| EXPRIMENTAL STOCK PORTFOLIO Figure 1 Portfolio Performance
As displayed in the chart above, the portfolio’s cumulative wealth index is compared to the wealth index generated by the Russell 2000 Growth Index. The portfolio is diversified and currently holds 30 stocks. Its composition closely resembles that of the Russell 2000 Growth Index. No margin debt, options, or short sales are used and the portfolio is always 100% invested in stocks. As of 12/31/2012, 18% of the portfolio was invested in stocks with equity capitalization exceeding $1.5 billion; 69% in companies less than $1.5 billion but greater than $200 million; and 13% in micro-caps less than $200 million. Many investors, myself included, have spent years analyzing companies for investment purposes and we perceive ourselves as “skillful.” But, our perception of skill can actually be due to a lack of rigorous statistical evaluation of one’s past investment performance, luck, or even wishful thinking on our part. From a different angle, it is possible that a part of one’s cognitive, investment decision-making process may actually lead to less effective decisions than warranted for a given skill level. As such, I believe that the experience of managing a stock portfolio can be used to research, and possibly improve, the subtle, cognitive influences on buy/sell decisions. I have no idea how to address these issues using statistical methods, so I chose to run a diversified, experimental stock portfolio for 5 years (initial performance results are shown in Figure 1) while attempting to improve the uniquely personal aspects of my thinking process. On one hand, the “conscious” or logical security analysis thinking that I use is rooted in the life-cycle valuation framework keyed to the pattern over time of firms’ economic returns and reinvestment rates. On the other hand, the analysis I am undertaking concerning the “subconscious” or auto-pilot part of my thinking deals with our participation, especially through the use of language, in creating a “reality” that each of us perceives as well as the role of high-level goals in the brain’s hierarchy of control. Investment process I have access to only publicly available information. My universe is the 3,500 companies contained in the complete Value Line Investment Survey, which includes small and mid-cap stocks. I use the weekly editions of Value Line to examine each company and ask myself if I might have an edge in disagreeing with the current market price (see life-cycle valuation discussed below). If so, I then research these selected companies using company websites, annual reports, 10-Ks, proxy statements, and other public information. My analytical focus is on judging a firm’s managerial skill versus competitors and contemplating scenarios that could unfold over a 3 to 5 year time horizon. Typically, I avoid buying stocks that have substantially out-performed over the past 5 years. My guiding rule is to own companies for the long term and to bet against short-term reactions in the market that are inconsistent with my long-term forecast. My experience is that this grind-it-out patience can easily yield a rough ride over the short term even if the long term works out. I look for situations where managerial skill could lead to exceptionally big profits in the future that are a genuine challenge for security analysts to quantify and defend in their research reports. I scrutinize the language (written and oral) that management uses in communicating with investors. I look for firms whose intangible capital is especially hard for security analysts to quantify. Although admittedly quite difficult to do since I do not visit companies, I try to assess a firm’s culture. My analysis depends critically on the current context of the firm, which reflects a changing economic/technological environment. This echoes Robert L. Bacon’s point in his Secrets of Professional Turf Betting about ever-changing cycles such that, “… the form moves away from the public’s knowledge.” My hunch is that our investment decision making is significantly influenced by deeply held, and oftentimes unrecognized, assumptions coupled to higher order goals that operate in the background. For example, we typically construct all sorts of linear cause and effect arguments to rationalize buy/sell decisions and to make us “feel smart.” Getting others to perceive us as smart is widely observed in money management organizations. To no surprise, looking smart within an organization keyed to short-term performance most often becomes a process of analyzing short-term news and attempting to forecast quarterly earnings better than the market. We need to be continually mindful of our participation in creating what occurs to us as the real world that seems objective but actually is a personal rendition of the external world largely created by our assumptions. Oftentimes, many of our assumptions are unconsciously accepted, wrapped in language that contributes to our belief that we perceive an objective reality quite independent of ourselves—and all this silently guides our decision-making. With this experimental portfolio, I am trying to better understand and improve how I perceive the world and, in so doing, leverage whatever skill I may have in analyzing companies. In this task, I find it useful to view behavior in terms of people being wired as hierarchical control systems, see http://www.pctweb.org. Life-cycle valuation framework The firms’ competitive life-cycle framework is based on the premise, as illustrated in Figure 2, that competition and capital flows operate over the longer term to force a firm’s economic returns toward the cost of capital. In short, the pattern (“fade” rate) of a firm’s economic returns and reinvestment rates reflects an unending struggle between managerial skill and competition over time. Figure 2 Firms’ Competitive Life-Cycle
Figure 3 illustrates a discounted cash flow approach to a firm’s warranted value, i.e., the present value warranted by a particular forecast of a firm’s long-term net cash receipt stream and by the assigned discount rate. An especially useful way to articulate a net cash receipt stream is by forecasting a future pattern over time of economic returns and reinvestment rates. Importantly, this model, which is sometimes referred to as the CFROI valuation model, is rooted in a systems perspective. The discount rate is a forward-looking (“market-derived”) discount rate that is dependent upon the procedures used for forecasting net cash receipts. Figure 3 Life-Cycle Valuation Model
Summary In summary, there are basically two parts to my thinking process for this experimental portfolio: (1) the life-cycle valuation framework is described in my book, Wealth Creation: A Systems Mindset for Building and Investing in Businesses for the Long Term (click here) and (2) important cognitive issues relevant to improving one’s investment decision-making are described in my article, “Management’s Worldview: Four Critical Points About Reality, Language, And Knowledge Building To Improve Organization Performance,” (click here for the article and here for a video presentation). While I have no interest in discussing individual stocks, I do welcome the opportunity to exchange ideas concerning my published work. |
