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Financial analysts' forecasts of earnings
Journal of Banking and Finance 4 (1980) 221-233. © North-Holland Publishing Company
FINANCIAL ANALYSTS' FORECASTS OF EARNINGS Their Value to Investors
Dan GIVOLY and Josef LAKONISHOK Faculty of Management, Tel-Aviv University, Ramat-Aviv, Tel-Aviv, Israel Received August 1979, final version received December 1979
This paper attempts to determine whether financial analysts' forecasts of earnings are useful to investors. This is accomplished by devising and evaluating the performance of trading rules under which transactions are triggered by revisions in earnings forecasts. The main finding is that an investor who acts upon the publicly available revisions of earnings forecasts can consistently outperform a buy-and-hold policy; in fact, such an investor could more than double his return. The results are inconsistent with the efficient market-hypothesis and indicate that the market reacts gradually rather than instantaneously to new information.
1. Introduction
Forecasts of earnings are perhaps the most conspicuous single product of the financial analysts' industry. These forecasts are a demanded and paid-for commodity used by small and institutional investors in making their investment decisions. While the social contribution of financial analysts' forecasts (hereafter FAF) in improving the resource allocation process is generally recognized, their value to the individual client is not entirely evident, at least not to those (and there are quite a few of them) who subscribe to the 'efficient market hypothesis'. According to this hypothesis, all publicly available information is already impounded in the security price in a correct manner. The implication is that, to the extent that they rely on publicly available information (financial statements, management forecasts, macro-economic developments, etc.), FAF do not convey new information; in blunter words, attempts by analysts to produce above-normal profits for their clients through a rigorous and professional security analysis are, according to the hypothesis, doomed and, in the presence of transaction costs and the analysts' own fees, are even counterproductive. Moreover, even if part of the input to FAF is not publicly available, their mass distribution (FAF are mailed simultaneously to hundreds or thousands of clients) makes them a public good immediately upon release and precludes their recipients from taking advantage of what could have been an exclusive oiece of information.
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Could the individual investor still beat the market by utilizing information conveyed by FAF? This is the main question addressed by the present study. Indeed, there is an ample empirical evidence that supports the efficient market hypothesis and therefore produces, a priori, a negative answer to the above question. However, it should also be recognized, that in several instances and for a certain information, the stock market was found to be inefficient. Jones and Litzenberger (1970) for example, observed that prices did not respond instantaneously to quarterly reports of a highly favorable content. In another study, by McKibben (1972) it was demonstrated that publicly available information on rates of return, change in earnings, growth relative to the price-earnings ratio and on payout ratio could be used in selecting a superior portfolio. Another variable, the P-E ratio, was found by Basu (1977) to be useful to investors in identifying potentially-profitable stocks. A different type of publicly available information, the value line ranking, produced, in a study by Black (1973) abnormal gains to investors who acted according to it~ Finally, in a more recent study, Davies and Canes (1978) reported a delayed market reaction to analysts' buy-or-sell recommendations. The findings of these and of several other studies cast some doubt on the existence of perfect market efficiency and indicate that the research question of this study - - can investors exploit publicly available FAF? - - is not trivial. If FAF have any impact on prices, one might expect that an upward revision of earnings would trigger an increase in the price of the stock and that a downward revision of earnings would likewise result in a decrease in the price. Furthermore, if market reaction is not immediate, an investor could devise a profitable trading policy based on the content of published forecasts. One possible way to implement this policy is for an investor to search systematically and add to his portfolio companies whose earnings forecast has just been revised upward. These companies will be held for a period of time sufficient to exploit the subsequent increase in price and will then be dropped from the portfolio to be substituted by a new 'just-been-revised' stock. The portfolio of an investor who adopts such a policy would consist of stocks with recent upward revisions and would exhibit a relatively high turnover. In a recent study Givoly and Lakonishok (1979) provide evidence which indicate that FAF might be relevant to investors, i.e., that they stir market reaction. Whether or not an investor can exploit information on revisions of FAF in the manner described depends on several factors: (1) the speed by which the market adjusts itself to FAF revisions (immediate adjustment would preclude any possibility of above-normal profit), (2) trading policy specifics - - such as the length of the holding period or the consideration given to downward revisions, (3) the transaction cost involved. The objective of the study is to test the hypothesis that investors can utilize the publicly
D. Givoly and J. Lakonishok, Financial analysts' forecasts of earnings
223
available FAF revisions to produce abnormal returns. Acceptance of the hypothesis might indicate both that FAF are relevant to investors and that market reaction to their publication is slow. The paper is organized as follows. The next section describes and discusses the data. The section that follows examines the effect of FAF revisions on the behavior of stock prices. Some trading rules based on FAF revisions are then constructed, their application is simulated for portfolios of different sizes and their performance is measured. Summary of the results and a discussion of the implications for investors conclude the paper.
2. Sample and data The sample consists of 49 companies in three industries: Chemicals and Allied Products (Industry 28 in the Standard Industrial Two-Digit Classification), Petroleum, Refining and Related Industries (Industry 29) and Transportation Equipment (Industry 37). All the companies in the sample are listed on the NYSE and have a fiscal year ending December 31. For each company's actual earnings, EPS forecasts and monthly stock returns were collected for the eight years 1967 to 1974. Forecasts of EPS were collected from Standard and Poor's Earnings Forecaster. A weekly publication that first appeared in 1967, the Earnings Forecaster lists in each issue the outstanding EPS forecasts for about 1500 companies. The forecasts are those made by S & P itself and by about fifty other security analysts and brokerage houses who agreed to submit their forecasts, upon release, for the publication. Typically, three to five forecasters were actively engaged in forecasting the earnings of a given company. As many as 15 different forecasts might simultaneously be available for companies with a widely traded stock. The representative of the group of forecasters for each company, was the forecaster with the greatest number of revisions. This most active forecaster is probably the first to react to new information and is likely to be the most watched and followed by investors. In most cases, Standard and Poor's was the most active forecaster. The three other frequent representatives were Bache, UBS and Shearson-Hammil. The actual EPS were collected from the Earnings Forecaster. The dates of the actual announcement were collected from the Wall Street Journal. The announcement date is the date of the announcement of the audited statements or the release date of the preliminary earnings, whichever is earlier. The rates of returns were retrieved from the monthly CRSP tape. Since FAF is the basic data of the study, a description of some of their characteristics is perhaps in order. Table 1 presents the size distribution of the revisions in FAF. The table reveals that the typical revision is small indeed: The median revision over the
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D. Givoly and J. Lakonishok, Financial analysts"forecasts of earnings
eight years of about 4 ~ , and less than 2 5 ~ of the revisions exceed 10~. The large frequency of small revisions might either reflect the responsiveness and sensitivity of analysts to events and information which have only minute effect of earnings, or they might reflect the existence, in large scale, of idle revisions whose sole purpose is to demonstrate activity. Another feature of
Table 1 Distribution of revision size by year (percentage). Year
First quartile
Median
Third quartile
All years
1.8 1.1 1.8 1.5 1.8 1.9 1.4 2.1 2.8
3.6 2.4 2.6 2.7 4.3 3.5 3.0 4.5 5.7
6.9 4.5 5.5 5.5 7.4 7.6 5.5 8.4 10.9
1967 1968 1969 1970 1971 1972 1973 1974
Table 2 Relative frequency of revisions by month of release.
