This study investigates whether market learning explains the absence of the accrual anomaly in recent years by examining three conditions associated with the presence of the anomaly in prior research: (i) a differential relation between future earnings and cash flows versus accruals, (ii) incorrect weighting of cash flows and accruals by investors when predicting earnings, and (iii) association of earnings forecast errors with returns. All of these conditions are widely documented in the anomaly period. In the no-anomaly period, I continue to find a differential relation of cash flows and accruals with future earnings. However, investors appear to correctly weight accruals and cash flows in their earnings predictions implicit in beginning-of-year security prices, consistent with learning. This study also investigates whether improvements in analyst forecasts contribute to investor learning and the absence of the anomaly. The association between analyst optimism and accruals is weaker in the no-anomaly period, but is still statistically significant. Furthermore, the anomaly ended simultaneously for firms followed by analysts and for non-followed firms, suggesting that improvements in analyst forecasts alone cannot account for improved market efficiency with respect to accruals. The results suggest that the anomaly was similar for firms held by institutional investors and for firms with no institutional holdings before the discovery of the anomaly while the anomaly ended sooner for held firms than for non-held firms after the discovery of the anomaly, consistent with the conjecture that arbitrage by institutional investors reduce the anomaly. Overall, the findings are consistent with market learning and suggest that improvement in investors' interpretation of accruals after the discovery of the anomaly explains the end of the anomaly. This improvement in investor learning is not due to changes in analysts' forecasting behavior, however.

Severe thunderstorm warnings (SVTs) are released by meteorologists in the local forecast offices of the National Weather Service (NWS). These warnings are issued with the intent of alerting areas in the path of severe thunderstorms that human and property risk are elevated, and that appropriate precautionary measures should be taken. However, studies have shown that the spatial distribution of severe storm warnings demonstrates bias. Greater numbers of severe thunderstorm warnings sometimes are issued where population is denser. By contrast, less populated areas may be underwarned. To investigate the spatial patterns of these biases for the central and southeastern United States, geographically weighted regression was implemented on a set of demographic and land cover descriptors to ascertain their patterns of spatial association with counts of National Weather Service severe thunderstorm warnings. GWR was performed for each our independent variables (total population, median income, and percent impervious land cover) and for all three of these variables as a group. Global R2 values indicate that each individual variable as well as all three collectively explain approximately 60% of the geographical variation in severe thunderstorm warning counts. Local R2 increased in the vicinity of several urban regions, notably Atlanta, Washington, D.C., St. Louis, and Nashville. However, the independent variables did not exhibit the same spatial patterning of R2. Some cities had high local R2 for all variables. Other cities exhibited high local R2 for only one or two of these independent variables. Median income had the highest local R2 values overall. Standardized residuals confirmed significant differences among several NWS forecast offices in the number and pattern of severe thunderstorm warnings. Overall, approximately half of the influences on the distribution of severe thunderstorm warnings across the study area are related to underlying land cover and demographics. Future studies may find it productive to investigate the extent to which the spatial bias mapped in this study is an artifact of forecast culture, background thunderstorm regime, or a product of urban anthropogenic weather modification.

