Aristotle on Investment Decision Making

Abstract

In this age of inexpensive and abundant data, investors must remain mindful of the limitations of the data they are using for their investment decisions. Despite widespread problems with the quality, timeliness, and relevance of financial and economic data, many investors accept and react to these data at face value. Often, further analysis of the data and an understanding of the factors that drive the data will lead to increased uncertainty—and may change the analyst's conclusions.

Most modern people acknowledge that Aristotle was a bright and insightful man, but most do not recognize that his wisdom can be applied to investment decision making. Aristotle warned, “It is the mark of an educated person to look for precision only as far as the nature of the subject allows.” Investors would do well to apply that wisdom to the data and models used in their analyses and forecasts.

Investors commonly presume greater precision than is warranted in data and quantitative techniques. This article examines the limitations, quality, timeliness, and relevance of some of the financial and economic data that are so extensively and inexpensively available today. Indeed, the power of today's computational techniques can magnify the consequences of bad data and poorly constructed models.

The first area of concern is the quality of the data used as inputs to analysis. Investors must acknowledge the shortcomings of many of the commonly used variables. Consider that company analysts tend to emphasize reported EPS, often to the exclusion of other factors—such as revenues and cash flow—and despite the volatility in earnings data related to unevenly applied accounting standards.

Second, users must be cognizant of the inherent weaknesses in the models themselves. Too often, investors rely on inexact rules of thumb rather than more disciplined theoretical approaches. A prime example is the use, when measuring and estimating fair value, of the historical “average” P/E multiple rather than more complex approaches that also consider trend growth in earnings or cash flows and adjust for inflation, interest rates, and sustainable return on investment.

Third, financial markets respond almost instantaneously to the public release of economic and financial data and investors rarely look back to readjust their response when more complete or accurate information becomes available. For example, share prices respond more notably to the release of pro forma company results than to the more precise information later filed with the U.S. SEC. Similarly, sharp market reactions often occur to initial economic data releases, although the data are of notoriously poor quality. Many of the series of government data are based on small samples and are repeatedly revised when more raw information becomes available to government statisticians. Even so, few investors review their earlier conclusions. The initial data releases can lead to misinterpretations, and those mistakes can be compounded when users fail to recognize the interrelationships between economic indicators. Examples include the links between GDP (which can be revised for years following the initial announcement), labor productivity, and employment costs. Furthermore, initial trade data are incomplete, yet investors often fail to review subsequent revisions when considering their views on trade deficits, currencies, and global flows of funds.

Some investors, especially those with very short horizons, quickly respond to data announcements, of whatever quality, and then move on. Those with longer-term horizons, however, would be well advised to become better acquainted with the construction of economic and financial information. Doing so, and monitoring later revisions, may provide exceptional insights into the true condition of the economy or the company being analyzed. Revised conclusions may be quite different from those that were developed by the earlier “instant analysis” approach and may reveal notable anomalies and distinctive opportunities.