Percentage of all revisions
Percentage of all revisions
Jan.
Feb.
MarchApril May
June
10.4
8.6
8.9
10.7
5.7
July
Aug.
Sept. Oct.
Nov.
Dec.
7.0
4.7
10.8
6.6
9.9
7.9
8.8
the revisions is that they do not concentrate systematically in certain months (table 2); still, there is a mild concentration during April-May (following the release of the annual reports and the first quarter) during July and August (following the release of the second quarter) during October and November (following the release of the third quarter) and during January (towards the release of the annual reports). The concentration is somewhat more pronounced when only revisions of over 10~o are considered. The lack of clear clustering around the announcement months might indicate that the input to FAF is not limited to accounting information.
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3. Trading rules and performance measurement To test whether investors can profit from information on FAF revisions, trading policies that utilize this information were devised and their performance was measured and compared to that of a buy-and-hold policy. Examined were realistic trading policies whose common feature was their availability to many or to all investors. Portfolios of four different sizes, 5, 10, 15 and 20 stocks, were constructed, each representing a different level of diversification. The selection of the stocks that composed the portfolio was related to the occurrence of FAF revisions in the following way: Each month, starting with the beginning of 1967, the sample of stocks was searched for stocks which have just had an upward revision of their earnings. Such stocks were added to the portfolio and were held for a certain period of time. The exact timing of the purchase and of the disposal of the stock was determined by the trading rule employed. Another parameter of the investment policy was a filter to screen material revisions. Some of the revisions might be too small to stir a meaningful market reaction. Moreover, reaction to any revision, regardless of its size, would increase the number of transactions and might boost transaction costs considerably. Three alternative filters were used: (1) 0%, or no filter (i.e., all revisions were acted upon), (2) 5 % and over, (3) 10 % and over. The search for new stocks was being conducted during all months in which there were 'openings' in the fixed-size portfolio. In months where the number of stocks eligible for inclusion exceeded the portfolio size, the candidates for inclusion were ranked according to the magnitude of the revision and the stocks with the largest revisions entered. According to this investment procedure, a portfolio of size n contains, at any given month, n stocks, at most; the identity of the stocks, though, changes over time. In other words, the portfolio size represents the number of 'slots' available for investment rather than the number of stocks held throughout the period. Due to the limited number of stocks in the sample (49), in some of the periods and for some of the slots of the portfolio, funds remained idle. This is particularly true for the larger portfolios (15 or 20 stocks). The implications of such idle funds will be discussed in the following section. Denoting the upward revision month as month 0 and the surrounding months according to their relative position with respect to the upward revision month (i.e., by - 1 , + 1, etc.), the following trading rules were used:
Rules with predetermined exit (1) Buy at the beginning of month - 1 and sell (exit) at the end of month 2 [ ( - 1 , 2) in notation form].
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D. Givoly and J. Lakonishok, Financial analysts' forecasts of earnings
(2) Buy at the beginning of month 0 and sell at the end of month 2 [(0, 2) in notation form]. (3) Buy at the beginning of month 1 and sell at the end of month 2 [(1, 2) in notation form].
Rules with early exit (4) Same as (1), but sell either at the end of month 2 or at the end of a month with a downward revision, whichever comes first [ ( - 1 , D) in notation form]. (5) Same as (2), but sell either at the end of month 2 or at the end of a month with a downward revision, whichever comes first [(0,D) in notation form]. (6) Same as (3), but sell either at the end of month 2 or at the end of a month with a downward revision, whichever comes first [(1, D) in notation form]. Since the exact time of the revision is some time during month 0, it appears that rules (1), (2), (4), and (5) are available only to investors with some preknowledge on the forthcoming revisions. This might not be accurate. There is always some lag between the generation of forecasts by analysts and their publication in the Earnings Forecaster. (This lag is caused by the processing time of the publication and the fact that the publication is issued only once a week.) In addition, the revision might be available to some privileged clients even before its release to S& P. Hence, rules (2) and (5) might be available to many investors and even rules (1) and (4) might still be available, although to much fewer investors. The holding period under all trading rules terminates on or before the end of month +2. Empirical evidence shows that price increases following an upward revision are fully exploited by the end of month + 2. This and other related evidence are presented in the next section. One might wonder why information on downward revisions has, for the most part, been ignored. Trading rules under which a short transaction occurs when a downward revision is made, could be considered. Indeed, as shown later, downward revisions are associated with an adverse price reaction of the stocks concerned. Yet, the profitability of such a rule is doubtful because its yield must be greater than that on a buy-and-hold policy. (The latter is expected to be positive.) The abnormal returns produced by the various rules were measured and compared to the yield from a buy-and-hold policy. The abnormal return was defined here as the excess return over the expected (normal) return under the widely used 'market model'. According to this model, the return of the
D. Givoly and J. l_,akonishok, Fimmcial analysts' forecasts of earnings
227
individual stock is associated with the market return so that
IR.,)=
+ fl,R,,,,,
where/~it denotes the return of security i at month t, ~i and fli are coefficients and Rmt is the actual market return. 1 The abnormal return in month t is measured by the difference e.lt=Rit-o~i+fliRm, where Rit and Rmt a r e the observed values of the respective returns. 2 To facilitate the interpretation of the results, the originally-derived abnormal returns (e,) were standardized in such a way as to produce each month a zero abnormal return from a buy-and-hold policy. The results are reported in terms of these standardized abnormal returns) The total abnormal return produced by a given trading rule over the eight-year period for a portfolio of size n, was the simple average of the compounded abnormal returns over the eight years in each of the n slots.'* 4. Empirical results The first analysis of the data involved the examination of price reaction to F A F revisions. If FAF are of any relevance to investors, one would expect an above-normal return around upward revisions and a below-normal return 1Brenner (1974) tested the sensitivity of conclusions of empirical studies on the efficiency of capital markets to various specifications of market models (one factor model, two factor model with risk free rate, two factor model with zero-beta portfolio, three factor model with unsystematic risk and a naive model which assumes that: r,=0 and fl-1). He found that empirical results are only slightly affected by the specification of the market model. 2The values of ~t~ and fl~ are estimated from a regression equation. Since the test period (the period during which abnormal returns axe being measured) should be completely divorced from the estimation period, ~ and fit used in year T are estimated by data of prior years. Specifically, the parameters for year T are estimated from the four years (forty-eight months) preceding that year. aThe residuals from the market line, e,, were standardized with respect to their contemporaneous cross-section average, as follows:
where ~, is the standardized abnormal return of stock i at month t, and 1
ni=l
(n is the number of firms in the sample). By performing this transformation, we actually force the average abnormal return derived from a buy-and-hold strategy to be zero in each month and facilitate the interpretation of the results. 4Initially, equal funds are invested in each slot in the portfolio. However, later, after the first turnover of stocks occurred, this is not true any more: When a stock exits the portfolio, the initial fund plus the accumulated return are available for reinvestment in the new stock. Since the returns vary among stocks, the funds reinvested in different slots of the portfolio beyond the first investment month are not necessarily equal.
