Financial Analysts’ Forecasts: Behavioral Aspects and the Impact of Personal Characteristics

Simone Stæhr

Research output: Book/ReportPhD thesis

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This thesis is broadly concentrated on decision making under uncertainty. It seeks to investigate how agents in financial markets make decisions at the individual level and how these decisions can sometimes be affected by personal traits and cognitive biases rather than being perfectly rational. The primary focus is on financial analysts in the task of conducting earnings forecasts while a secondary focus is on investors’ abilities to interpret and make use of these forecasts. Simply put, financial analysts can be seen as information intermediators receiving inputs to their analyses from firm management and providing outputs to the investors. Amongst various outputs from the analysts are forecasts of earnings. According to decision theories mostly from the literature in psychology all humans are affected by cognitive constraints to some degree. These constraints may lead to unintentional biases in the decision making and the magnitude of these constraints does sometimes vary with personal traits. Therefore, to the extent that financial analysts are subjects to behavioral biases their outputs to the investors are likely to be biased by their interpretation of information. Because investors need accuracy in the financial forecasts on which they base investment decisions they may end up losing money as a consequence of biased forecasts. Thus, relying primarily on decision theories such as social comparison theory and theories on confirmation bias this thesis investigates how and why pronounced biases in financial analysts’ forecasts documented at the market level by prior literature occur at the individual level and which personal traits interact in this process.
Original languageEnglish
Place of PublicationFrederiksberg
PublisherCopenhagen Business School [Phd]
Number of pages254
ISBN (Print)9788793579187
ISBN (Electronic)9788793579194
Publication statusPublished - 2017
SeriesPhD series

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