The objective of this study was to jointly analyze the importance of cognitive and financial factors in the accuracy of profit forecasting by analysts. Data from publicly traded Brazilian companies in 2019 were obtained. We used text analysis to assess the cognitive biases from the qualitative reports of analysts. Further, we analyzed the data using statistical regression learning methods and statistical classification learning methods, such as Multiple Linear Regression, k-dependence Bayesian, and Random Forest. The Bayesian inference and classification methods allow (...) an expansion of the research line, especially in the area of machine learning, which can benefit from the examples of factors addressed in this research. The results indicated that, among cognitive biases, optimism had a negative relationship with forecasting accuracy while anchoring bias had a positive relationship. Commonality, to a lesser extent, also had a positive relationship with the analyst’s accuracy. Among financial factors, the most important aspects in the accuracy of analysts were volatility, indebtedness, and profitability. Age of the company, fair value, American Depositary Receipts, performance, and loss were still important but on a smaller scale. The results of the RF models showed a greater explanatory power. This research sheds light on the cognitive as well as financial aspects that influence the analyst’s accuracy, jointly using text analysis and machine learning methods, capable of improving the explanatory power of predictive models, together with the use of training models followed by testing. (shrink)

Analytic processes reduce biases, but it is not known how or when these processes will be deployed. Based on an affective signal hypothesis, relatively strong affective reactions were expected to result in increased analytic processing and reduced bias in judgement. The valence and strength of affective reactions were manipulated through varying outcomes in a game or evaluative conditioning of a stimulus. Relatively strong positive or negative affective reactions resulted in less desirability bias. Bias reduction only occurred if participants had time (...) to deploy analytic processes and indicators of the degree of analytic processing (in the form of attentional control) predicted less bias. Affective processes have long been acknowledged as a source of bias, but these findings suggest affective processes are also integral to bias reduction. (shrink)

The statistics of small samples are often quite different from those of large samples, and this needs to be taken into account in assessing the rationality of human behavior. Specifically, in evaluating human responses to environmental statistics, it is the effective environment that matters; that is, the environment actually experienced by the agent needs to be considered, not simply long‐run frequencies. Significant deviations from long‐run statistics may arise through experiential limitations of the agent that stem from resource constraints and/or information‐processing (...) bounds. The article draws together recent work from a number of areas in judgment and decision making ranging from randomness perception (Hahn & Warren, ), information sampling (Hertwig & Pleskac, ; Kareev et al., ), and consequences of choice for exploration or exploitation (e.g., Denrell, ) to demonstrate how proper consideration of these deviations leads to reevaluation of behaviors that are otherwise deemed irrational. (shrink)

Do people tend to be overconfident? Many think so. They’ve run studies on whether people are calibrated: whether their average confidence in their opinions matches the proportion of those opinions that are true. Under certain conditions, people are systematically ‘over-calibrated’—for example, of the opinions they’re 80% confident in, only 60% are true. From this empirical over-calibration, it’s inferred that people are irrationally overconfident. My question: When and why is this inference warranted? Answering it requires articulating a general connection between being (...) rational and being right—something extant studies have not done. I show how to do so using the notion of deference. This provides a theoretical foundation for calibration research, but also reveals a flaw: the connection between being rational and being right is much weaker than is standardly assumed, since rational people can often be expected to be miscalibrated. Thus we can’t test whether people are overconfident by simply testing whether they are over-calibrated; instead, we must try to predict the rational deviations from calibration, and then compare those predictions to people’s performance. I show how this can be done—and that doing so complicates the interpretation of robust empirical effects. (shrink)

Humans generally exhibit a pervasive future bias in favour of optimism. We overestimate the likelihood of success in work, relationships and financial investments. Similarly, we underestimate the probability of experiencing negative events such as, serious illness or financial ruin. The optimism bias is widely considered as one of the most reproducible, prevalent and robust cognitive biases observed in psychology and behavioural economics. The catastrophic impact of the recent economic collapse has laid this cognitive bias bare. In this introductory overview, current (...) understanding of the neural basis of the optimism bias is explored. Topics considered include: converse negative biases in depressive illnesses, the role of dopamine in optimism bias generation and modulation and evidence from functional neuroimaging studies. Research on the optimism bias has afforded us a unique window into decision-making, reward-processing and the potential for systematic irrationality in the human mind. (shrink)