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D. Givoly and J. Lakonishok, Financial analysts"forecasts of earnings
around downward revisions. Furthermore, the duration of the price effect might be indicative of the market efficiency and of the degree by which publicly available information in FAF revisions can be profitably used by investors. Table 3 indicates that stock prices are affected by, or are at least associated with revisions of earnings by FAF. The table presents the abnormal returns produced, on the average, over selected periods around FAF revisions. The abnormal returns were computed per each holding Table 3 Mean abnormal return per holding period by industry and direction of revision (percentage)." Direction (upward/ downward)
4-month period - 1, 0, 1, and 2
3-month period 0, 1, and 2
2-month period 1 and 2
Total sample
U D
4.7 - 3.8
3.1 - 1.9
2.7 - 1.0
Industry 28
U D
5.0 - 3.4
2.9 - 1.6"
2.1 - 1.2
Industry 29
U D
2.3 - 4.5
2.3 - 3.2
2.4 - 1.9
Industry 37
U D
12.7 -3.1
10.3 -2.3
4.9 1.2"
"All values except those marked by asterisks are different from zero at the 3 % (or less) significance level.
period, and were not annualized. The results are shown separately for periods around upward revisions and for periods around downward revisions. Breakdown by industry is also provided. The abnormal returns were derived according to the computational procedure described in the previous section and they represent the difference between the actual return and the expected normal return on the stock (for downward revisions, the sign of the difference is reversed). Three holding periods were examined - months 1, 2, months 0, 1, and 2, and months - 1 , 0, 1, and 2. Results for months before month - 1 and after month + 2 were also examined, but no significant price effect was revealed in them. The average abnormal return for each of the three holding periods was computed for various revision sizes. As not to confuse the presentation, only the results for revisions larger than 5 % are tabulated. In general, the results for other size-groups were similar. However, on the average, greater abnormal returns were observed in holding periods around large revisions. In all three industries, abnormal returns were found during each of the holding periods around upward revisions. The same result, with the
D. Givoly and J. Lakonishok, Financial analysts' forecasts of earnings
229
exception of one holding period in Industry 37, is reported for periods around downward revisions. The values of all the abnormal returns for the entire sample and the values of most of the abnormal returns for the individual industries are significantly different from zero at the 1% or less significance level. 5 The magnitude of the abnormal returns should also be noted. An upward revision (of over 5 %) was associated with an average price increase of 4.7 % over the four months - 1 , 0, 1, and 2. The abnormal return over the two months 1 and 2 around upward revisions reached 2.7 %. The price reaction to downward revisions was almost as strong as to upward revisions. The Table 4 Mean annual abnormal returns for a 5-stock portfolio by revision size, trading rule, and level of transaction cost (percentages). Level of transaction cost
Trading rule (1, 2)
(1, D)
0%+
0% 0.5 % 1.0%
10.18 4.93 -0.29
5%+
0% 0.5 % 1.0%
10%+
0% 0.5 % 1.0 %
Revision size
(0, 2)
(0, D)
( - I, 2)
( - 1, D)
10.38 3.92 -2.30
8.28 4.85 1.55
11.20 7.00 2.93
12.10 9.19 6.36
15.86 12.45 9.18
9.41 6.46 3.68
13.31 10.29 7.53
10.f8 8.02 5.29
8.84 6.20 3.57
16.98 14.77 12.81
12.42 10.01 7.77
7.78 6.12 4.52
7.54 5.85 4.23
7.88 6.23 4.62
8.11 6.57 5.08
12.15 9.18 6.36
15.91 12.50 9~24
same results were found in each industry and for each year. (The results by years were not presented.) In sum, the findings strongly suggest that stock prices do react in a predictable way to F A F revisions and that the adjustment of the stock market to their release is slow and extends beyond the revision month. Whether or not an investor could actually gain by utilizing information on F A F revisions must yet be tested in the context of a trading policy under which exact trading rules are specified, transaction costs are considered, and a standard measure of performance is set. This test was performed within the framework of the trading policy described in the previous section. The results for a 5-stock portfolio and for a 15-stock portfolio are presented in tables 4, 5, 6, and 7. The results for a 10-stock portfolio and for a 20-stock portfolio were basically the same. STwo statistical tests were used, a parametric and a non-parametric test. The parametric test used was a t-test, and the non-parametric test used was a binomial test in which the variable was the occurrence of abnormal return in the right direction, in a given holding period.
D. Givoly and J. Lakonishok, Financial analysts' forecasts of earnings
230
Table 5 Mean annual abnormal returns for a 15-stock portfolio by revision size, trading rule, and level of transaction cost (percentages).
Level of transaction cost
(1,2)
(1,D)
(0,2)
(0, D)
(-1,2)
( - 1 , D)
0%+
0% 0.5 % 1.0 %
7.71 3.49 0.83
7.79 3.82 0.65
6.33 3.67 0.99
8.12 6.41 2.53
10.43 8.56 5.80
11.02 8.21 5.77
5%+
0% 0.5 % 1.0 %
6.39 5.13 2.87
7.11 5.26 3.45
5.49 3.98 2.54
5.57 4.25 2.61
10.71 8.98 6.85
8.91 7.05 5.56
10~+
0% 0.5 % 1.0 %
3.72 2.77 1.87
3.98 3.02 2.12
3.81 2.74 2.29
4.08 3.28 2.53
6.60 5.62 4.81
6.79 5.93 4.96
Revision size
Trading rule
Table 6 Mean annual abnormal returns for a 15-stock portfolio adjusted for idle time by revision size, trading rule, and the level of transaction cost (percentages).
Trading rule (1, 2) Revision size 0%+
Level of transaction cost 0% 0.5 %
1.0% 5%+
10 % +
Trading rule (1, D)
Percentage of idle time 12.94 5.79
39 %
1.36"
Percentage of idle time 15.20 7.34
47 %
1.22,
0% 0.5 % 1.0%
22.11 17.51 9.55
69 %
26.21 18.98 12.20
71%
0% 0.5 % 1.0%
25.62 18.60 12.29
84 %
29.45 21.80 14.92
85 %
"All values except those marked by asterisks are significant at the 1% significance level.
