This study examines whether short-sellers and financial analysts develop complementary information about future earnings and returns and assesses whether investors can improve predictions made by each of these intermediaries using information provided by the other. The first main result is that the relative short interest ratio (shares sold short divided by total shares outstanding) contains information that is useful for predicting future earnings, beyond (i.e., incremental to) the information in analyst forecasts. I also find that analysts do not fully incorporate short interest information into their forecasts and demonstrate that analyst forecasts can be improved (i.e., can be made to be less biased and more accurate) by adjusting for short interest information. The second main result is that analyst forecast revisions contain information that is useful for predicting future abnormal returns, beyond the information in the relative short interest ratio. I demonstrate that portfolios of stocks formed based on consistent signals from short-sellers and analysts produce abnormal return spreads that are significantly larger than spreads produced by portfolios formed using signals from short-sellers alone. Collectively, the evidence suggests that short-sellers and analyst provide complementary information about future firm performance that is useful to investors.

Stock recommendations,frequently produced under time pressure, are susceptible to being the result of automatic and intuitive thinking. This is associated with using heuristics in decision-making which is studied by an entire school of research – the heuristics and bias approach. Heuristics of representativeness, availability and anchoring including associated biases as defined by Tversky and Kahneman provide the theoretical framework for the study. This study is aimed at extending the understanding of biases in general and cognitive biases with regard to stock recommendations. A total of thirty equity recommendations were analyzed. A t-test showed that more biases were present in the incorrect recommendations. Overconfidence, illusion of validity and anchoring were among the most frequently observed. The vast majority of recommendations were characterized by insensitivity to predictability indicating that forecasters are seemingly unaware of the difficulty of accurately predicting where the stock price is going to be within the next three to six months.

Forecasting, cost management and pricing policies are topics which have beenwidely investigated over time. Due to a lack of scientific research about therelationships between each of those subjects, these methods have beeninvestigated in combination with their outcomes. The purpose of this work was todevelop a framework which can be used by managers who want to make adecision in either of the subjects mentioned before. By the use of a qualitative, interpretive research design, a literature review was performed which led to some interesting findings. Generally, it can be said that the methods are not related directly, although the outcomes are linked and can often be used as a criterion for the decision making process for the other methods.

This study examines whether short-sellers and financial analysts develop complementary information about future earnings and returns and assesses whether investors can improve predictions made by each of these intermediaries using information provided by the other. The first main result is that the relative short interest ratio (shares sold short divided by total shares outstanding) contains information that is useful for predicting future earnings, beyond (i.e., incremental to) the information in analyst forecasts. I also find that analysts do not fully incorporate short interest information into their forecasts and demonstrate that analyst forecasts can be improved (i.e., can be made to be less biased and more accurate) by adjusting for short interest information. The second main result is that analyst forecast revisions contain information that is useful for predicting future abnormal returns, beyond the information in the relative short interest ratio. I demonstrate that portfolios of stocks formed based on consistent signals from short-sellers and analysts produce abnormal return spreads that are significantly larger than spreads produced by portfolios formed using signals from short-sellers alone. Collectively, the evidence suggests that short-sellers and analyst provide complementary information about future firm performance that is useful to investors.

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.

This study presents an analysis of several recently published methods for quantifying the uncertainty in economic evaluations due to uncertainty in future oil prices. Conventional price forecasting methods used in the industry typically underestimate the range of uncertainty in oil and gas price forecasts. These forecasts traditionally consider pessimistic, most-likely, and optimistic cases in an attempt to quantify economic uncertainty. The recently developed alternative methods have their unique strengths as well as weaknesses that may affect their applicability in particular situations. While stochastic methods can improve the assessment of price uncertainty they can also be tedious to implement. The inverted hockey stick method is found to be an easily applied alternative to the stochastic methods. However, the primary basis for validating this method has been found to be unreliable. In this study, a consistent and reliable validation of uncertainty estimates predicted by the inverted hockey stick method is presented. Verifying the reliability of this model will ensure reliable quantification of economic uncertainty. Although we cannot eliminate uncertainty from investment evaluations, we can better quantify the uncertainty by accurately predicting the volatility in future oil and gas prices. Reliably quantifying economic uncertainty will enable operators to make better decisions and allocate their capital with increased efficiency.

