Are analysts biased

By Deborah Edwards,2014-04-18 00:25
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Are analysts biased



    Thabang Mokoteli

    Cranfield School of Management



    MK43 0AL


    Tel: +44 0 1234 751122 ext 3259

    Fax: +44 0 1234 752554


    Richard J Taffler*

    Martin Currie Professor of Finance and Investment

    Management School and Economics

    University of Edinburgh

    William Robertson Building

    50 George Square

    Edinburgh, EH8 9JY


    Tel: +44 (0) 131 651 1375


    First draft

    November 30 2005

    * Corresponding author

    Are analysts biased? An analysis of stock recommendations that perform contrary to expectations


    This paper seeks to test whether analysts are prone to behavioral biases when making stock recommendations. In particular, we work with stocks whose performance subsequent to a new buy or sell recommendation is in the opposite direction to the recommendation. We find that these “nonconforming recommendations are associated

    with overconfidence bias (as measured by optimism in language analysts they use), representativeness bias (as measured by previous stock price performance, market capitalization, book-to-market, and change in target price), and potential conflicts of interest (as measured by investment banking relationships).

    Finding that potential conflicts of interest significantly predict analyst nonconforming stock recommendations supports recent policy-makers‟ and investors‟

    allegations that analysts‟ recommendations are driven by the incentives they derive

    from investment banking deals. These allegations have led to implementation of rules governing analyst and brokerage house behavior. However, finding that psychological biases also play a major role in the type of recommendation issued suggests that these rules may work only in as far as regulating conflicts of interest, but will have a limited role in regulating the cognitive biases to which analysts appear to be prone. Our results suggest that, as a result of this, analyst stock recommendations may continue to lack investment value.


1. Introduction

    Sell-side analysts play an important role in pricing of stocks in financial markets. Grossman and Stiglitz (1980) show that stock prices cannot perfectly reflect all information that is available, and therefore analysts devote enormous resources to gathering new information. Analysts deserve to be compensated as information gatherers. Beaver (2002) indicates that efficient analyst information processing facilitates efficient security price setting, while Fernandez (2001) shows that analysts produce information that is the “life-blood” of both the market and the individual


    Although research attests to the importance of financial analysts for the efficient functioning of the capital markets, in the recent past strong doubts have been expressed about the credibility and objectivity of their stock recommendations. Specific concerns related to the fact that analysts‟ recommendations were overly optimistic and did not seem to reflect their true beliefs about the stocks they were reporting on. By mid-2000, the percentage of buy recommendations had reached 74% of total recommendations outstanding while the percentage of sells had fallen to 2% (Barber et al., 2004a). The main reason held to be responsible for this unequal distribution of buy and sell recommendations was that optimistic analyst recommendations could earn their investment bank employers large fees from corporate finance transactions.

    The problem of optimistic research reports and the public outcry over analysts‟

    conflicts of interest led to intervention by policy-makers and professional bodies who responded by implementing regulations to govern brokerage firms and analysts. In September, 2000, the Securities and Exchange Commission (SEC) implemented Regulation Fair Disclosure (Reg FD). Reg FD was meant to curb the practice of asymmetric information provision where top executives in companies would disclose information to particular analysts, often to those working for the investment banks with whom they had ongoing business relationships. In August, 2002, the National Association of Securities Dealers (NASD) and the SEC issued NASD 2711 and Rule 472 respectively. Overall, these two regulations require analyst research reports to display the proportion of the issuing firm‟s recommendations that are buys, holds and sells. In April 2003, the “Global Analyst Research Settlement” was reached between the


    top ten US brokerage firms and the SEC, New York Stock Exchange (NYSE), NASD and the New York Attorney General. This led, inter alia, to these brokerage firms paying $1.4bn in penalties for alleged misconduct resulting in investors losing large sums of money from trading on their analysts‟ stock recommendations during the technology bubble. Importantly, however, the intervention of policy-makers and regulators assumes that the problem of optimistic analyst reports is caused only their conflicts of interest.

