University of Guilan Journal of Mathematical Modeling 2345-394X 13 4 2025 12 01 A novel leading moving average indicator using ANFIS-Wavelet hybrid method for financial market forecasting 817 830 8838 10.22124/jmm.2025.30492.2737 EN Mohammad Kazemi Department of Statistics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran Hamed Mohammadi Peyhani Department of Information Science, University of Ottago, Dunedin, New Zealand Mohsen Akbari Department of Business Administration, Faculty of Management and Economics, University of Guilan, Rasht, Iran Journal Article 2025 04 25 Technical analysis aims to identify market trends and forecast future direction to support profitable trading decisions. This paper introduces a novel leading moving average indicator based on a hybrid ANFIS-Wavelet approach. The proposed method consists of two main components. First, a hybrid model combining an Adaptive Network-based Fuzzy Inference System (ANFIS) and Wavelet Transform (WT) is employed to forecast future market prices, with the full wavelet decomposition of the price time series serving as input parameters for ANFIS. Second, a leading moving average is constructed using both historical and forecasted prices. Similar to the other leading indicators, the proposed indicator can serve as a market predictor due to its incorporation of forecasted values. Empirical evaluation on NASDAQ-listed stocks demonstrates that this indicator is effective as a trading decision-support tool in financial markets, such as stock exchanges. https://jmm.guilan.ac.ir/article_8838_c175b155d1c8e754e53dcb850d7b9933.pdf