A Novel Combined Investment Recommender System Using Adaptive Neuro-Fuzzy Inference System

Investment recommendation systems (IRSs) are critical tools used by potential investors to make informed decisions about investment options. However, existing systems have limitations in terms of accuracy and efficiency, leading to a need for more effective and efficient recommendation systems. This dissertation proposes the use of an adaptive neuro-fuzzy inference system (ANFIS) to develop a combined IRS that can provide accurate and efficient investment recommendations for potential investors. The main research question for this study is "How can an ANFIS be utilized to propose an effective and efficient investment recommendation system?" The specific sub-goals of the study are: 1) to categorize and cluster potential investors based on available data to make accurate investment recommendations, 2) to offer customized investment-type services using adaptive neural-fuzzy inference solutions for different categories of potential investors, and 3) to propose a combined recommender system to provide appropriate investment type recommendations for all categorized and clustered potential investors. The dissertation is structured into five chapters. Chapter I provides an overview of the research question and objectives, and Chapter II presents a theoretical framework and literature review, covering existing research on ANFIS in investment recommendation systems. Chapter III explains the methodology used to develop the combined IRS using ANFIS, including data collection, categorization and clustering of potential investors, development of the combined ANFIS model, and evaluation of the proposed system. Chapter IV presents the experimental results and analysis, highlighting the effectiveness of the model in providing appropriate investment-type recommendations for categorized and clustered potential investors. This chapter describes seven experiments that focused on investment recommender systems. Each experiment proposed a unique system that utilized various features of potential investors and their investment type ...