    Research also finds that although analysts issue optimistic reports on most of the stocks they cover, their recommendations lack market impact. For example, Barber et al. (2001) and Mikhail et al. (2004) show that, after accounting for risk and transaction costs, investors do not earn better than average returns from following analysts‟ stock

    recommendations. Womack (1996), on the other hand, finds that new buy stock recommendations continue to go up for four to six weeks after the new stock recommendation is made, while new sell recommendations lead to stock prices drifting significantly lower for six more months. His results suggest that the average level of recommendation has little investment value but changes in level are valuable, although for a limited time. Ryan and Taffler (2005), for the UK, find that only new sells, and recommendations for smaller, less-followed stocks, have investment value. These research findings lead to the question of what causes analysts to issue stock recommendations that lack investment value.

    This paper argues that an important determinant of the apparent judgmental errors made by analysts is cognitive bias. Although there are various cognitive biases documented in the behavioral finance literature, two salient biases recognized as key in explaining the “irrational” behavior of market participants are overconfidence and


    Overconfidence is defined as overestimating what one can do compared to what objective circumstances would warrant. The more difficult the decision task, and the more complex it is, the more successful we expect ourselves to be. Overconfidence may help to explain why investment analysts believe they have superior investment insights, and yet their stock recommendations are of limited investment value. Various authors have noted that the overconfidence of investors, including analysts, plays a major role in the anomalies observed in financial markets. For example, Odean (1998a) looks at the


    buying and selling activities of individual investors at a discount brokerage. On average the stocks that individuals buy subsequently underperform those they sell even when liquidity demands, risk management, and tax consequences are taken into consideration. He suggests that this behavior of selling winners too soon is motivated by overconfidence. Barber and Odean (2001) assert that rational investors trade only if the expected gains exceed transaction costs. But overconfident investors overestimate the precision of their information and thereby the expected gain of trading.

    The representativeness heuristic (Tversky and Kahneman, 1974) involves making judgments based on stereotypes rather than on the underlying characteristics of the decision task. People tend to try and categorize events as typical of a representative of a well-known class and then, in making probability estimates that overstress the importance of such a categorization, disregard evidence about the underlying probabilities. One consequence of this heuristic is for people to see patterns in data that is truly random and draw conclusions based on very little information. Shefrin and Statman (1995) indicate that investors believe that good stocks are stocks of good companies, which is not necessarily true. This is rooted in the representative bias, which supports the idea that winners will always be winners and losers will always be losers. DeBondt and Thaler (1985) argue that because investors rely on the representative heuristic they could become overly optimistic about past winners and overly pessimistic about past losers. This bias could cause prices to deviate from their fundamental level.

    The aim of this paper is to establish whether policy-makers are addressing the only important real issue in seeking to address conflicts of interest alone, or whether other factors, in particular, cognitive bias, which, in fact, may be difficult to regulate, also plays a major role in influencing analysts to issue stock recommendations that lack market impact.

    Using an appropriate benchmark metric, we first evaluate the performance of analyst stock recommendations over the 12-month period after their recommendations are changed from their previous categories to new buy (sell) categories. In line with the results of earlier studies, we find that the stockmarket reacts significantly to new buy recommendations only in the recommendation month (month 0), with no subsequent drift. Conversely, the market reacts significantly and negatively to new sell ratings, not just in the month of recommendation change. It also exhibits a post-recommendation


    stock price drift which lasts for up to 12 months subsequent to the new stock recommendation. Consistent with the extant literature (e.g., Womack, 1996) we also find the complete price reaction to new sell recommendations is much greater than to new buy recommendations.

    With both buy and sell recommendations, many stocks perform different to expectations. For instance, there are new buys (sells) that underperform (outperform) the benchmark 12 months after the recommendation is made. To focus on these stocks where analysts can be viewed, ex post, as having made erroneous judgment calls, we

    therefore work with cases where subsequent stock performance is contrary to expectations. We find in our data that 56% of new buy recommendations have underperformed the appropriate benchmark 12 months after the recommendations are changed and, of these, more that 6 out of 10 stocks (62.5%) underperform the benchmark by at least 20% by month 12. On the other hand, 70% of new sell recommendations perform as expected over the 12 month period and only 16% outperform the benchmark by at least 20% by month 12.

