The “Jack Welch” Portfolio Algorithm
Jack Welch is one of the most recognizable names in business as the former CEO of General Electric. His skills and leadership in running one of the largest companies in the world have been the source of numerous books and case studies in the business literature. Running such a large corporation like GE that makes investments all over the world and employs tens of thousands of people is so complex for one person that it requires intelligent heuristics to manage effectively. Most certainly these “rules of thumb” that Jack Welch used have proven themselves in the real world. So the question is, what can we learn from Jack about portfolio management?
One of the famous approaches that Jack was known for was to fire the least productive employees in his workforce. In fact, he chose to let go roughly 10% of his workforce each year. His philosophy was slightly more nuanced, and there is a good clip in wikipedia http://en.wikipedia.org/wiki/Vitality_curve that
covers his theory in more detail. Here is a key excerpt:
“Jack Welch’s vitality model has been described as a “20-70-10” system. The “top 20” percent of the workforce is most productive, and 70% (the “vital 70”) work adequately. The other 10% (“bottom 10”) are nonproducers and should be fired. “Rank-and-yank” advocates credit Welch’s rank-and-yank system with a 28-fold increase in earnings (and a 5-fold increase in revenue) at GE between 1981 and 2001.
In Straight from the Gut, Welch says that he asked “each of the GE’s businesses to rank all of their top executives”. Specifically (in accordance with the 20-70-10 model) the top executives were divided into “A”, “B”, and “C” players. Welch admitted that the judgments were “not always precise”.
Essentially “A” executives were in the top 20% and considered a key value center for the company, “B” employees were the middle 70% and were considered vital but not extraordinary, and 10% were considered to be of little value—“non-producers” in Jack parlance. If there is truth to the theory that this heuristic was critical for GE’s success during his tenure, then there are some lessons to be drawn for portfolio management. The most obvious is that historical performance is predictive of future performance, and thus momentum or relative-strength investing should be effective. Of course this is well-supported in the academic literature. But what is interesting to me is the concept of “firing” C players–or the bottom 10%, and also the concept of considering the B players to be vital to the organization. What this implies is that we should “cut our losers”–a concept validated by “trend-following” and many of the greatest traders of all-time. But the concept of keeping the B players is counter-intuitive, and yet for portfolio management this implies that one should keep most of the middle-performing holdings as a base of diversification to support the top 20%. One can easily see how this can be applied with several different variations as a portfolio algorithm.
As but one of many examples, suppose you started with a diversified basket of 100 stocks. You could overweight the top 20 by 1-year returns (50% of the portfolio), keep a smaller weight in the middle 70 by 1-year returns (50% of the portfolio), and simply cut the remaining 10 stocks at each rebalancing period- say every quarter (0% of the portfolio). Suppose one repeated this process until they were left with 10 stocks or fewer, at this point one could start again with 100 stocks. In theory this method could also function as a “follow-the-leader” algorithm in keeping with the last post. This variant might look at the cumulative performance or a combination of cumulative and rolling performance until you ended up with a handful of stocks. This method of “force-feeding” winners in your portfolio with capital is an approach long-heralded by William O’Neill and other trading legends. While I have not yet tested these variants, I expect to put this simple yet intuitive heuristic algorithm to the test.