FinStock-Net – Financial Integration of Short & Medium Trends for Stock Price Prediction - Accepted at CICBA2025

Authors : Anubhab Bhattacharya, Abir Chakraborty, Soham Mandal , Aritra Chatterjee, Utathya Aich and Ram Sarkar

This is the official implementation of "FinStock-Net – Financial Integration of Short & Medium Trends for Stock Price Prediction".

Proposed workflow:

Abstract

The stock market serves as a fundamental pillar of the global financial ecosystem, influencing macroeconomic stability and investment strategies. Among various financial indicators, the closing price is a critical metric, encapsulating aggregated market sentiment and informing risk assessment, portfolio optimization, and algorithmic trading. Despite its significance, precise forecasting of closing prices remains a formidable challenge due to the stochastic nature of financial markets, characterized by high volatility, non-stationarity, and susceptibility to exogenous economic shocks. We propose FinStock-Net, a multi-scale temporal fusion model that leverages Bidirectional Long Short-Term Memory (BiLSTM) networks for temporal analysis and a gated fusion mechanism to balance short-term (1-day), mid-term (7-day), and long-term (15-day) market dynamics, aligning with real-world decision-making. Our framework also integrates volatility-sensitive indicators, called VIX index, to enhance robustness against abrupt fluctuations. Unlike conventional approaches. FinStock-Net employs adaptive gating mechanisms to dynamically reweight temporal features, ensuring contextual alignment with real-world decision-making horizons. We benchmark FinStock-Net on publicly available financial datasets like Nifty50, Sensex, and S&P500, demonstrating its superior predictive accuracy over some existing models and establishing it as a robust framework for stock market forecasting.

Dataset Sample from Sensex with VIX

FinStock-Net Performance

Sample FinStock-Net Predictions

Citation: ( Will be updated )

Please do cite our paper in case you find it useful for your research.
If you're using this article or code in your research or applications, please consider citing using this BibTeX:

@inproceedings{Bhattacharya2025finstocknet,
  title={FinStock-Net – Financial Integration of Short & Medium Trends for Stock Price Prediction},
  author={ Bhattacharya Anubhab, Chakraborty Abir , Mandal Soham  , Chatterjee Aritra , Aich Utathya  and Sarkar Ram },
  ConferenceTitile={CICBA2025},
  year={2025},
  note={Accepted for publication}
}