An intelligent hybrid model for forecasting the stock price index volatility: the case of Tehran stock exchange

Forecasting the stock price index volatility is considered a strategic and challenging issue in the stock markets, and it is momen-tous for traders and investors in the decision-making process. hence, the presentation of an efficient model for forecasting the stock price index volatility is a crucial and hard task because stock market data and price fluctuations have high volatility and nonlinearity characteristics. to beat this challenge, this paper proposes a new hybrid model by applying artificial intelligence algorithms to forecast the stock price index. it incorporates four phases to provide a dynamic and exact model: (1) select popular and key technical indicators as input variables (2) apply adaptive neuro-fuzzy inference system (anfis) for designing a sub-structure to provide a high-quality and quick solution (3) use modified particle swarm optimization (mpso) to enhance pre-dictive accuracy by simultaneously and adjusting the length of each interval in the discourse universe and the appropriate degree of membership (4) employ parallel genetic algorithm (pga) to solve complex issues with computational weight optimization and adjusting the decision vectors employing genetic operators. the stock market data of “tehran stock exchange (tse)” from 01/01/2011 to 31/12/2021 are utilized to examine the functionality of the proposed model. in comparative assessments, the overall performance of the anfis-mpso-pga model based on 5 criteria achieved 81.45%, which was significantly better than other methods.