The aim of this Master’s Thesis is noise map creation and processing of measured data using ArcView 9.2 and its extensions. One - year Student Edition of ArcView 9.2 and its extensions have been provided by ARCDATA PRAHA, s.r.o. Noise maps are created from measured data in the neighborhood of the faculty building and the Moravian Square in Brno. Measurement of geographic position of measuring points is realized by GPS. For creation of noise maps, analysis and processing of measured data ArcGIS extensions - Spatial Analyst and Geostatistical Analyst were used. The last part of this Master’s Thesis is specialized on creation of 3D landscape model using ArcGIS 3D Analyst extension.

The aim of this Master’s Thesis was mapping of electromagnetic fields and processing of measured data using ArcView 9.2 and its extensions. Software ArcView 9.2 with extensions was provided by university to create this Master’s Thesis. Maps of electromagnetic fields are created from measured data in the neighborhood of the faculty building. Measurement of geographic position of measuring points is realized by GPS. For creation of maps and further analysis in ArcView, ArcGIS extensions - Spatial Analyst, Geostatistical Analyst and 3D Analyst were used.

This study examines the long-run accuracy of analyst recommendations issued at the expiration of the initial public offering (IPO) quiet period and examines the relation between the Global Settlement, NYSE Rule 472, NASD Rule 2711, and analyst recommendations. It is expected that firms which receive positive recommendations will outperform the market and firms with neutral recommendations. In addition, it is expected that banks named in the Global Settlement will become more selective when issuing recommendations. This study examines firms engaging in IPOs from July 9, 2002 through December 31, 2005 and finds that analyst ratings have become more balanced following the Global Settlement, NYSE Rule 472, and NASD Rule 2711. When controlling for firm size, underpricing, rating heterogeneity, and analyst affiliation, firms which receive positive analyst ratings experience greater buy-and-hold abnormal returns than firms which do not. Furthermore, firms which receive multiple “buy” ratings outperform firms which receive only one “buy” rating when controlling for underpricing firm size, and the number of neutral ratings. Banks named in the Global Settlement appear to be more selective when issuing positive recommendations. Firms which receive a positive rating from a bank named in the Global Settlement outperform firms which receive a positive rating from a bank not named in the Global Settlement. Lastly, prior to the Global Settlement, it appears that sanctioned banks issued ratings one level higher than they should have. Firms which received positive ratings experienced neutral performance and firms which received natural ratings experienced negative performance. Since the Global Settlement, sanctioned banks appear to issue accurate ratings since positive ratings are associated with increased buy-and-hold abnormal returns and neutral ratings do not significantly impact firm performance.

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I explore whether participation on Estimize.com, a crowdsourced earnings-forecasting platform aimed primarily at novices, improves professional analysts’ forecast accuracy and career outcomes. Estimize provides its contributors with frequent and timely feedback on their forecast performance and offers them a new channel for disseminating their forecasts to a wider public, features that could help analysts improve their forecast accuracy and raise their online visibility. Using proprietary data obtained from Estimize and a difference-in-differences research design, I find that IBES analysts who are active on Estimize improve their EPS forecast accuracy by 13% relative to the sample-mean forecast error, as well as reduce forecast bias. These improvements in performance vary predictably in ways consistent with learning through feedback. Additionally, I find increased market reaction to the positive earnings-forecasts revisions issued by analysts who are active on Estimize. I also find that analysts active on Estimize enjoy incremental positive career outcomes after controlling for forecast accuracy. My results suggest that professional analysts can learn to become better forecasters through online feedback and consequently garner more attention from the market. My results also suggest analysts can improve their career outcomes by gaining additional online visibility. / Business Administration/Accounting

This paper investigates both the information contents of recommendations disseminated by foreign security firms and the interaction of foreign security firms’ trading activities with their recommendations in Taiwan’s stock market. Using event study, correlation test, and regression analysis, we find negative average abcdrmal returns(AARs) and average cumulative abcdrmal returns(CARs) for negative and neutral foreign analysts’ recommendations levels and recommendation changes in the pre-recommendation period. AARs and CARs for positive recommendations in pre-recommendation period are positive, but reverse to negative three days after the event day. Our results also show that correlation coefficients of recommendations (both in recommendation levels and recommendation changes) and holding period returns are significantly positive in the pre-recommendation period, but insignificantly negative in the post-recommendation period. In the regression analyses, we find that price momentum factor is significantly related to foreign analysts’ recommendation, but the incremental contribution of this factor to foreign analysts’ recommendations are marginal and not significant. We also find that foreign security firms respond more rigorously to stocks receiving recommendation above buy recommendations and stocks being downgraded. These results show that foreign security firms are more conservative toward trading stocks in Taiwan’s stock market. They only buy stocks above buy recommendations (in a delay pattern), but immediately sell downgraded stocks.

