Prediction of mutual fund closing price (net asset value) and performance using portfolio distribution

Mutual funds are one of the important financial investment types that are professionally managed, diversify the investment by investing in various investment instruments by bringing together the money of the investors and thus spreading the risk to the base. In recent years, the number of mutual funds has been increasing more and more. This fact indicates that selecting specific mutual funds requires significant investor effort in collecting and analyzing information about funds operating in the same investment universe. Accurate prediction of the closing prices of mutual funds has become an increasingly important issue for investors. Mutual fund investors will be significantly supported by the development of models that accurately predict the future performances of mutual funds. Using these models will facilitate the selection of suitable mutual funds for investors who want to invest in the medium and long term. The aim of this thesis, using Artificial Neural Networks and Nonlinear Autoregressive Network With Exogenous Inputs (NARX), is to predict the closing price of a mutual fund with the help of fund portfolio distribution and values of performance evaluation methods. The analysis made in the study will provide a helpful perspective to investors and help investors make better decisions about mutual funds. This way, the investors' returns will increase, and the effort they spend on fund selection will decrease.