The major finding is that the trading policy devised which utilized information on FAF revisions was profitable, i.e., generated abnormal returns. Obviously, the profitability of any trading rule diminished as transaction cost increased. Still, a positive abnormal return was generally produced, even under a 1 ~ transaction cost (each direction). The performance of the trading policy is impressive indeed if one considers the frequent and often long periods during which the funds are idle due to lack of investment opportunities (i.e., due to the absence of upward revisions ), no abnormal returns are produced during idle time. The effect of the idle periods becomes more pronounced as the portfolio size and the minimal
D. Givoly and J. Lakonishok, Financial analysts' forecasts of earnings
231
revision size required to trigger a transaction increase and as the holding period gets shorter. 'For instance, the idle time percentage for a 5-stock portfolio, policy ( - 1 , 2) and 0~o revision size was l lY/o whereas the percentage for a 15-stock portfolio, policy (1,D) and 10~ revision size was 84 ~. The idle time is a consequence of the limited number of companies in the sample (49). In real life, however, investors can conceivably watch a large number of companies for revisions of their earnings forecasts without incurring a material search cost. It can be shown that it is enough to follow 250 stocks in order to cut the idle time percentage in a 5 and a 15-stock portfolio down to 3 ~ and 5 ~, respectively. 6 Table 7 Mean annual abnormal returns for a 15-stock portfolio adjusted for idle time by year, trading rule, and level of transaction cost (percentages). Trading rule (1, 2) (transaction cost)
Trading rule (1, D) (transaction cost)
Year
0~
0.5 ~
1.0 Y/o
0~
0.5 ~
1967 1968 1969 1970 1971 1972 1973 1974
23.1 3.1 16.7 47.4 16.9 32.2 20.7 27.8
15.0 -2.5 10.1 39.5 10.4 24.1 14.2 24.9
7.3 -7.8 4.0 32.0 4.3 16.6 8.1 16.7
20.2 -2.6 21.4 48.4 25.7 29.6 23.6 28.4
11.1 -8.5 13.5 39.7 18.0 22.7 16.94 20.16
1.0 Y/o 2.7 - 14.0 6.2 30.9 10.7 16.3 10.7 16.2
aAll values except those marked by asterisks are significant at the 1% significance level.
In other words, the results shown in tables 4 and 5 might quite likely be an underestimate of the performance that could be achieved from employing the same trading policy in real life. Table 6 presents the abnormal returns for trading rules (1, 2) and (1, D) for a portfolio of 15 stocks, after an adjustment for idle time was made. The adjustment was carried out by raising the abnormal return to the power of 1 / 1 - p, where p is the idle time percentage. This procedure assumed that the same opportunities which existed during busy periods prevailed also during idle times. This assumption does not seem to be restrictive since the average return measured over busy periods was based on a large enough sample of transactions. The abnormal returns presented in table 6 are a function of several features of the trading rule employed: the minimal revision size, the entry time and the exit time. Usually, a switch from one rule to another involves a trade-off between expected total transaction costs and expected gross return. 6The estimate is derived by formulating the problem as a queuing system in which the number of servers is the number of slots in the portfolio, the service rate is the reciprocal of the length of the holding period, the arrival rate depends on the frequency of upward revisions in the Ncompanies sample and the maximum line allowed in the system is 0.
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D. Givoly and J. Lakonishok, Financial analysts' forecasts of earnings
Specifically, more transactions and therefore higher transaction costs per time unit are expected as the minimal revision size is reduced, the entry becomes earlier and the early exit option is selected. In all three cases, a higher return might offset or even more than offset the greater transaction costs. The results show that an investor could gain a substantial abnormal return by acting upon FAF revisions. For example, when his transactions are triggered by upward revisions of 5 ~o or more and employing rule (1, 2), an investor could obtain annually an abnormal return, net of 1 ~ transaction costs, of 9.55 ~. The annual normal return during the same year was 7.3 ~, so that the application of this trading policy resulted in an improvement of 130 ~ over a buy-and-hold policy. Almost all the abnormal returns reported in the table are statistically significant. It should be noted that this performance was achieved by utilizing only information which is publicly available to all investors for some time (on the average, more than two weeks) which implies that market reaction to revisions in earnings is relatively slow. The results indicate that the performance of an investor who is more selective and responds only to large revisions is better than that of an investor who acts upon any revision. This is true even if no transaction costs exist. Another finding is that, in all cases, rule (1,D), in spite of the higher transaction costs (due to more transactions), was superior to rule (1, 2). This is consistent with the results reported in table 3, which showed that stock prices reacted adversely to downward revisions. Rule (1, D), under which the stock is sold prematurely upon downward revision, outperformed rule (1, 2), under which sale always occurred at the end of month 2. The above conclusions did generally hold when year-by-year analysis was conducted. From table 7, it can be shown that the only year in which the trading policy did poorly and was even inferior to a buy-and-hold policy (at least when transaction costs were introduced), was 1968. In all other years, the abnormal returns from adopting trading policies based on F A F revisions were quite sizable and statistically significant.
5. Summary and conclusions The paper attempted to assess the value of financial analysts' earnings forecasts to individual investors. This was done by examining the performance of various trading policies which were based on revisions of earnings forecasts. It was demonstrated that an investor who uses only publicly available information on F A F and incurs normal transaction cost could more than double his return compared to a buy-and-hold strategy. These empirical results indicate that contrary to the implications of the efficient market hypothesis, the market does not adjust immediately to new
D. Givoly and J. Lakonishok, Financial analysts' forecasts of earnings
233
information. Another conclusion which can be drawn and which is also supported by other studies is that earnings are an important determinant of stock prices. It appears, therefore, that financial analysis is not fruitless to investors as implied by the efficient market hypothesis: There is a premium to investors who are provided with and act upon accurate and timely earnings forecasts. References Basu, S., 1977, Investment performance of common stock in relation to their price - - earnings ratios: A test of the efficient market hypothesis, Journal of Finance, June, 663-683. Black, F., 1973, Yes Virginia, there is hope: Tests of the value line ranking system, Financial Analyst Journal, Sept.-Oct., 10-14. Brenner, M., 1974, The sensitivity of the efficient market hypothesis to alternative specifications of the market model, Unpublished Ph.D. dissertation (Cornell University, Ithaca, NY). Davies, P. and M. Cane, 1978, Stock prices and the publication of second-hand information, The Journal of Business, Jan., 43-56. Givoly, D. and J. Lakonishok, 1979, The information content of financial analysts' forecasts of earnings: Some evidence on semi-strong inefficiency, Journal of Accounting and Economics 1, no. 3, Dec., 165-185. Jones, C. and R. Litzenberger, 1970, Quarterly earnings reports and intermediate stock price trends, Journal of Finance, March, 143-148. McKibben, W., 1972, Econometric forecasting of common stock individual returns: A new methodology using fundamental operating data, Journal of Finance, May, 371-380. Watts, R., 1978, Systematic 'abnormal' returns after quarterly earnings announcements, Journal of Financial Economics, Sept., 127-150.
