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

Milin Patel, Keyur Suthar, Rashmin Prajapati, Keyur Upadhyay, Jaydeepsinh Solanki and Neha Soni

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 2, 959-970

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.

Keywords: Stock market prediction; moving average; technical analysis; trend indicators; algorithmic trading; financial markets (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i2:id:1547

DOI: 10.32628/IJSRST26133102

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