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The Biases of Analysts

Be aware that securities analysts are human, and thus they are influenced by the same psychological biases and emotions as the rest of us. They tend to follow a specific industry and get attached (see attachment bias in Chapter 2, "Behavioral Finance") to the firms in that industry. Despite their rigorous training in stock valuation, analysts can completely disregard traditional theory during periods of stock market mania. During the tech stock bubble, most analysts encouraged the purchase of stocks that already had prices over 10 times their reasonable value. For example, making aggressive and optimistic assumptions in traditional valuation models, DoubleClick, Inc. was worth less than $7 per share in late 1999 and early 2000. Its stock price was over $120 per share. Yet, analysts were forecasting even higher prices and recommending a Strong Buy. Indeed, by mid- to late 2001, the stock was trading in the much more appropriate $7 to $10 range. Of course, this wasn't good for investors who bought DoubleClick at $100 per share (or more) based on those recommendations.

The changing financial environment of the 1990s has caused (or made worse) a conflict of interest for analysts. Most financial firms earn their income from three sources: (1) investment banking, (2) brokerage services, and (3) proprietary trading. Proprietary trading is the trading and investing of the firm's own money. Brokerage services are the fees generated from the activities of other investors, like commission fees and market-making activities. Competition from the rise of discount brokerages in the 1980s and the deep discount online brokerages in the 1990s reduced this income for traditional financial firms. The largest source of income is the investment banking activities, which include helping firms issue stocks, bonds, and other securities. The fees from helping a firm go public in its initial public offering (IPO) can be in the tens of millions of dollars.

Consider what you would do if you were a company hiring an investment bank to help issue some securities. You can hire Morgan Stanley, whose analyst rates your firm a Strong Buy, or you can hire Merrill Lynch, whose analyst rates your firm a Strong Sell. Which do you chose? Morgan Stanley would get the business every time. Since the fees are so large, the investment-banking segment of the firm puts pressure on the analysts to rate firms highly so that they can get the IPO and other investment banking business.

Indeed, an analyst that gives a firm a poor rating will get attacked on multiple fronts. First, the company may retaliate by limiting the analyst's access to information about the firm in the future. Then the company may pull its business from the analyst's firm. Also, other clients of the analyst's firm may complain because they own the stock and it now has a poor rating.

Of the 39,972 analyst recommendations made in 1999, only 1,173 (or 2.9%) recommended investors sell a firm. Contrast this with the 27,654 (69.2%) recommendations to buy a firm. Note that this was at the peak of the tech bubble in the stock market. When the collapse was occurring, one year later, the recommendations were still nearly identical: 70.8% buy and 1.8% sell. The analyst recommendations seem to have had little to do with market conditions. Additionally, a recommendation to buy a stock is not that remarkable.

The bias of analysts can be seen quite clearly in those firms that have recently gone public with an IPO. Consider the 391 firms that went public in 1990 and 1991. When an investment bank helps a firm conduct an IPO, the bank is called the underwriter. To keep a good reputation as an underwriter, the investment firm will sometimes work to support the price of the IPO shares when they weaken. For example, an analyst for the underwriter may issue a Buy recommendation for the IPO firm after the price has slipped in hopes that the recommendation will generate more buyers and increase the stock price. Indeed, when an analyst associated with the underwriter initiated a Buy recommendation on an IPO firm, that IPO firm experienced an underperformance of the market by an average 1.6% in the prior month.3 Are these firms really good to buy? Actually, they continue to underperform the market by 5.3% for the next year. On the other hand, prior performance of IPO firms that non-underwriter analysts recommended beat the market by 4.1% the month before the recommendation and continued to beat the market during the next year by an astonishing 13.1%. It appears that the conflict of interest that analysts face when their employer conducts business with the firms they follow creates biased and poor recommendations.

To illustrate these points, take the behavior of Morgan Stanley's analyst superstar, Mary Meeker. Her frequent appearance on financial television, like CNBC, and her relentless doling out of bullish recommendations on Internet firms dubbed her the Queen of the Internet. By 1998, the prices of these firms far exceeded any valuation rationally computed using traditional methods, so she invented new methods.4 When the profits of the firm can't justify its price, switch to sales. When sales can't justify the price, switch to the number of eyeballs (number of Web page viewers). When the price of the stock achieves the target price, raise the target price. Of the 11 firms she recommended as a Strong Buy, her employer had underwritten eight. Indeed, Morgan Stanley grossed between $500 million and $1 billion in investment banking fees during the Internet IPO mania. At the end of 2000, the tech stock bubble had collapsed. Her 11 recommendations averaged a –83% return, yet all retained a Strong Buy rating. They declined further in 2001.

This conflict of interest faced by analysts has drawn the attention of both the Securities and Exchange Commission (SEC) and the U.S. Congress.5 The SEC has been considering bringing charges against some analysts who were selling stock from their own accounts while publicly recommending other investors purchase those stocks. Congress has been holding hearings on analyst credibility.

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