FINANCIAL ANALYSTS' FORECASTS OF EARNINGS Their Value to Investors
Dan GIVOLY and Josef LAKONISHOK Faculty of Management, Tel-Aviv University, Ramat-Aviv, Tel-Aviv, Israel Received August 1979, final version received December 1979
This paper attempts to determine whether financial analysts' forecasts of earnings are useful to investors. This is accomplished by devising and evaluating the performance of trading rules under which transactions are triggered by revisions in earnings forecasts. The main finding is that an investor who acts upon the publicly available revisions of earnings forecasts can consistently outperform a buy-and-hold policy; in fact, such an investor could more than double his return. The results are inconsistent with the efficient market-hypothesis and indicate that the market reacts gradually rather than instantaneously to new information.
1. Introduction
Forecasts of earnings are perhaps the most conspicuous single product of the financial analysts' industry. These forecasts are a demanded and paid-for commodity used by small and institutional investors in making their investment decisions. While the social contribution of financial analysts' forecasts (hereafter FAF) in improving the resource allocation process is generally recognized, their value to the individual client is not entirely evident, at least not to those (and there are quite a few of them) who subscribe to the 'efficient market hypothesis'. According to this hypothesis, all publicly available information is already impounded in the security price in a correct manner. The implication is that, to the extent that they rely on publicly available information (financial statements, management forecasts, macro-economic developments, etc.), FAF do not convey new information; in blunter words, attempts by analysts to produce above-normal profits for their clients through a rigorous and professional security analysis are, according to the hypothesis, doomed and, in the presence of transaction costs and the analysts' own fees, are even counterproductive. Moreover, even if part of the input to FAF is not publicly available, their mass distribution (FAF are mailed simultaneously to hundreds or thousands of clients) makes them a public good immediately upon release and precludes their recipients from taking advantage of what could have been an exclusive oiece of information.
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Could the individual investor still beat the market by utilizing information conveyed by FAF? This is the main question addressed by the present study. Indeed, there is an ample empirical evidence that supports the efficient market hypothesis and therefore produces, a priori, a negative answer to the above question. However, it should also be recognized, that in several instances and for a certain information, the stock market was found to be inefficient. Jones and Litzenberger (1970) for example, observed that prices did not respond instantaneously to quarterly reports of a highly favorable content. In another study, by McKibben (1972) it was demonstrated that publicly available information on rates of return, change in earnings, growth relative to the price-earnings ratio and on payout ratio could be used in selecting a superior portfolio. Another variable, the P-E ratio, was found by Basu (1977) to be useful to investors in identifying potentially-profitable stocks. A different type of publicly available information, the value line ranking, produced, in a study by Black (1973) abnormal gains to investors who acted according to it~ Finally, in a more recent study, Davies and Canes (1978) reported a delayed market reaction to analysts' buy-or-sell recommendations. The findings of these and of several other studies cast some doubt on the existence of perfect market efficiency and indicate that the research question of this study - - can investors exploit publicly available FAF? - - is not trivial. If FAF have any impact on prices, one might expect that an upward revision of earnings would trigger an increase in the price of the stock and that a downward revision of earnings would likewise result in a decrease in the price. Furthermore, if market reaction is not immediate, an investor could devise a profitable trading policy based on the content of published forecasts. One possible way to implement this policy is for an investor to search systematically and add to his portfolio companies whose earnings forecast has just been revised upward. These companies will be held for a period of time sufficient to exploit the subsequent increase in price and will then be dropped from the portfolio to be substituted by a new 'just-been-revised' stock. The portfolio of an investor who adopts such a policy would consist of stocks with recent upward revisions and would exhibit a relatively high turnover. In a recent study Givoly and Lakonishok (1979) provide evidence which indicate that FAF might be relevant to investors, i.e., that they stir market reaction. Whether or not an investor can exploit information on revisions of FAF in the manner described depends on several factors: (1) the speed by which the market adjusts itself to FAF revisions (immediate adjustment would preclude any possibility of above-normal profit), (2) trading policy specifics - - such as the length of the holding period or the consideration given to downward revisions, (3) the transaction cost involved. The objective of the study is to test the hypothesis that investors can utilize the publicly
D. Givoly and J. Lakonishok, Financial analysts' forecasts of earnings
223
available FAF revisions to produce abnormal returns. Acceptance of the hypothesis might indicate both that FAF are relevant to investors and that market reaction to their publication is slow. The paper is organized as follows. The next section describes and discusses the data. The section that follows examines the effect of FAF revisions on the behavior of stock prices. Some trading rules based on FAF revisions are then constructed, their application is simulated for portfolios of different sizes and their performance is measured. Summary of the results and a discussion of the implications for investors conclude the paper.
2. Sample and data The sample consists of 49 companies in three industries: Chemicals and Allied Products (Industry 28 in the Standard Industrial Two-Digit Classification), Petroleum, Refining and Related Industries (Industry 29) and Transportation Equipment (Industry 37). All the companies in the sample are listed on the NYSE and have a fiscal year ending December 31. For each company's actual earnings, EPS forecasts and monthly stock returns were collected for the eight years 1967 to 1974. Forecasts of EPS were collected from Standard and Poor's Earnings Forecaster. A weekly publication that first appeared in 1967, the Earnings Forecaster lists in each issue the outstanding EPS forecasts for about 1500 companies. The forecasts are those made by S & P itself and by about fifty other security analysts and brokerage houses who agreed to submit their forecasts, upon release, for the publication. Typically, three to five forecasters were actively engaged in forecasting the earnings of a given company. As many as 15 different forecasts might simultaneously be available for companies with a widely traded stock. The representative of the group of forecasters for each company, was the forecaster with the greatest number of revisions. This most active forecaster is probably the first to react to new information and is likely to be the most watched and followed by investors. In most cases, Standard and Poor's was the most active forecaster. The three other frequent representatives were Bache, UBS and Shearson-Hammil. The actual EPS were collected from the Earnings Forecaster. The dates of the actual announcement were collected from the Wall Street Journal. The announcement date is the date of the announcement of the audited statements or the release date of the preliminary earnings, whichever is earlier. The rates of returns were retrieved from the monthly CRSP tape. Since FAF is the basic data of the study, a description of some of their characteristics is perhaps in order. Table 1 presents the size distribution of the revisions in FAF. The table reveals that the typical revision is small indeed: The median revision over the
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D. Givoly and J. Lakonishok, Financial analysts"forecasts of earnings
eight years of about 4 ~ , and less than 2 5 ~ of the revisions exceed 10~. The large frequency of small revisions might either reflect the responsiveness and sensitivity of analysts to events and information which have only minute effect of earnings, or they might reflect the existence, in large scale, of idle revisions whose sole purpose is to demonstrate activity. Another feature of
Table 1 Distribution of revision size by year (percentage). Year
First quartile
Median
Third quartile
All years
1.8 1.1 1.8 1.5 1.8 1.9 1.4 2.1 2.8
3.6 2.4 2.6 2.7 4.3 3.5 3.0 4.5 5.7
6.9 4.5 5.5 5.5 7.4 7.6 5.5 8.4 10.9
1967 1968 1969 1970 1971 1972 1973 1974
Table 2 Relative frequency of revisions by month of release.