Security analysts play a central role in the functioning of financial markets through their privileged position as intermediaries between firms and investors. Analyst activity is important to reduce information uncertainty but it is not unbiased. On the one hand, the literature shows that these sophisticated agents promote market efficiency by facilitating the incorporation of new information into stock prices. On the other hand, there is evidence that analysts underreact to negative information and that they tend to be optimistic about firms they follow. Recent studies show that the market does not assimilate immediately the disclosure of a first-time going-concern modified (GCM) audit report. This accounting event is part of a wide range of bad news events which investors are particularly inefficient at dealing with. My thesis explores how analysts deal with the GCM audit report and whether they facilitate the correct assimilation of such information into stock prices. In particular, I use a sample of 924 firms for which their auditors disclose a GCM audit report for the first-time between 01.01.1994 and 31.12.2005. I find that security analysts anticipate the publication of a first-time GCM audit report. My results show that within the one-year period before the GCM disclosure, security analysts downgrade the average recommendation for GCM firms from “buy” to “hold” whereas similar non-GCM firms maintain an average “buy” rating. A number of robustness tests confirm that this finding is not sensitive to the criteria used to select the non-GCM control firm. Moreover, analysts are more likely to cease coverage of GCM firms prior to the GCM event than for matched control firms. In addition, I show that analysts react to the publication of a GCM audit report by ceasing coverage of GCM firms. My results suggest that investors do not recognize an average “hold” recommendation for a stock of a firm immediately before the announcement of a GCM audit report as an unfavourable message even considering that it represents a downgrade from a previous “buy” rating. In particular, I find that the negative short-term market reaction to the publication of a GCM audit report is significantly higher for firms with pre-event analyst coverage compared to firms with no pre-event analyst coverage. This suggests that analyst activity may be misleading the market in terms of the saliency of pre-GCM unfavourable news by issuing “disconfirming opinions” to the market and thus increasing the “surprise” associated with the publication of a GCM audit report. In addition, I show that analyst post-GCM coverage does not increase the efficiency with which the market assimilates the GCM audit report into stock prices. In particular, I fail to find significant differences between the post-GCM return performance of covered firms compared to firms with no analyst coverage. However, I show that the percentage of covered firms following the GCM disclosure is significantly higher for those with best post-GCM return performance than for those with worst post-GCM return performance. This suggests that post-GCM return performance explains the decision of analysts to cover GCM firms but analyst coverage does not influence significantly the post-GCM return performance of such firms. Overall, my thesis contributes to the accounting and finance literature by showing that analyst activity is not providing investors with adequate value-relevant information for their investment decisions in the GCM bad news domain. Firstly, the reluctance of analysts to issue a clear unfavourable message about the stocks of GCM firms seems to explain why the “surprise” associated with the publication of a GCM audit report is greater for covered firms than for non-covered firms. Secondly, the tendency of analysts to cease coverage of GCM firms and the low level of analyst coverage following the GCM announcement may explain why analyst coverage does not reduce the magnitude of the post-GCM negative drift. As such, analyst contribution to the price-discovery process in this case is likely confined to firms with high levels of analyst coverage.

This study investigates the extent to which sell-side analysts make full use of available financial and nonfinancial information signals in formulating stock recommendations. Prior research shows that investors rely strongly on sell-side analysts’ recommendations and that sell-side analysts pay considerable attention to nonfinancial measures in making their decisions. However, prior research has primarily focused on the mere presence of nonfinancial measures and not the extent to which the direction of such measures (i.e. favorability) is associated with firm value, or assessed the extent to which any interaction between financial measures and the direction of nonfinancial measures may influence analysts in formulating stock recommendations. Using a data set hand-collected from annual proxy statements, I use ordered logistic regression analysis to provide a multivariate test of the relation between sell-side analyst recommendations, financial and context-specific nonfinancial measures. I find that analysts do incorporate the direction (favorability) of nonfinancial measures in formulating stock recommendations and that unfavorable nonfinancial measures attenuate positive financial information.

Despite significant regulatory and academic interest in sell-side analyst forecasts and an extensive literature demonstrating the impact of teamwork in general, we lack evidence of the effect of teamwork on analyst forecasts. In 2005 analyst teams issued nearly three-fourths of analyst reports for a sample of 89 large, heavily followed companies. Over a twelve-year period 86 of those companies had more reports issued by analyst teams than by individual analysts. Using a hand-collected sample of more than 17,000 analyst reports, I document that forecasts issued by analyst teams systematically differ from the forecasts of individual analysts in ways predicted by team literature. I find that prior to the year 2000 analyst teams issue forecasts that are less accurate and more biased than forecasts issued by individual analysts. Beginning in 2000, the relative benefit of analyst teamwork strengthens, consistent with changes due to Regulation Fair Disclosure, brokerage closures, and other regulatory interventions. In addition I find that, within company-year, team-issued forecasts are less pessimistically biased but not less optimistically biased than the forecasts issued by individual analysts. Lastly, the benefits of teamwork vary with the size of the team and over the life of the team, following an inverted u-shaped pattern. My results inform regulators as they consider factors that impact analyst forecast accuracy and bias.

Financial analysts are considered inefficient when they do not fully incorporate relevant information into their forecasts. In this dissertation, I investigate differences in the observable efficiency of analysts' earnings forecasts between firms that consistently meet or exceed analysts' earnings expectations and those that do not. I then analyze the extent to which the market incorporates this (in)efficiency into its earnings expectations. Consistent with my hypotheses, I find that analysts are relatively less efficient with respect to prior returns for firms that do not consistently meet expectations than for firms that do follow such a strategy, especially when prior returns convey bad news. However, forecast errors for firms that consistently meet expectations do not appear to be serially correlated to a greater extent than those for firms that do not consistently meet expectations. It is not clear whether the market considers such inefficiency when setting its own expectations. While the evidence suggests they may do so in the context of a shorter historical pattern of realized forecast errors, other evidence suggests they may not distinguish between predictable and surprise components of forecast error when the historical forecast error pattern is more established.