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Stock price prediction is an important topic in finance and economics, which over the years has attracted the attention of researchers to develop superior prediction models. The stock market is also one of the indicators of the industry’s prosperity and growth in terms of production, revenue, and expansion. The various sectors, like IT, Pharma, Petroleum, FMCG, Defense, PSU, Financials, etc., are the key drivers for the Sensex. FII, DII, HNI, retail investors, and promoters are important key players in the stock market. It is very important for the traders to recognize the dynamics/direction of the price to increase their wealth. Most private investors lose their wealth due to a lack of basic knowledge about the stock market. There are a number of methods/techniques for predicting stock momentum in the short term. It is examined that the time series approach is more realistic and provides a significantly more precise forecast. This article makes a stock price prediction based on historical data with different time frames. The results obtained show that the ARMA model provides greater accuracy of the future values ​​of the price on a short-term basis.

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Stock Momentum Prediction Using Time Series Autoregressive Moving Average Models

  • 会议论文

  • 发表日期：

    2026年

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  • Aishwarya K. Nesari,
  • Pralhad Rathod

摘要

Stock price prediction is an important topic in finance and economics, which over the years has attracted the attention of researchers to develop superior prediction models. The stock market is also one of the indicators of the industry’s prosperity and growth in terms of production, revenue, and expansion. The various sectors, like IT, Pharma, Petroleum, FMCG, Defense, PSU, Financials, etc., are the key drivers for the Sensex. FII, DII, HNI, retail investors, and promoters are important key players in the stock market. It is very important for the traders to recognize the dynamics/direction of the price to increase their wealth. Most private investors lose their wealth due to a lack of basic knowledge about the stock market. There are a number of methods/techniques for predicting stock momentum in the short term. It is examined that the time series approach is more realistic and provides a significantly more precise forecast. This article makes a stock price prediction based on historical data with different time frames. The results obtained show that the ARMA model provides greater accuracy of the future values ​​of the price on a short-term basis.