    We then establish which factors are associated with these “contrarian” stocks.

    We find that analysts stock recommendations that perform contrary to expectations are associated with (i) overconfidence bias (as measured by the optimistic tone of language used in their research reports), (ii) representativeness bias (as measured by previous positive stock price performance, size of firm, growth status of the firm (book-to-market), and change in target price), and (iii) corporate relationships between their investment bank employers and the firms they are following. These findings imply that the regulations recently promulgated to govern analyst and brokerage house activity, however successful they might be in dealing with analyst conflict of interest, may have only limited impact on problems associated with analyst cognitive bias, which is probably inherent in the nature of their work.

    The remainder of the paper is organized as follows: the next section formulates our research hypotheses. In section 3 we present our data and in section 4 we described our research method. Section 5 discusses the price performance of new stock recommendations both for our full sample and also for our non-conforming stocks. Section 6 presents our empirical results and concluding section 7 discusses these and their implications.


2. Hypotheses

    Our null hypotheses about the determinants of nonconforming analysts‟ stock recommendations are developed in this section. The hypotheses are grouped under two broad categories, cognitive biases and corporate relationships.

    2.1. Cognitive biases

    Tversky and Kahneman (1974) postulate that when people are faced with complicated judgments or decisions, they simplify the task by relying on heuristics or general rules of thumb. Because of the complex nature of the analysts work, we

    postulate they are likely to be prone to cognitive biases, in particular, overconfidence and representativeness.

    2.1.1. Overconfidence bias

    We measure overconfidence bias by the tone of language that analysts use in their research reports. Specifically, we use the variables OPTIMISM and CERTAINTY, provided by the Diction content analysis software. OPTIMISM is defined in Diction as

    language endorsing some person, group, concept or event or highlighting their positive entailment, while CERTAINTY is defined as language indicating resoluteness, inflexibility, completeness and a tendency to speak ex cathedra. Our first null

    hypothesis is thus defined as follows:

    H1: The tone of the language used by investment analysts in their research 0

    reports to justify their stock ratings is not optimistic independent of

    whether the stock recommendation is new buy or new sell.

    If overconfidence bias (as measured by OPTIMISM and CERTAINTY)

    influences analyst stock recommendations, then we expect it to have a significant positive (negative) impact on their new buy (sell) ratings that subsequently perform in a contrarian manner.

2.1.2. Representativeness bias Activity


    We use the Diction variable ACTIVITY to measure the degree of

    representativeness bias in the language used by analysts when preparing their research reports. ACTIVITY is defined in Diction as language featuring movement, change, and

    the implementation of ideas and the avoidance of inertia. Fogarty and Rogers (2005) conclude that analysts‟ decisions about firms‟ stock tend to be influenced by their knowledge of corporate plans, merger/acquisition talk, or any suggestion of proffered change in corporate direction. Our second null hypothesis is therefore stated as follows:

    H2: The tone of the language used by investment analysts in their research 0

    reports to justify their stock ratings is not positively biased towards the

    level of activity (or change) taking place within the firm. Previous price performance

    Stickel (2000) posits that Wall Street darlings are stocks with, among other

    characteristics, recent positive EPS momentum and surprise, and recent positive relative price momentum. Analysts have incentives to give buy recommendations to stocks with these financial characteristics because they follow from documented momentum pricing anomalies, and because they are actionable ideas that generate trading commissions. We take previous price momentum as another measure of representativeness bias in that analysts might assume that the previous price performance of the stock is representative of the future performance of the stock. Null hypothesis 3 is therefore established as follows:

    H3: Price momentum either has a negative (positive) or insignificant impact 0

    on whether analysts will issue a buy (sell) recommendation which does

    not perform as expected.

