Providing a Prediction Model of Investment Decision Based on the Individual Characteristics of Investors using Artificial Intelligence

Investment decisions are very important for managing current needs and future goals, and individuals and families spend considerable time and resources on financial planning, and much research has documented the importance of such decisions. These studies show the importance of evaluating the impact of personality characteristics on investment decisions. In the first part of this research, the correlation between representative parameters of individual characteristics (including psychological characteristics, personality characteristics and risk-taking attitude) with investment decisions has been done. The research statistical population is all investors. Sampling is available due to the unlimited statistical population. In the second part, the data related to the representative parameters of individual characteristics were used to predict investment decisions using data mining models of Artificial Neural Network (ANN), XGBoost and AdaBoost. Comparing the results of data mining algorithms showed that the best results are related to the XGBoost model. In this case, the R2 is the highest and the MSE is the lowest compared to other cases. At the next level, the best results are obtained by the ANN with 1 hidden layer and 44 neurons. Finally, the weakest results are related to the AdaBoost model.