سمیرا خونشا

Financial services companies such as banks, brokerage firms, family offices, insurance companies, and trusts, provide advisory services to help clients achieve their investment goals. These services typically include offering investment solutions and discretionary portfolio management, where asset management is entrusted to financial experts. One of the main challenges in this field is recommending investment strategies that align with clients' needs and risk tolerance. In this study, a model was designed to assess investors' risk tolerance using advanced artificial intelligence (AI) and machine learning techniques. The model analyzed investors' demographic and financial data using regression algorithms to calculate their risk profiles. Then, an intelligent robo-advisor was designed to recommend the most suitable investment mix in S&P 500 companies' stocks based on individual investor profiles. The data for this study was extracted from the Federal Reserve's Survey of Consumer Finances (SCF), conducted between 2007 and 2009. The results of this research indicated that the use of AI and machine learning models can significantly improve the accuracy of assessing investors' risk tolerance. The proposed model, utilizing demographic and financial data from the SCF, successfully generated diverse risk profiles for investors. The designed robo-advisor intelligently analyzed these profiles and provided appropriate investment strategies for the S&P 500 index.