SMART NET ASSET VALUE (NAV) PREDICTION USING MACHINE LEARNING

Net Asset Value (NAV) serves as the price at which investors buy or sell units of mutual funds. It is computed at the end of each business day using closing prices of securities held by the fund. NAV is a benchmark for tracking a fund’s performance and is updated daily for open-end funds. This article presents NAV prediction using XG Boost machine learning Algorithm. The proposed model suggests time series prediction model. Lower MAE / RMSE shows predictions are numerically close. Very low MAPE (~0.55%) indicates strong relative accuracy. It is quite effective, with forecasted values only marginally different from actual NAV. For daily NAV forecasting, such low errors are often considered very acceptable. Very low MAPE (~0.55%) indicates strong relative accuracy.