Using a novel setting, I examine the relation between a CEO's career concerns and the provision of an annual earnings forecast. Specifically, I exploit staggered changes in non-compete enforcement laws in three U.S. states as a source of exogenous variation in a CEO’s career concerns. Consistent with theory suggesting that career concerns increase a manager's aversion to risk, I find that a CEO is less likely to issue an earnings forecast in periods of stricter non-compete enforcement. Further, cross-sectional analyses indicate that the lower probability of forecast issuance is more pronounced for a CEO who has greater concern for his reputation, faces more risk in forecasting, and is more vulnerable to dismissal.

In the early feasibility study stage, the information concerning the target project is very limited. It is very common in practice for a Quantity Surveyor (Q.S.) to use the mean value of the historical building price data (with similar characteristics to the target project) to forecast the early construction cost for a target project. Most clients rely heavily on this early cost forecast, provided by the Q.S., and use it to make their investment decision and advance financial arrangement. The primary aim of this research is to develop a statistical model and demonstrate through this developed model how to measure the accuracy of mean value forecast. A secondary aim is to review the homogeneity of construction project cost. The third aim is to identify the best data pooling for mean value cost forecast in early construction stages by making the best use of the data available. Three types of mean value forecasts are considered: (1) the use of the target base group (relating to a source with similar characteristics to the target project), (2) the use of a non-target base group (relating to sources with less or dissimilar characteristics to the target project) and (3) the use of a combined target and non-target base group. A formulation of mean square error is derived for each to measure the forecasting accuracy. To accomplish the above research aims, this research uses cost data from 450 completed Hong Kong projects. The collected data is clustered into two levels as: (1) Level one - by project nature (i.e. Residential, Commercial centre, Car parking, Social community centre, School, Office, Hotel, Industrial, University and Hospital), (2) Level two -by project specification and construction floor area. In this research, the accuracy of mean value forecast (i.e. mean square error) for a total number of 10,539 of combined data groups is measured. From their performance, it may reasonably be concluded that (1) the use of a non-target base group (relating to sources with less or dissimilar characteristics to the target project) never improves the forecasting performance, (2) the use of a target base group (relating to a source with similar characteristics to the target project) cannot always provide the best forecasting performance, (3) the use of a combined target and non-target base group in some cases can furnish a better forecasting performance, and (4) when the cost data groups are clustered into a more detailed level, it can improve the forecasting performance.

The primary aim of this study is to investigate the stock return volatility surrounding management earnings forecasts. Disclosure by managers of expected earnings are particularly important communications, and as such, it is important to understand the capital market implications surrounding them. In doing so, the research questions are essentially aimed at examining the stock return volatility, first, at the release of a management earnings forecast, and second, at the eventual announcement of the realised earnings for that period. The first test investigates whether there is an increase in volatility surrounding a management earnings forecast for those firms who release them compared to a matched-firm sample of firms without a management earnings forecast at that date, and then further examines that result based on different forecast antecedents and forecast characteristics. Next, this study tests, for firms who do release a management earnings forecast during the year, whether stock volatility is lower than firms who do not release a management earnings forecast at the eventual earnings announcement date. In brief, the evidence using the Garman and Klass [1980] ???best analytic scale-invariant estimator??? of volatility in an Australian context, between 1993 and 2003, finds that stock return volatility is greater for bad news forecasts, forecasts of low specificity, and forecasts issued by firms perceived ex ante as being of lower credibility using both permutation analysis and modelling daily volatility. At the earnings announcement date, however, there is no evidence that stock return volatility is lower for firms that issue management earnings forecasts during the year. Overall, this result challenges the information asymmetry argument in the literature that disclosure will reduce volatility in the long-run.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 123-126). Also available via the Internet.