Decoding Market Cycles: A Technical and Time Series Analysis of Nifty 50

Abstract

The Indian stock market’s behavior over the past year (April 2024 – March 2025) is analyzed using technical analysis and time series modeling, with a focus on the Nifty 50 index. The Nifty 50 experienced a sharp rise to an all-time high in late September 2024 followed by a significant correction in subsequent months. Technical analysis tools – including moving averages, momentum oscillators (e.g., Relative Strength Index), and chart patterns – are applied to daily index data to identify trend reversals and momentum shifts. Time series methods, specifically ARIMA models and GARCH volatility modeling, are used to forecast index levels and assess volatility dynamics. Our findings show that technical indicators provided timely signals (e.g., overbought conditions before the peak and oversold conditions near the bottom), while a basic ARIMA model struggled to foresee the market reversal, underscoring the challenges of purely statistical forecasts in a turbulent market. We also observe pronounced volatility clustering during the downturn, consistent with GARCH model findings in the literature. Overall, the integrated analysis demonstrates that technical analysis can offer valuable short-term insights in the Indian market, and time series models can quantify risk, but combining these approaches and incorporating external information may yield more robust forecasting performance. The paper provides a cohesive, evidence-based discussion of these results, contributing to the understanding of market efficiency, the utility of technical trading signals, and the limitations of conventional time series forecasts in the context of India’s equity market.