Accurately predicting stock prices is crucial for both investors and policymakers. This paper presents the first empirical evaluation of Lag-Llama, a novel probabilistic time series forecasting model, in predicting stock prices on the Indonesian Stock Exchange (IDX). By applying Lag-Llama to univariate and multi-time series forecasts of key IDX stocks, we assess its ability to capture temporal patterns and market volatility, particularly in comparison to established models like DeepAR (RNN) and Temporal Fusion Transformer (TFT). Our results show that, in fine-tuning scenarios, Lag-Llama achieves a Continuous Ranked Probability Score (CRPS) of 0.0195 for the combined BBCA, BMRI, and AMRT stocks, surpassing TFT (CRPS: 0.0179) and DeepAR (CRPS: 0.0270). However, forecasting across broader stock groups (Top 1-9 and Top 10-18 by market cap) presents more variability, with CRPS values rising to 0.0517 for the Top 1-9 stocks. This study demonstrates Lag-Llama's potential as a robust tool for stock price prediction, particularly for select stock groupings, offering enhanced precision and reliability compared to traditional methods.