Can investors use publicly available stock recommendations made by Wall Street
analysts to generate positive risk-adjusted excess returns? Using data from 1986
through 1996, the authors show that a significant difference in returns exists
between the most highly rated and the least favorably rated stocks.
Unfortunately, after accounting for transaction costs, a strategy of buying the
most highly rated and selling the least favorably rated stocks fails to produce
a net risk-adjusted excess return greater than zero.
The authors test the semi-strong form of market efficiency by examining whether publicly
available analyst stock recommendations can be used to generate risk-adjusted excess
returns. The authors devise an investment strategy based on analyst recommendations and
measure its risk-adjusted performance before and after accounting for transaction costs.
The authors begin by collecting analyst recommendations from Zacks Investment Research
for the period from 1986 through 1996 for approximately 3,600 listed companies. Each
observation in the Zacks database includes the name of the stock, the identity of the
analyst or broker/dealer making the recommendation, the recommendation date, and the
rating given on a 5-point scale: 1 (strong buy), 2 (buy), 3 (hold), 4 (sell), and 5
(strong sell). Overall, the authors produce a final sample size of approximately 360,000
observations from more than 4,300 analysts. Interestingly, for all stocks in the
database, the average analyst rating fell from 2.37 in 1986 to 2.04 in 1996, indicating
that analysts' recommendations improved over time. Of all observations, 54 percent were
buys (rating equaled 1 or 2) but only 7 percent were sells (rating equaled 4 or 5),
which may reflect the analysts' reluctance to make sell recommendations.
On a given day, a stock may have ratings from several analysts (on average, five
analysts). For each stock, the authors calculate a daily consensus recommendation,
defined as the average of the analysts' ratings for the stock. As analysts change their
ratings or as new analysts initiate coverage, the stock's consensus recommendation may
change.
To test whether analyst recommendations contain profitable information, the authors
construct portfolios by sorting stocks according to their consensus recommendations. On
a given day, stocks with a consensus recommendation of 1.0–1.5 comprise the first
portfolio, those rated 1.5–2.0 the second, and so on. The fifth, and last,
portfolio contains those stocks rated greater than 3.0. These five portfolios are
created each day, and their daily returns, using market value weights, are calculated.
Because of changes in a stock's consensus recommendation, the composition of the five
portfolios also changes, producing turnover. For each portfolio, the daily returns are
compounded each month to produce a monthly return. The five monthly returns are then
risk adjusted, using, among others, a four-factor model that controls for market risk,
size, book-to-market, and price momentum effects.
The authors consider the following investment strategy: buy stocks in the first, most
favorably rated, portfolio and sell stocks in the fifth, least favorably rated,
portfolio. The average monthly risk-adjusted performance for the first portfolio was 34
basis points (bps), whereas for the fifth portfolio it was –41 bps. Consequently,
the strategy produced excess risk-adjusted returns of 75 bps a month, or roughly 900 bps
a year. Assuming that investors react to analyst recommendations with various lags, the
authors also examine the profitability of this strategy.
Unfortunately, rating changes produce a great deal of turnover in the five portfolios. On
average, a given company changes portfolios 3.8 times a year. This turnover produces a
large number of transactions for the investor who buys the stocks in the first portfolio
and sells those in the fifth. Using an estimate of a round-trip transaction cost of 1.31
percent from Keim and Madhavan (
authors find that a strategy of buying the most highly rated stocks produces a negative
net annual return ranging from –3.6 percent to –1.8 percent. The authors
then examine the strategy using less frequent rebalancing. Although this approach lowers
transaction costs, it also reduces returns; the investor fails to garner the initial
price move when a stock moves to a new portfolio. Overall, the investment strategy with
less frequent rebalancing also fails to produce net excess returns greater than zero.
The authors conclude that the market is not semi-strong efficient. Buying stocks with the
highest analyst ratings generates positive abnormal returns. Furthermore, a supplemental
test by the authors reveals that the abnormal returns are most favorable for small and
medium-sized companies. This result is also consistent with a semi-strong inefficient
market.
Because of transaction costs, however, this market inefficiency cannot be profitably
exploited by investors because the strategy requires a substantial amount of trading.
The authors acknowledge that other strategies based on analyst recommendations might
produce net positive excess returns. Finally, the authors' results do provide some
practical value: For investors planning to buy or sell stocks anyway (and thereby incur
transaction costs), they would do well to buy stocks in the first portfolio and sell
those in the fifth portfolio.