Abstract
This research aims to examine empirically the overreliance on representativeness heuristic and anchoring-adjustment influences experienced by investors in forecasting future earnings. This research was a laboratory experiment with a design of 2x2 full factorial between subject. The results showed that representativeness heuristics were only experienced by investors who obtained positive information. Besides, this study also shows that investors do not overreliance on anchoring-adjustment heuristics. Generally, this research shows that cognitive biases occur when the information presented is of good value so that it can be taken into consideration for investors to be more careful in making predictions. Multiple benchmark information can be used as a consideration in evaluating the company’s earnings and stock performance.
Highlights
The main goal in the discussion of earnings forecasting is not to find the best model but to identify useful models
The experimental revealed that investors relied heavily on previous earnings and made the level and pattern of the previous earnings their initial belief
This study presents validity data to support a vignette-based instrument quantifying bias due to the anchoring, availability, and representativeness heuristics
Summary
The main goal in the discussion of earnings forecasting is not to find the best model but to identify useful models. Heuristic representativeness is a psychological bias that explains that in conditions of uncertainty, an investor tends to believe in history in terms of the common results of a company’s performance in general (Boussaidi, 2013). The earnings expected by investors with heuristic representativeness are greater than rational investors This can answer the question of why most investors use the heuristic representativeness in making estimates in the capital market. Initial values can be offered in earnings for the past period (Wahyuni et al, 2016) Both of heuristic factors, namely representativeness and anchoring-adjustment, cause cognitive biases that are considered as inaccuracies in predicting future earnings performance (Lee et al, 2016). The use of information with multiple benchmark models illustrates that comprehensive disclosure of information can assist investors in predicting accounting information
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