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Deepstock : AI Powered Stock Analysis
Subject area: Science,Engineering and Technology · Area of research: Machine Learning in Finance / Stock Prediction
DOI: https://doi.org/10.64388/IREV9I11-1717317
Abstract
The pervasive challenge in modern quantitative finance lies in the failure of traditional models to integrate crucial, non-numerical market factors, leading to poor predictive stability during event-driven volatility. This manuscript details the DeepStock platform, an operative architecture engineered to systematically harmonize structured financial time-series data with highly descriptive, unstructured textual narratives. The system leverages the superior contextual reasoning of the Google Gemini Large Language Model (LLM) to generate robust, quantifiable financial sentiment scores. This sentiment feature vector is fused with traditional technical indicators via an Early Fusion strategy to construct a comprehensive predictive feature set. We hypothesize that models incorporating these hybrid features will yield superior risk-adjusted returns, as validated by the Sharpe Ratio, relative to purely time-series-dependent strategies. The deployment utilizes a Flask/Python framework to provide a free, real-time AI tool focused on analysis and prediction within the NSE and BSE stock markets.
How to cite this paper
@article{1717317,
author = {Shreyash Nimbargi, Vedant Joshi, Om Hundekari, Vedant Jagtap},
title = {Deepstock : AI Powered Stock Analysis},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {753-757},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717317.pdf},
abstract = {The pervasive challenge in modern quantitative finance lies in the failure of traditional models to integrate crucial, non-numerical market factors, leading to poor predictive stability during event-driven volatility. This manuscript details the DeepStock platform, an operative architecture engineered to systematically harmonize structured financial time-series data with highly descriptive, unstructured textual narratives. The system leverages the superior contextual reasoning of the Google Gemini Large Language Model (LLM) to generate robust, quantifiable financial sentiment scores. This sentiment feature vector is fused with traditional technical indicators via an Early Fusion strategy to construct a comprehensive predictive feature set. We hypothesize that models incorporating these hybrid features will yield superior risk-adjusted returns, as validated by the Sharpe Ratio, relative to purely time-series-dependent strategies. The deployment utilizes a Flask/Python framework to provide a free, real-time AI tool focused on analysis and prediction within the NSE and BSE stock markets.},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717317}
}