Partitioning market efficiencies by analyst attention: the case of annual earnings announcements
| dc.contributor.author | Dempsey, Stephen J. | en |
| dc.contributor.department | Business | en |
| dc.date.accessioned | 2015-06-29T22:06:55Z | en |
| dc.date.available | 2015-06-29T22:06:55Z | en |
| dc.date.issued | 1985 | en |
| dc.description.abstract | This study addresses the empirical question of heterogeneous market efficiency characteristics, specifically as they are attributable to divergent levels of professional securities analyst attention. As a significant group of information intermediaries, analyst institutions conceivably influence, in a profound manner, the efficiency with which security prices respond to new information. Consistent with this notion is the hypothesis that the securities of firms which are neglected in terms of analyst coverage exhibit price inefficiencies relative to their closely followed counterparts. Two market efficiency constructs with respect to annual earnings announcements are examined in this study. Preannouncement information efficiency is guaged by the degree to which security prices appear to lead or anticipate the information content of subsequent public earnings releases. Such price behavior is indicative of the market's ability to acquire and, process interim, signals that are relevant to the determination of proper and timely security valuations. Postannouncement, or semi-strong-form, efficiency is in turn referenced by the relative absence of anomalous "drifting" patterns in postdisclosure returns. The presence of significant drifts is inconsistent with a market that adjusts quickly and unbiasedly to signals that are transmitted publicly. Sample firms taken from the NYSE are ranked into three groups according to their relative following by the professional securities analyst community. Analyst attention is surrogated by the number of investment houses providing annual earnings per share forecasts for companies listed in the Institutional Brokers Estimate System (IBES) computer file. The delineation of the three attention concentration groups' relative efficiency profiles is accomplished by means of two uniquely derived metrics that restate cumulative abnormal returns (CAR's) into an ordered domain of pre- and postannouncement efficiency structures. The CAR's are derived from dailly price data immediately surrounding annual earnings announcement dates for the calendar years ended 1976 through 1982. Owing to the nonnormal distributional properties of the inefficiency metrics, two nonparametric procedures are employed to detect group mean differences. The results overwhelmingly indicate that both pre- and postannouncement efficiency are positively associated with professional analyst attention. Moreover, the detected efficiency differences cannot be attributed to firm size effects or to the extent of the market's forecast error -- two factors that have previously been established in the empirical literature to be associated with event period CAR magnitudes. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.extent | x, 198 leaves | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | http://hdl.handle.net/10919/53866 | en |
| dc.language.iso | en_US | en |
| dc.publisher | Virginia Polytechnic Institute and State University | en |
| dc.relation.isformatof | OCLC# 14231829 | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject.lcc | LD5655.V856 1985.D456 | en |
| dc.subject.lcsh | Efficient market theory | en |
| dc.subject.lcsh | Wages and labor productivity | en |
| dc.subject.lcsh | Securities | en |
| dc.subject.lcsh | Stock exchanges | en |
| dc.subject.lcsh | Marketing -- Costs | en |
| dc.title | Partitioning market efficiencies by analyst attention: the case of annual earnings announcements | en |
| dc.type | Dissertation | en |
| dc.type.dcmitype | Text | en |
| thesis.degree.discipline | Business | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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