Development of an Investment Recommender System Using Factor Analysis, ANFIS, and MMNN

The main goal is to present two investment recommender systems (IRS), by combining clustering, factor analysis, Adaptive Neuro Fuzzy Inference System (ANFIS), and Multimodal Neural Network (MNN). The aim is to merge each method with advanced techniques to improve the precision and efficiency of investment recommendations. To develop and implement the IRS, clustering and factor analysis are initially used to detect patterns and connections among variables aiding in grouping individuals into several categories. Then ANFIS is developed in MATLAB using data derived from factor analysis to prove rules for recommending clusters of investment types. Furthermore, MNN was created using Python making use of TensorFlow and Keras libraries using same data for ANFIS. This network is pre-trained with data to predict investment types. The performance of both models is assessed by metrics RMSE and MSE on test data to gauge their accuracy of recommendations. An assessment of the IRSs illustrates its effectiveness in offering investment recommendations. Both models highlight promising performance as shown by the error rates on the test data. By combining clustering, factor analysis, ANFIS and MNN a holistic strategy appears for tailoring investment advice. This approach effectively merged methods with innovative machine learning (ML) and deep learning (DL) techniques. This paper proposes the personalized IRSs that are useful for investment advice. By integrating clustering, factor analysis, ANFIS, and MNN, IRS provides a unique approach with using Explainable artificial intelligence (XAI) to increase the accuracy of investment recommendations. These systems use the strengths of each method in combining them.