There is a considerable volume of research on what influences the accuracy of financial analysts' predictions. Although the findings suggest a variety of explanations related to firm size, analyst experience, and forecasting task complexity, the evidence is inconclusive. The meta-analysis method allows an integration of some results on the association between analyst errors and their principal influences. The findings show that country, measurement of the variables, and time period of forecast moderate the effect of some characteristics on analysts' accuracy.

Financial Analysts' Forecasts and Stock Recommendations reviews research related to the role of financial analysts in the allocation of resources in capital markets. The authors provide an organized look at the literature, with particular attention to important questions that remain open for further research. They focus research related to analysts' decision processes and the usefulness of their forecasts and stock recommendations. Some of the major surveys were published in the early 1990's and since then no less than 250 papers related to financial analysts have appeared in the nine major research journals that we used to launch our review of the literature. The research has evolved from descriptions of the statistical properties of analysts' forecasts to investigations of the incentives and decision processes that give rise to those properties. However, in spite of this broader focus, much of analysts' decision processes and the market's mechanism of drawing a useful consensus from the combination of individual analysts' decisions remain hidden in a black box. What do we know about the relevant valuation metrics and the mechanism by which analysts and investors translate forecasts into present equity values? What do we know about the heuristics relied upon by analysts and the market and the appropriateness of their use? Financial Analysts' Forecasts and Stock Recommendations examines these and other questions and concludes by highlighting area for future research.

This study conducts a comprehensive review of the literature published during 1996- 2017 to identify the factors that affect the accuracy of financial analysts' forecasts. We organize our review around three main groups, namely, (a) drivers of analyst forecast accuracy, (b) quality financial reporting, and (c) accounting standards. Among the several factors found, some factors (experience of the analyst, earnings quality, audit quality, IFRS adoption, and annual report readability) have a positive relationship with the accuracy of analysts' forecasts while others (politically connected firms, firms audited by Non-Big 4, and international GAAP differences) have a negative relationship. Our findings contribute to future research by examining the factors affecting analyst forecast accuracy from different perspectives, which will prove to be useful for academicians, regulators, investors, and financial analysts.

​Earnings forecasts are ubiquitous in today’s financial markets. They are essential indicators of future firm performance and a starting point for firm valuation. Extremely inaccurate and overoptimistic forecasts during the most recent financial crisis have raised serious doubts regarding the reliability of such forecasts. This thesis therefore investigates new determinants of forecast errors and accuracy. In addition, new determinants of forecast revisions are examined. More specifically, the thesis answers the following questions: 1) How do analyst incentives lead to forecast errors? 2) How do changes in analyst incentives lead to forecast revisions?, and 3) What factors drive differences in forecast accuracy?

Prior research on financial analyst' quarterly earnings forecasts has documented serial correlation in forecast errors. This paper examines the way serial correlation in quarterly earnings forecast errors varies with firm and analyst attributes such as the firm's industry and the analyst's experience and brokerage house affiliation. Finding that serial correlation in forecast errors is significant and seemingly independent of firm and analyst attributes, I model consensus forecast errors as an autoregressive process. I demonstrate that the model of forecast errors that best fits the data is AR(1), and use the obtained autoregressive coefficients to predict consensus forecast errors. Modeling the consensus forecast errors as an autoregressive process, the present study predicts future consensus forecast errors, and proposes a series of refinements to the consensus. These refinements were not presented in prior literature, and can be useful to financial analysts and investors.

Financial analysts provide information in their research reports and thereby help forming expectations of a firm’s future business performance. Thus, it is essential to recognize analysts who provide the most precise forecasts and the accounting literature identifies characteristics that help finding the most accurate analysts. Tanja Klettke detects new relationships and identifies two new determinants of earnings forecast accuracy. These new determinants are an analyst’s “general forecast effort” and the “number of supplementary forecasts”. Within two comprehensive empirical investigations she proves these measures’ power to explain accuracy differences. Tanja Klettke’s research helps investors and researchers to identify more accurate earnings forecasts.