Percentage of all revisions
Percentage of all revisions
Jan.
Feb.
MarchApril May
June
10.4
8.6
8.9
10.7
5.7
July
Aug.
Sept. Oct.
Nov.
Dec.
7.0
4.7
10.8
6.6
9.9
7.9
8.8
the revisions is that they do not concentrate systematically in certain months (table 2); still, there is a mild concentration during April-May (following the release of the annual reports and the first quarter) during July and August (following the release of the second quarter) during October and November (following the release of the third quarter) and during January (towards the release of the annual reports). The concentration is somewhat more pronounced when only revisions of over 10~o are considered. The lack of clear clustering around the announcement months might indicate that the input to FAF is not limited to accounting information.
D. Givoly and J. Lakonishok, Financial analysts"forecasts of earnings
225
3. Trading rules and performance measurement To test whether investors can profit from information on FAF revisions, trading policies that utilize this information were devised and their performance was measured and compared to that of a buy-and-hold policy. Examined were realistic trading policies whose common feature was their availability to many or to all investors. Portfolios of four different sizes, 5, 10, 15 and 20 stocks, were constructed, each representing a different level of diversification. The selection of the stocks that composed the portfolio was related to the occurrence of FAF revisions in the following way: Each month, starting with the beginning of 1967, the sample of stocks was searched for stocks which have just had an upward revision of their earnings. Such stocks were added to the portfolio and were held for a certain period of time. The exact timing of the purchase and of the disposal of the stock was determined by the trading rule employed. Another parameter of the investment policy was a filter to screen material revisions. Some of the revisions might be too small to stir a meaningful market reaction. Moreover, reaction to any revision, regardless of its size, would increase the number of transactions and might boost transaction costs considerably. Three alternative filters were used: (1) 0%, or no filter (i.e., all revisions were acted upon), (2) 5 % and over, (3) 10 % and over. The search for new stocks was being conducted during all months in which there were 'openings' in the fixed-size portfolio. In months where the number of stocks eligible for inclusion exceeded the portfolio size, the candidates for inclusion were ranked according to the magnitude of the revision and the stocks with the largest revisions entered. According to this investment procedure, a portfolio of size n contains, at any given month, n stocks, at most; the identity of the stocks, though, changes over time. In other words, the portfolio size represents the number of 'slots' available for investment rather than the number of stocks held throughout the period. Due to the limited number of stocks in the sample (49), in some of the periods and for some of the slots of the portfolio, funds remained idle. This is particularly true for the larger portfolios (15 or 20 stocks). The implications of such idle funds will be discussed in the following section. Denoting the upward revision month as month 0 and the surrounding months according to their relative position with respect to the upward revision month (i.e., by - 1 , + 1, etc.), the following trading rules were used:
Rules with predetermined exit (1) Buy at the beginning of month - 1 and sell (exit) at the end of month 2 [ ( - 1 , 2) in notation form].
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D. Givoly and J. Lakonishok, Financial analysts' forecasts of earnings
(2) Buy at the beginning of month 0 and sell at the end of month 2 [(0, 2) in notation form]. (3) Buy at the beginning of month 1 and sell at the end of month 2 [(1, 2) in notation form].
Rules with early exit (4) Same as (1), but sell either at the end of month 2 or at the end of a month with a downward revision, whichever comes first [ ( - 1 , D) in notation form]. (5) Same as (2), but sell either at the end of month 2 or at the end of a month with a downward revision, whichever comes first [(0,D) in notation form]. (6) Same as (3), but sell either at the end of month 2 or at the end of a month with a downward revision, whichever comes first [(1, D) in notation form]. Since the exact time of the revision is some time during month 0, it appears that rules (1), (2), (4), and (5) are available only to investors with some preknowledge on the forthcoming revisions. This might not be accurate. There is always some lag between the generation of forecasts by analysts and their publication in the Earnings Forecaster. (This lag is caused by the processing time of the publication and the fact that the publication is issued only once a week.) In addition, the revision might be available to some privileged clients even before its release to S& P. Hence, rules (2) and (5) might be available to many investors and even rules (1) and (4) might still be available, although to much fewer investors. The holding period under all trading rules terminates on or before the end of month +2. Empirical evidence shows that price increases following an upward revision are fully exploited by the end of month + 2. This and other related evidence are presented in the next section. One might wonder why information on downward revisions has, for the most part, been ignored. Trading rules under which a short transaction occurs when a downward revision is made, could be considered. Indeed, as shown later, downward revisions are associated with an adverse price reaction of the stocks concerned. Yet, the profitability of such a rule is doubtful because its yield must be greater than that on a buy-and-hold policy. (The latter is expected to be positive.) The abnormal returns produced by the various rules were measured and compared to the yield from a buy-and-hold policy. The abnormal return was defined here as the excess return over the expected (normal) return under the widely used 'market model'. According to this model, the return of the
D. Givoly and J. l_,akonishok, Fimmcial analysts' forecasts of earnings
227
individual stock is associated with the market return so that
IR.,)=
+ fl,R,,,,,
where/~it denotes the return of security i at month t, ~i and fli are coefficients and Rmt is the actual market return. 1 The abnormal return in month t is measured by the difference e.lt=Rit-o~i+fliRm, where Rit and Rmt a r e the observed values of the respective returns. 2 To facilitate the interpretation of the results, the originally-derived abnormal returns (e,) were standardized in such a way as to produce each month a zero abnormal return from a buy-and-hold policy. The results are reported in terms of these standardized abnormal returns) The total abnormal return produced by a given trading rule over the eight-year period for a portfolio of size n, was the simple average of the compounded abnormal returns over the eight years in each of the n slots.'* 4. Empirical results The first analysis of the data involved the examination of price reaction to F A F revisions. If FAF are of any relevance to investors, one would expect an above-normal return around upward revisions and a below-normal return 1Brenner (1974) tested the sensitivity of conclusions of empirical studies on the efficiency of capital markets to various specifications of market models (one factor model, two factor model with risk free rate, two factor model with zero-beta portfolio, three factor model with unsystematic risk and a naive model which assumes that: r,=0 and fl-1). He found that empirical results are only slightly affected by the specification of the market model. 2The values of ~t~ and fl~ are estimated from a regression equation. Since the test period (the period during which abnormal returns axe being measured) should be completely divorced from the estimation period, ~ and fit used in year T are estimated by data of prior years. Specifically, the parameters for year T are estimated from the four years (forty-eight months) preceding that year. aThe residuals from the market line, e,, were standardized with respect to their contemporaneous cross-section average, as follows:
where ~, is the standardized abnormal return of stock i at month t, and 1
ni=l
(n is the number of firms in the sample). By performing this transformation, we actually force the average abnormal return derived from a buy-and-hold strategy to be zero in each month and facilitate the interpretation of the results. 4Initially, equal funds are invested in each slot in the portfolio. However, later, after the first turnover of stocks occurred, this is not true any more: When a stock exits the portfolio, the initial fund plus the accumulated return are available for reinvestment in the new stock. Since the returns vary among stocks, the funds reinvested in different slots of the portfolio beyond the first investment month are not necessarily equal.