    Variable PRICE_MOM is used to capture the effect of price momentum on analysts‟ new buy/sell recommendations. If a stock‟s past performance has a direct

    influence on the type of stock recommendation that an analyst issues, positive PRICE_MOM will be associated with buy recommendations and negative

    PRICE_MOM with sell recommendations. That is, firms that receive buy recommendations are those that have consistently performed well in the recent past, while sell recommendations are given to stocks that have performed poorly over the previous period.

    8 Size of firm

    We consider firm size as another potential aspect of representativeness bias in that analysts might assume that a large (small) firm is a good i.e., well-managed (bad) firm, and thus will subsequently outperform (underperform) the benchmark (Solt and Statman, 1989). Null hypothesis 4 is therefore established as follows:

    H4: Firm market capitalization does not have any significant impact on the 0

    type of stock recommendation issued by analysts for stocks which

    subsequently perform contrary to expectation.

    Variable FIRM_SIZE is used to pick up the effect of market capitalization on the determination of buy and sell recommendations. As in Mikhail et al. (2004), size of the firm is measured using the natural logarithm of the market value of equity for the firm at the end of the financial year preceding the recommendation revision. Our conjecture is that large firms are less likely to receive sell recommendations than small firms; on this basis, new non-conforming buy recommendations are likely to be associated with larger values of FIRM_SIZE, and new non-confirming sell recommendations with smaller values on this variable. Book-to-market

    Most buy recommendations are made by analysts who tend to favor “growth”

    over “value” stocks. This is because growth stocks exhibit greater past sales growth and are expected to grow their earnings faster in the future. Financial characteristics of preferred stocks include higher valuation multiples, more positive accounting accruals, investing a greater proportion of total assets in capital expenditure, recent positive relative price momentum, and recent positive EPS forecast revisions (Jegadeesh et al., 2004). Based on these arguments, we expect that stocks with low book-to-market ratios (growth stocks) are more likely to receive buy recommendations than stocks with high book-to-market ratios (value stocks). Book-to-market is yet another form of representativeness bias because the development stage of the firm is regarded as representative of the stock‟s future performance by analysts. Null hypothesis 5 is

    therefore established as follows:


    H5: The firm’s book-to-market ratio does not have any significant impact 0

    on the type of recommendation issued by analysts for stocks which

    subsequently perform contrary to expectation.

    Variable BTOM is used to capture the effect of book-to-market on our nonconforming stock recommendations. It is measured as book value per share divided by market price of equity. Book value per share is calculated as total assets minus total liabilities deflated by the number of shares outstanding at the end of the firm‟s previous

    fiscal year. Market value of equity is calculated by dividing the firms market value by

    the total number of shares in issue (Mikhail et al., 2004). All accounting measures are obtained from COMPUSTAT. High values of BTOM are expected to be associated with

    buy recommendations and low values with sell recommendations. Target price

     Brav and Lehavy (2003) document a significant market reaction to changes in target prices, both unconditionally and conditional on contemporaneously issued stock recommendations and earnings forecast revisions. Their results suggest that price targets have information content beyond that which is contained in the stock recommendation. As such, stock recommendations should not be looked at in isolation by investors but be used together with target prices. Analysts associate target price direction as being indicative of what the stock recommendation direction should be, which means that target price is considered to be representative of the type of stock recommendation analysts will issue. Null hypothesis 6 is therefore established as follows:

    H6:Target price is not important in determining whether analysts will issue 0

    new buy/sell recommendations on stocks that subsequently perform

    contrary to expectation.

    Target price change variable TGTPRCE_CHNG is constructed to measure the effect of target prices on the determination of buy and sell recommendations. As in Asquith et al., (2005), this variable is the percentage change in the analyst‟s projected target price for a firm; it is computed as the new target price divided by the old target price minus 1. Current and previous target prices are obtained from the respective analyst research reports. In cases where the previous target prices are not available in the current reports, such data is obtained from the First Call database. It is anticipated


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