The author started this chapter with a description of how security analysts became knowledgeable and gained popularity among Wall Street firms. Portfolio managers became reliable on recommendations of those security analysts and used them to develop and manage investment-banking clients. Next, the author raised the question whether fundamental analysis is any good. The author described two opposing views that have been taken about the efficacy of fundamental analysis. On one hand, Wall Streeters and market professionals consider a fundamental analysis more powerful all the time. On the other hand, the academic community argues that fund managers and their analysts can do no better at selecting stocks than a rank amateur. The subchapter “Are security analysts fundamentally clairvoyant?” explains main role of security analysts.
Forecasting future earnings is the main reason why security analysts exist. These analysts predict future directions by analyzing past performance. The author used one of analyst’s quote “A proven score of past performance in earning growth is a most reliable indicator of future earning growth.” Thus, analysts argue that if skillful management remains at the helm, future earning should continue as it has in the past and it is based on specific, proven company performance. However, academics decline such assumptions. Academics argue that analysis of past earnings growth does not help to predict future growth. Some of the examples of inconsistency between past and future earnings are companies such as IBM, Polaroid, Kodak, Nortel Networks, Xerox. Academics argue that analysts cannot predict consistent long-run growth because such growth does not exist.
Although good analysts make predictions using not only the past record but also every factor that goes into the actual forecasting process, the author and John Cragg conducted a research study. They asked nineteen of the most respected Wall Street fundamental analysis firms for their estimates of the future one-year and five-year earnings for a large sample of S&P 500 companies. The results demonstrated that fundamental analysts’ predictions of both one-year and five-year forecasts were worse than naïve forecasts. Some analysts argued that high-tech companies and various cyclical companies are hard to forecast, thus one of the analysts proposed the author to check the accuracy of utilities company’s forecast. However, even stable utilities were far off mark. Thus, the author made the second major conclusion that not one industry is easy to predict.
Moreover, the author argues that none of analysts did better that the other on a continuous basis. There is no pattern of analyst performance. These findings were also confirmed by other researchers – Michael Sandretto of Harvard and Sudhir Milkrishnamurthi of MIT. These researchers conducted a massive research study of the one-year forecasts of the 1,000 most widely followed companies. The conclusion of their studies was that the error rates each year were remarkably consistent and that the average annual error of the analysts was 31.3 percent over a five-year period. Thus, financial forecasting is a less respectable science that astrology. Investors who trust analysts’ predictions and make their investments based on their trust end up disappointed in many cases. Next, the author argues that investors should not take for granted the reliability and accuracy of any judge, no matter how expert.
The author provided examples of how doctors and radiologists make mistakes in some cases. This may explain why analyst is not an exception. The author argues that there five factors of why security analysts make mistakes in predicting the future. The first factor is the influence of random events. There are many unpredictable events that have a strong impact on the company’s earnings. Even stable and dependable companies such as utilities are not an exception because unpredictable events such as unfavorable ruling of state public utility commissions cannot be expected or predicted. The second factor is the production of dubious reported earnings through “creative” accounting procedures. Many companies use “creative” accounting to mislead investors. For example, failing dotcoms, high-tech companies and even old-economy blue chip companies tried to hype earnings and mislead investors. The author provided 7 examples of how different companies boosted its revenues and profits or underreported costs to satisfy Wall Street’s need for steadily growing earnings. Thus, security analysts have difficulty estimating future earnings.
The third factor is errors made by the analysts themselves. Security analysts are very human beings who make mistakes. The author provided an example of how some analysts parrot back what managements tell them being misguided and self-important or some just make mistakes of misplacing decimals without further correction of those mistakes. The fourth factor is the loss of the best analysts to the sales desk, to portfolio management, or to hedge funds. Security analysts are often sent to manage the regular salesperson on a call to a financial institution. Thus, analysts spent time with institutional clients, not with financial reports. The majority of security analysts do not remain long in their jobs because of promotions to the prestigious more exciting and better paid positions of portfolio manager for hedge funds.
The fifth factor is the conflict of interest between research and investment banking departments. The author argues that analyst recommendations are affected by profitable investment banking relationships of the brokerage firms. Results of several studies have confirmed that stock recommendations of Wall Street firms without investment banking relationships did much better than the recommendations of brokerage firms that were involved in profitable investment banking relationships with the companies they covered. In the next subchapter “Do security analysts pick winners?”, the author provides the evidence from several studies that even though mutual funds are chosen by some of the best analysts, mutual funds have not outperformed randomly selected groups of stocks in the long run. One of the main reasons is that it is impossible to predict future performance of funds in any given future period. In addition, there is no scientific evidence that the investment performance of professionally managed portfolios has outperformed a broad-based index.
