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The stock market functions as a financial marketplace where shares of publicly sorted companies are transacted through buying and selling activities. Serving as an economic indicator, it reflects the performance of businesses and the entire economy. Stock prices are affected by the laws of supplies and demands. This paper presents a deep learning (DL)-based long short-term memory (LSTM) model in time series dataset for effective stock price prediction. A metaheuristic arithmetic optimization algorithm is applied in order to fine tune the hyperparameters of LSTM and to improve the accuracy of stock price prediction. The DJIA dataset is considered in the experiments. Results have shown that the proposed model attains higher R2 and MSE values around 0.891 and 0.02, respectively.

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Stock Price Prediction Using Arithmetic Optimizer-Assisted LSTM Model

  • 会议论文

  • 发表日期：

    2025年

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  • P. V. Bhuvaneshwari,
  • Radhakrishnan Vignesh,
  • H. B. Asif Mohamed

摘要

The stock market functions as a financial marketplace where shares of publicly sorted companies are transacted through buying and selling activities. Serving as an economic indicator, it reflects the performance of businesses and the entire economy. Stock prices are affected by the laws of supplies and demands. This paper presents a deep learning (DL)-based long short-term memory (LSTM) model in time series dataset for effective stock price prediction. A metaheuristic arithmetic optimization algorithm is applied in order to fine tune the hyperparameters of LSTM and to improve the accuracy of stock price prediction. The DJIA dataset is considered in the experiments. Results have shown that the proposed model attains higher R2 and MSE values around 0.891 and 0.02, respectively.