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D. Givoly and J. Lakonishok, Financial analysts"forecasts of earnings
around downward revisions. Furthermore, the duration of the price effect might be indicative of the market efficiency and of the degree by which publicly available information in FAF revisions can be profitably used by investors. Table 3 indicates that stock prices are affected by, or are at least associated with revisions of earnings by FAF. The table presents the abnormal returns produced, on the average, over selected periods around FAF revisions. The abnormal returns were computed per each holding Table 3 Mean abnormal return per holding period by industry and direction of revision (percentage)." Direction (upward/ downward)
4-month period - 1, 0, 1, and 2
3-month period 0, 1, and 2
2-month period 1 and 2
Total sample
U D
4.7 - 3.8
3.1 - 1.9
2.7 - 1.0
Industry 28
U D
5.0 - 3.4
2.9 - 1.6"
2.1 - 1.2
Industry 29
U D
2.3 - 4.5
2.3 - 3.2
2.4 - 1.9
Industry 37
U D
12.7 -3.1
10.3 -2.3
4.9 1.2"
"All values except those marked by asterisks are different from zero at the 3 % (or less) significance level.
period, and were not annualized. The results are shown separately for periods around upward revisions and for periods around downward revisions. Breakdown by industry is also provided. The abnormal returns were derived according to the computational procedure described in the previous section and they represent the difference between the actual return and the expected normal return on the stock (for downward revisions, the sign of the difference is reversed). Three holding periods were examined - months 1, 2, months 0, 1, and 2, and months - 1 , 0, 1, and 2. Results for months before month - 1 and after month + 2 were also examined, but no significant price effect was revealed in them. The average abnormal return for each of the three holding periods was computed for various revision sizes. As not to confuse the presentation, only the results for revisions larger than 5 % are tabulated. In general, the results for other size-groups were similar. However, on the average, greater abnormal returns were observed in holding periods around large revisions. In all three industries, abnormal returns were found during each of the holding periods around upward revisions. The same result, with the
D. Givoly and J. Lakonishok, Financial analysts' forecasts of earnings
229
exception of one holding period in Industry 37, is reported for periods around downward revisions. The values of all the abnormal returns for the entire sample and the values of most of the abnormal returns for the individual industries are significantly different from zero at the 1% or less significance level. 5 The magnitude of the abnormal returns should also be noted. An upward revision (of over 5 %) was associated with an average price increase of 4.7 % over the four months - 1 , 0, 1, and 2. The abnormal return over the two months 1 and 2 around upward revisions reached 2.7 %. The price reaction to downward revisions was almost as strong as to upward revisions. The Table 4 Mean annual abnormal returns for a 5-stock portfolio by revision size, trading rule, and level of transaction cost (percentages). Level of transaction cost
Trading rule (1, 2)
(1, D)
0%+
0% 0.5 % 1.0%
10.18 4.93 -0.29
5%+
0% 0.5 % 1.0%
10%+
0% 0.5 % 1.0 %
Revision size
(0, 2)
(0, D)
( - I, 2)
( - 1, D)
10.38 3.92 -2.30
8.28 4.85 1.55
11.20 7.00 2.93
12.10 9.19 6.36
15.86 12.45 9.18
9.41 6.46 3.68
13.31 10.29 7.53
10.f8 8.02 5.29
8.84 6.20 3.57
16.98 14.77 12.81
12.42 10.01 7.77
7.78 6.12 4.52
7.54 5.85 4.23
7.88 6.23 4.62
8.11 6.57 5.08
12.15 9.18 6.36
15.91 12.50 9~24
same results were found in each industry and for each year. (The results by years were not presented.) In sum, the findings strongly suggest that stock prices do react in a predictable way to F A F revisions and that the adjustment of the stock market to their release is slow and extends beyond the revision month. Whether or not an investor could actually gain by utilizing information on F A F revisions must yet be tested in the context of a trading policy under which exact trading rules are specified, transaction costs are considered, and a standard measure of performance is set. This test was performed within the framework of the trading policy described in the previous section. The results for a 5-stock portfolio and for a 15-stock portfolio are presented in tables 4, 5, 6, and 7. The results for a 10-stock portfolio and for a 20-stock portfolio were basically the same. STwo statistical tests were used, a parametric and a non-parametric test. The parametric test used was a t-test, and the non-parametric test used was a binomial test in which the variable was the occurrence of abnormal return in the right direction, in a given holding period.
D. Givoly and J. Lakonishok, Financial analysts' forecasts of earnings
230
Table 5 Mean annual abnormal returns for a 15-stock portfolio by revision size, trading rule, and level of transaction cost (percentages).
Level of transaction cost
(1,2)
(1,D)
(0,2)
(0, D)
(-1,2)
( - 1 , D)
0%+
0% 0.5 % 1.0 %
7.71 3.49 0.83
7.79 3.82 0.65
6.33 3.67 0.99
8.12 6.41 2.53
10.43 8.56 5.80
11.02 8.21 5.77
5%+
0% 0.5 % 1.0 %
6.39 5.13 2.87
7.11 5.26 3.45
5.49 3.98 2.54
5.57 4.25 2.61
10.71 8.98 6.85
8.91 7.05 5.56
10~+
0% 0.5 % 1.0 %
3.72 2.77 1.87
3.98 3.02 2.12
3.81 2.74 2.29
4.08 3.28 2.53
6.60 5.62 4.81
6.79 5.93 4.96
Revision size
Trading rule
Table 6 Mean annual abnormal returns for a 15-stock portfolio adjusted for idle time by revision size, trading rule, and the level of transaction cost (percentages).
Trading rule (1, 2) Revision size 0%+
Level of transaction cost 0% 0.5 %
1.0% 5%+
10 % +
Trading rule (1, D)
Percentage of idle time 12.94 5.79
39 %
1.36"
Percentage of idle time 15.20 7.34
47 %
1.22,
0% 0.5 % 1.0%
22.11 17.51 9.55
69 %
26.21 18.98 12.20
71%
0% 0.5 % 1.0%
25.62 18.60 12.29
84 %
29.45 21.80 14.92
85 %
"All values except those marked by asterisks are significant at the 1% significance level.
