Purpose: Stock markets are characterized by intense volatility and fragility which are making them susceptible to almost everything that’s occurring around it. This volatility is a subject of interest to companies, stock traders and investors at large.  Besides, stock market is perceived to be the backbone of every economy and is the face of an economy’s performance in the eyes of global trade community. Especially a country like India that is fast growing and is making to constant global headlines having a less fragile or approximate predictable markets are a symbol of stability and sustenance. This focus on stability in stock markets is the central idea for using advanced data analysis techniques for stock prediction.

Methodology: The current paper adopts Single layer LSTM model on the data of NSE’s Nifty 500 covering the duration of Q1(2021)- Q4(2025). The paper aims at assessing the predictive performance of LSTM model by comparing the predicted values with the actual values of Nifty 500.

Findings: The empirical results exhibited that the multi-layered LSTM architecture yielded an exceptional prediction efficacy with minimal error variance during steady trading periods. Crucially, the model bypassed the “naïve one-day lag trap” by leveraging its 60-day look-back momentum window to delineate a stable, macro-level cyclical contraction instead of overreacting to short-term speculation.

Contributions: The study contributes to financial literature by focusing on post-training model interpretability that intend to bridge the gap between opaque AI computational algorithms and actionable investment frameworks, providing a robust trend detection framework for institutional and retail portfolio managers that mitigate the risk prevalent in emerging markets..