چکیده مقاله
The paper presents a novel and advanced approach in the field of stockmarket analysis, leveraging the synergy of adaptive neural fuzzynetworks and swarm optimization techniques The primary objective isto enhance the accuracy of stock market predictions and decision makingby considering past financial data and expertise The core component ofthis method is the adaptive neural fuzzy network, which acts as theintermediary bridging the gap between input variables and the desiredstock market index output The input parameters encompass a range ofcrucial economic factors such as oil income, tax income, infrastructurepayments, and deficit spending These variables are interconnected andindirectly dependent on each other, forming a complex web ofrelationships that significantly impact the stock market's performance The adaptive neural fuzzy network plays a vital role in extracting anddeciphering these intricate relationships among the input parameters Itdoes so with a high degree of precision, which is essential for makingaccurate predictions in the volatile and dynamic world of stock markets By better understanding these interdependencies, the proposed intelligentalgorithm is able to provide more informed estimations and subsequentlyenables investors and financial decision makers to make more effectiveand profitable choices based on historical financial data In essence, thisresearch contributes to the development of intelligent tools that empowerstakeholders to navigate the complexities of the stock market with greaterconfidence and success
کلیدواژهها
نویسندگان
شیوه ارجاع
Shafieenejad, Iman,1403,Combination of Fuzzy, Neural-Fuzzy and Artificial Bee ColonyOptimization Algorithms for Predicting and Investigating theMarketing Index,the 8th International Conference on Management, Accounting, Banking and Economics in Iran,Mashhad
ارائهشده در
مجموعه مقالات هشتمین کنفرانس بین المللی مدیریت، حسابداری، بانکداری و اقتصاد ایران8 تیر 1403 · مشهد