An Investigation of the Impact Publicly Available Accounting Data, Other Publicly Available Information and Management Guidance on Analysts’ Forecasts

Abstract

There have been a number of studies that indicate that analysts recommendations are superior to other forecasts, such as those by time-series models, and add economic benefit, adjusted for transaction costs, to clients who first receive and then use analysts’ forecasts. There is also academic literature documenting the use of accounting information in valuing firms by analysts and others, the use of financial information from other sources than the firm itself by analysts, the impact of management guidance on decisions made by analysts, and the concept of herd behavior among analysts. The majority of studies about analysts have used sell-side analyst data to reach their findings. However, there has been little research involving buy-side analysts, analysts who are employed by institutional investors to provide stock purchase recommendations to their employers for internal investment decision making purposes. The research there has studied investments by institutional investors, many of which employ buy-side analysts. The purpose of this study is to add to the literature by investigating what information buy-side analysts use in arriving at their stock investment recommendations. This study also investigates whether or not buy-side analysts are predominantly influenced by the information they receive from publicly available accounting data, other available public information, other analysts or management guidance. The data for this investigation is being obtained from a survey of buy-side analysts. A list of 130 analysts was prepared and asked to take the survey. The use of a survey to gather the data was consistent with the use in prior studies. The PLS approach to structural equation analysis was used to assess the measurement model because it can be used for theory confirmation and suggest possible relationships, and because it is more suitable for prediction since it assumes that all measured variance can be explained in a study. The SEM-based method has been described as a coupling of two traditions: an econometric perspective focusing on prediction and a psychometric emphasis that models concepts as latent (unobserved)variables that are indirectly inferred from multiple observed measures (alternately termed as indicators or manifest variables). This method allows for the performance of path analytic modeling and has been referred to as a second generation of multivariate analysis. The results of this study further the academic literature concerning analysts by investigating what information buy-side analysts use to arrive at their overall stock investment recommendations and by the use of the PLS approach.