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Insights on Moving Average Strategies and Their Forecasting Performance: a case study on National Stock Exchange Data

Author

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  • Milin Patel
  • Keyur Suthar
  • Rashmin Prajapati
  • Keyur Upadhyay
  • Jaydeepsinh Solanki
  • Neha Soni

Abstract

Predicting the stock market is a highly important yet complex challenge. Among a large variety and types of indicators used for the market trend analysis, Moving Average (MA) based indicators are one of the most widely used tools in the stock market prediction. This paper presents a comprehensive review of various moving average based trend indicators supported by the empirical evaluation done using linear regression as a one of the basic machine learning model over the National Stock Exchange (NSE) data. The main objective of the study is to evaluate the Moving Average and its variants as feature transformations across multiple lag and smoothing windows. The study validates that the moving average based filtering improves forecasting performance more significantly for short-to-medium lag structures than the long lag. Insights are presented on the behaviour and effectiveness of moving average based indicators across different scenarios, with specific attention to error reduction, overall predictive accuracy as well as directional accuracy.

Suggested Citation

  • Milin Patel & Keyur Suthar & Rashmin Prajapati & Keyur Upadhyay & Jaydeepsinh Solanki & Neha Soni, 2026. "Insights on Moving Average Strategies and Their Forecasting Performance: a case study on National Stock Exchange Data," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 13(2), pages 959-970, April.
    • Handle: RePEc:etm:ijsrst:v13:y2026:i2:id:1547
    DOI: 10.32628/IJSRST26133102
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