Unveiling the impact of managerial traits on investor decision prediction: ANFIS approach

Abstract Investment decisions are influenced by various factors, including personal characteristics and managerial issues. In this research, we aimed to investigate the impact of managerial traits on investment decisions by using adaptive neuro-fuzzy inference system (ANFIS) to develop a personalized investment recommendation system. We collected data from potential investors through a survey, which included questions on investment-types, investment habits, and managerial traits. The survey data were used to create an ANFIS model, which is a hybrid model that combines the strengths of both artificial neural networks and fuzzy logic systems. The ANFIS model was trained using 1542 survey data pairs, and the model's performance was evaluated using a validation set. The results of the ANFIS model showed that the model had a minimal training root mean square error of 0.837341. The ANFIS model was able to effectively capture the relationship between managerial traits and investment decisions and was able to make personalized investment recommendations based on the input data. The results of this research provide valuable insights into the impact of managerial traits on investment decisions and demonstrate the potential of ANFIS in developing personalized investment recommendation systems. In conclusion, this research aimed to investigate the impact of managerial traits on investment decisions using ANFIS. The results of this study demonstrate the potential of ANFIS to personalize investment recommendations based on the input data. This research can be used as a foundation for future research in the field of investment recommendations and can be helpful to investors to take their decision-making.