The Influence of Cognitive Biases and Financial Factors on Forecast Accuracy of Analysts - Nardi, Paula Carolina Ciampaglia; Ribeiro, Evandro Marcos Saidel; Bueno, José Lino Oliveira; Aggarwal, Ishani (Frontiers in Psychology, 2022)
The objective of the 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 cognitive biases from 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-DB and Random Forest. The Bayesian inference and classification methods allow expansion of the research line, especially in the area of machine learning, which can benefit from 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. Still important, but on a smaller scale, were age of the company, fair value, ADRs, performance and loss The results of the Random Forest models showed 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.