The major finding is that the trading policy devised which utilized information on FAF revisions was profitable, i.e., generated abnormal returns. Obviously, the profitability of any trading rule diminished as transaction cost increased. Still, a positive abnormal return was generally produced, even under a 1 ~ transaction cost (each direction). The performance of the trading policy is impressive indeed if one considers the frequent and often long periods during which the funds are idle due to lack of investment opportunities (i.e., due to the absence of upward revisions ), no abnormal returns are produced during idle time. The effect of the idle periods becomes more pronounced as the portfolio size and the minimal
D. Givoly and J. Lakonishok, Financial analysts' forecasts of earnings
231
revision size required to trigger a transaction increase and as the holding period gets shorter. 'For instance, the idle time percentage for a 5-stock portfolio, policy ( - 1 , 2) and 0~o revision size was l lY/o whereas the percentage for a 15-stock portfolio, policy (1,D) and 10~ revision size was 84 ~. The idle time is a consequence of the limited number of companies in the sample (49). In real life, however, investors can conceivably watch a large number of companies for revisions of their earnings forecasts without incurring a material search cost. It can be shown that it is enough to follow 250 stocks in order to cut the idle time percentage in a 5 and a 15-stock portfolio down to 3 ~ and 5 ~, respectively. 6 Table 7 Mean annual abnormal returns for a 15-stock portfolio adjusted for idle time by year, trading rule, and level of transaction cost (percentages). Trading rule (1, 2) (transaction cost)
Trading rule (1, D) (transaction cost)
Year
0~
0.5 ~
1.0 Y/o
0~
0.5 ~
1967 1968 1969 1970 1971 1972 1973 1974
23.1 3.1 16.7 47.4 16.9 32.2 20.7 27.8
15.0 -2.5 10.1 39.5 10.4 24.1 14.2 24.9
7.3 -7.8 4.0 32.0 4.3 16.6 8.1 16.7
20.2 -2.6 21.4 48.4 25.7 29.6 23.6 28.4
11.1 -8.5 13.5 39.7 18.0 22.7 16.94 20.16
1.0 Y/o 2.7 - 14.0 6.2 30.9 10.7 16.3 10.7 16.2
aAll values except those marked by asterisks are significant at the 1% significance level.
In other words, the results shown in tables 4 and 5 might quite likely be an underestimate of the performance that could be achieved from employing the same trading policy in real life. Table 6 presents the abnormal returns for trading rules (1, 2) and (1, D) for a portfolio of 15 stocks, after an adjustment for idle time was made. The adjustment was carried out by raising the abnormal return to the power of 1 / 1 - p, where p is the idle time percentage. This procedure assumed that the same opportunities which existed during busy periods prevailed also during idle times. This assumption does not seem to be restrictive since the average return measured over busy periods was based on a large enough sample of transactions. The abnormal returns presented in table 6 are a function of several features of the trading rule employed: the minimal revision size, the entry time and the exit time. Usually, a switch from one rule to another involves a trade-off between expected total transaction costs and expected gross return. 6The estimate is derived by formulating the problem as a queuing system in which the number of servers is the number of slots in the portfolio, the service rate is the reciprocal of the length of the holding period, the arrival rate depends on the frequency of upward revisions in the Ncompanies sample and the maximum line allowed in the system is 0.
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D. Givoly and J. Lakonishok, Financial analysts' forecasts of earnings
Specifically, more transactions and therefore higher transaction costs per time unit are expected as the minimal revision size is reduced, the entry becomes earlier and the early exit option is selected. In all three cases, a higher return might offset or even more than offset the greater transaction costs. The results show that an investor could gain a substantial abnormal return by acting upon FAF revisions. For example, when his transactions are triggered by upward revisions of 5 ~o or more and employing rule (1, 2), an investor could obtain annually an abnormal return, net of 1 ~ transaction costs, of 9.55 ~. The annual normal return during the same year was 7.3 ~, so that the application of this trading policy resulted in an improvement of 130 ~ over a buy-and-hold policy. Almost all the abnormal returns reported in the table are statistically significant. It should be noted that this performance was achieved by utilizing only information which is publicly available to all investors for some time (on the average, more than two weeks) which implies that market reaction to revisions in earnings is relatively slow. The results indicate that the performance of an investor who is more selective and responds only to large revisions is better than that of an investor who acts upon any revision. This is true even if no transaction costs exist. Another finding is that, in all cases, rule (1,D), in spite of the higher transaction costs (due to more transactions), was superior to rule (1, 2). This is consistent with the results reported in table 3, which showed that stock prices reacted adversely to downward revisions. Rule (1, D), under which the stock is sold prematurely upon downward revision, outperformed rule (1, 2), under which sale always occurred at the end of month 2. The above conclusions did generally hold when year-by-year analysis was conducted. From table 7, it can be shown that the only year in which the trading policy did poorly and was even inferior to a buy-and-hold policy (at least when transaction costs were introduced), was 1968. In all other years, the abnormal returns from adopting trading policies based on F A F revisions were quite sizable and statistically significant.
5. Summary and conclusions The paper attempted to assess the value of financial analysts' earnings forecasts to individual investors. This was done by examining the performance of various trading policies which were based on revisions of earnings forecasts. It was demonstrated that an investor who uses only publicly available information on F A F and incurs normal transaction cost could more than double his return compared to a buy-and-hold strategy. These empirical results indicate that contrary to the implications of the efficient market hypothesis, the market does not adjust immediately to new
D. Givoly and J. Lakonishok, Financial analysts' forecasts of earnings
233
information. Another conclusion which can be drawn and which is also supported by other studies is that earnings are an important determinant of stock prices. It appears, therefore, that financial analysis is not fruitless to investors as implied by the efficient market hypothesis: There is a premium to investors who are provided with and act upon accurate and timely earnings forecasts. References Basu, S., 1977, Investment performance of common stock in relation to their price - - earnings ratios: A test of the efficient market hypothesis, Journal of Finance, June, 663-683. Black, F., 1973, Yes Virginia, there is hope: Tests of the value line ranking system, Financial Analyst Journal, Sept.-Oct., 10-14. Brenner, M., 1974, The sensitivity of the efficient market hypothesis to alternative specifications of the market model, Unpublished Ph.D. dissertation (Cornell University, Ithaca, NY). Davies, P. and M. Cane, 1978, Stock prices and the publication of second-hand information, The Journal of Business, Jan., 43-56. Givoly, D. and J. Lakonishok, 1979, The information content of financial analysts' forecasts of earnings: Some evidence on semi-strong inefficiency, Journal of Accounting and Economics 1, no. 3, Dec., 165-185. Jones, C. and R. Litzenberger, 1970, Quarterly earnings reports and intermediate stock price trends, Journal of Finance, March, 143-148. McKibben, W., 1972, Econometric forecasting of common stock individual returns: A new methodology using fundamental operating data, Journal of Finance, May, 371-380. Watts, R., 1978, Systematic 'abnormal' returns after quarterly earnings announcements, Journal of Financial Economics, Sept., 127-150.