Sentiment-Enhanced Stock Price Prediction in Nepalese Small-Cap Equities using Natural Language Processing

Abstract

In this study, we examine how the integration of market sentiment with traditional financial indicators can improve the accuracy of stock price forecasts in Nepal’s small-cap equity market. With the NEPSE Index as our point of focus, we examined market trends from July 2024 to January 2025, with Nepal Finance Limited as a prominent case. To measure investors' sentiment, we applied natural language processing to diverse local data sources, including the financial news from Sharesansar, Floorsheet insights from Merolagani, and street-level views from the r/nepalstock community at Reddit. Those sentiment drivers were then combined with conventional technical drivers in a two-model forecasting model with a combination of LSTM neural networks and XGBoost. The findings are substantial: sentiment models outperformed technical analysis-only models in all tests, lowering Mean Squared Error by 17.3% and doing much better on directional forecasting. Amongst all inputs, previous-day stock prices and movement of the NEPSE index were the most significant predictors, whilst sentiment inputs provided a rich source of leading data. By illustrating the efficacy of alternative data in a frontier market environment, this study presents both scholarly timeliness and pragmatic insight for Nepali investors pursuing an advantage within the country's emerging capital markets.