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Made available in DSpace on 2010-04-20T20:51:42Z (GMT). No. of bitstreams: 3 142184.pdf.jpg: 20410 bytes, checksum: 720b476fe32b25d220b0dde4d663ee25 (MD5) 142184.pdf: 373613 bytes, checksum: 1b6743be6830c2ae7ab8245255b9ad6b (MD5) 142184.pdf.txt: 120505 bytes, checksum: be4e63d920365eb874f914450f641b26 (MD5) Previous issue date: 2005-10-17T00:00:00Z / A presente dissertação analisa o erro de projeção dos analistas de investimentos do sell side, definido como a diferença entre o consenso das projeções dos analistas e o resultado reportado pela empresa. O tamanho do erro de projeção é uma medida da qualidade das projeções dos analistas de um determinado mercado de capitais. Uma vasta literatura acadêmica mostra que uma melhora na qualidade das projeções dos analistas, medida através de uma diminuição do tamanho do erro de projeção, está relacionada com a redução da assimetria de informação e com um aumento do valor de mercado das empresas. São testadas duas regressões, nas quais características das empresas, como setor, tamanho, endividamento e variabilidade do lucro, e características do ambiente de informação da empresa, como listagem de ADR, número de analistas que acompanham a empresa e convergência das projeções, são testadas contra duas métricas do erro de projeção, acurácia e viés. Nossas hipóteses são que existem fatores que influenciam de maneira significativa o tamanho do erro de projeção (acurácia) e o viés das projeções (viés). Estas hipóteses foram confirmadas, isto é, nossas regressões apresentaram pelo menos um fator que se mostrou significativo estatisticamente para influenciar o tamanho do erro de projeção (hipóteses H1 e H2) ou o seu viés (hipótese H3). Entretanto, os resultados mostram que vários fatores que se mostram significativos em testes conduzidos em mercados desenvolvidos – tais como tamanho, endividamento e variabilidade do lucro – não se mostraram significativos no mercado brasileiro. Por outro lado, os fatores relacionados com o resultado do ano projetado ou do ano anterior se mostraram fortemente significativos. Acreditamos que os resultados podem ser explicados de três maneiras: 1) ou a capacidade de adicionar valor dos analistas em relação a modelos estatísticos de projeção é muito pequena, devido à sua falta de habilidade; ou 2) a instabilidade macroeconômica é tão grande domina todos os outros fatores que poderiam influenciar o tamanho do erro de projeção; ou 3) os resultados das empresas nos mercados desenvolvidos são tão administrados, isto é, tão estáveis, que permitem que fatores mais sutis como o tamanho, o nível de endividamento e a variabilidade do lucro se tornem significativos. Esta dissertação não permite distinguir qual das explicações é a correta. Uma de suas limitações é não incluir variáveis referentes à habilidade e experiência dos analistas e, também, variáveis relacionadas a fatores como governança corporativa e disclosure de informações. Em uma linha de pesquisa muito extensa nos países desenvolvidos, mas praticamente inexistente no Brasil, esperamos que estudos futuros supram estas lacunas e nos permitam entender melhor a questão da qualidade das projeções de resultados no contexto brasileiro. / The current dissertation analyses the forecast error of the sell side analysts in the Brazilian context, defined as the difference between the forecast consensus and the company earnings effectively reported. The size of the forecast error is used as a proxy for the quality of the forecast produced by the analysts of a specific stock market. A vast academic literature shows that an improvement in the quality of the forecasts produced by the analysts, measured by a decrease in the size of the forecast error, is related with a decrease in the asymmetry of information in such market and by an increase in the market value of its companies. Two regressions are tested, in which company characteristics, such as sector, size, leverage and variability of earnings, and characteristics of the company’s information environment, such as ADR listing, number of analysts following and forecast convergence, are tested against two metrics of forecast error, accuracy and bias. Our hypotheses are that there are factors that impact significatively both the size of the forecast error (accuracy) and the bias presented by the projections (bias). The hypotheses are confirmed, that is, the regressions present at least one factor that impacts significantly either the size of the forecast error (hypotheses H1 and H2) or the bias (hypothesis H3). However, the results show that many factors that are significant in tests conducted in developed markets – such as size, leverage and earnings variability – are not significant in the Brazilian context. On the other hand, factors related to the company results in the fiscal year being forecast and in the previous year result to be strongly significant. We believe that these results can be explained in three ways: 1) either forecasts produced by Brazilian analysts add very little value over statistical models, probably because of lack of ability; or 2) the macroeconomic instability in Brazil is so great that its influence on the companies’ results dominates all other factors that could impact the size of the forecast error; or 3) the earnings management of the companies in the developed markets is so widespread, leading to such a stability of earnings, that it allows for more subtle factors such as size and leverage become significant. This study does not allow us to distinguish which one is the correct explanation. One of its limitations is not to include variables related to the ability and experience of the analysts, as well as variables related to governance and disclosure. In a body of research that is very extensive in developed countries, but practically inexistent in Brazil, we hope that future research fills these gaps and allow us to better understand the issue of the quality of earnings forecast in the Brazilian context.

本研究以日本東京證券交易所上市公司為研究對象，探討董事會成員年齡與強制性盈餘預測之關聯性。本研究將董事會成員年齡區分為五種：董事長的年齡、董事會成員的平均年齡、董事會成員年齡的標準差、董事會成員最高年齡跟最低年齡的差距及董事長年齡是否高於董事會成員平均年齡，以測試其所發布盈餘預測準確度與盈餘預測偏差之關聯性。而實證結果發現董事長的年齡越大、董事會成員的平均年齡越大、董事長年齡高於董事會成員平均年齡時，所發布的盈餘預測準確度也就越高，且傾向較為保守的盈餘預測。而董事會成員年齡的標準差越大、董事會成員最高年齡與最低年齡差距越大時，所發布的盈餘預測準確度較低，且傾向較為樂觀的盈餘預測。