the area of value/quality investing, and I found that quantitative modeling offers decent leverages as it is efficient at processing a huge amount of data and effective through backtesting.

Overall, there need to be three issues here to be addressed –

  1. Finding wonderful businesses;
  2. Buying at the right valuation;
  3. Be patient with invested businesses unless their fundamentals deteriorate.

My previous article, Introducing The Urbem Quality Score, described a factor-based model to rank business quality so as to address the issue of “finding wonderful businesses.” If a portfolio just selects top-quality stocks (with highest Urbem Quality Scores) even without the consideration of valuation, it would still beat the market average over the past 15 years by a sizable margin.

Now I would like to make this algo-investing approach optimized and complete by incorporating a couple of rules, including buying at the right price, in order to hopefully enhance risk-adjusted alpha.


Built upon the Urbem Quality Score, the algorithm trades according to the following rules –

  • Buy stocks earning the highest Urbem Quality Scores where: 1) Free cash flow yield > 2.5%, 2) Free cash return on assets above 5% every year for the past decade, 3) EPS and FCF both positive every year for the past decade, 4) stock price >= $3, 5) past-20-day average trading volume >= 20k, and 6) the stock is not already in the portfolio;
  • Sell stocks where: 1) the rank drops out of the top 100 according to the Urbem Quality Scoring model; 2) Free cash return on assets turns below 5% for the past year, OR 3) EPS or FCF turns negative for the past year;
  • Rebalance every 26 weeks;
  • Aim for 33 stocks in the portfolio at any given time;
  • Deploy residual cash evenly when establishing new positions in different stocks;
  • Stock-picking universe = all US-listed stocks;
  • Maximum exposure of a single stock = 30%.

I call this automatic value-investing algorithm “Urbem Quantitive Investing.” You can find more details here. One point worth noting is that valuation is considered only when a stock is being bought but not when a stock is being sold (only business fundamentals are considered then).


As described below, the Urbem Quantitive Investing portfolio outperformed the benchmark, the Total Stock Market Index (VTI), by a wide margin since the beginning of 2004.

Source: Portfolio123.

We also noticed that there was only one year of negative return with the algorithm (i.e., 2008), and the max yearly drawdown is lower (see below).

Source: Portfolio123.

Out of the 15 years between 2004 and 2018, the algo-investing portfolio managed to beat the market in 11 years and has been doing so consecutively every year since 2013.

More importantly, although slightly and with a greater max drawdown, this new portfolio driven by rule-based investing algorithm does outperform the previous one purely taking Urbem Quality Score (i.e., business “wonderfulness”) into consideration (see below).

Portfolio of Urbem Quantitative Investing Portfolio of Urbem Quality Score VTI
Total Return 525.27% 481.71% 279.73%
Annualized Return 12.46% 11.96% 8.92%
Annualized Alpha 3.73% 3.55% N/A
Max Drawdown -51.42% -46.68% -55.45%

Performance data between 1/1/2004 and 8/9/2019.

From a trading perspective, the average annual turnover so far amounts to 19.43% and the percentage of winners among all picked stocks is 74.13%.

As of most recently, the model did not beat the benchmark over the past 3 months or so (see below), but short-term underperformance should never concern any intelligent investor. The YTD alpha is currently 0.43%.

Source: Portfolio123.


Below lists all current 33 holdings in the portfolio managed by Urbem Quantitative Investing. You should find a lot of familiar names if you have been closely following my SA channel.

Ticker Name
AAPL Apple Inc
ACN Accenture PLC
AMGN Amgen Inc
APH Amphenol Corp
BIIB Biogen Inc
BKNG Booking Holdings Inc
CACC Credit Acceptance Corp
CHKP Check Point Software Technologies Ltd
CPRT Copart Inc
CTSH Cognizant Technology Solutions Corp
EW Edwards Lifesciences Corp
FDS FactSet Research Systems Inc.
FFIV F5 Networks Inc
GGG Graco Inc.
GILD Gilead Sciences Inc
GOOGL Alphabet Inc
INTU Intuit Inc.
ISRG Intuitive Surgical Inc
JKHY Jack Henry & Associates Inc
MANH Manhattan Associates Inc
MSFT Microsoft Corp
NTES Netease Inc
NVO Novo Nordisk A/S
ORCL Oracle Corp
PZN Pzena Investment Management Inc
ROL Rollins Inc.
SEIC SEI Investments Co
TJX TJX Companies Inc
TPL Texas Pacific Land Trust
TXN Texas Instruments Inc
USNA USANA Health Sciences Inc
WAT Waters Corp

According to the charts below, the portfolio overweights technology, has no exposure in utilities or materials, and is heavily concentrated in large-caps.

Source: Portfolio123.


An algorithm-driven investing approach like Urbem Quantative Investing appears to have worked so far for those value/quality investors who want to pursue the strategy of buying wonderful businesses at reasonable prices. Of course, some readers may argue that 15 years (the limit imposed by Portfolio123) is a little short time horizon for backtesting a strategy. Also, the rule-based algorithm has been outperforming the market 6 years in a row. So we will see how the performance goes from this point on. However, I do enjoy the stock picks from the algorithm so far. As long as the underlying approach is right, the future should look bright as long as we stick to it.

Disclosure: I am/we are long AAPL, CHKP, CACC, FDS, ISRG, NVO, ROL, SEIC, WAT. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Additional disclosure: Mentioning of any stock in the article does not constitute investment recommendations. Investors should always conduct careful analysis themselves and/or consult with their investment advisors before acting in the stock market.