Long-lasting heuristics principles for efficient investment decisions

Abstract

Purpose Behavioral solutions to our cognitive biases have long been studied in the literature. However, there is still ample evidence of behavioral biases in decision-making, with only limited improvement in the medium/long term even when debiasing methods are applied. The purpose of this paper is to describe how financial investors could benefit from a proactive management of emotions combined with a set of learning and decision-making heuristics to make more efficient investments in the long run. Design/methodology/approach First, the authors offer a classification of the appropriate quantitative and qualitative methodologies to use in different ecological environments. Then, the authors offer a list of detailed heuristics to be implemented as behavioral principles intended to induce more long-lasting changes than the original rules offered by the adaptive toolbox literature. Finally, the authors provide guidelines on how to embed artificial intelligence and cognitive diversity within the investment decision architecture. Findings Improvements in decision skills involve changes that rarely succeed through a single event but through a succession of steps that must be habitualized. This paper argues that implementing a more conscious set of personal and group principles is necessary for long-lasting changes and provides guidelines on how to minimize systematic errors with adaptive heuristics. To maximize their positive effects, the principles outlined in this paper should be embedded in an architecture that fosters cognitively diverse teams. Moreover, when using artificial intelligence, the authors advise to maximize the interpretability/accuracy ratio in building decision support systems. Originality/value The paper proposes a theoretical reflection on the field of behavioral research and decision-making in finance, where the chief goal is to offer practical advices to investors. The literature on debiasing cognitive biases is limited to the detection and correction of immediate effects. The authors go beyond the traditional three building blocks developed by the behavioral finance literature (search rules, stopping rules and decision rules) and aim at helping investors who are interested in finding long-term solutions to their cognitive biases.