Enhanced Deep Learning-Based Stock Prediction Platform using BiLSTM for Accurate Market Analysis

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@article{1707714,
      author = {Naveenkrishna A S, Mohamed Hasheem N, Praveen K, Shivashankar S},
      title = {Enhanced Deep Learning-Based Stock Prediction Platform using BiLSTM for Accurate Market Analysis},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {10},
      pages = {21-27},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707714.pdf},
      abstract = {Since deep learning algorithms can identify complex patterns in market data, there has been an increasing interest in using them for financial prediction in recent years. In this study, the stock price movements of multiple markets were predicted using a Bidirectional Long Short-Term Memory (BiLSTM) neural network. A forecast model that can spot patterns and trends in stock prices was developed using data from Yahoo Finance. The experiment demonstrated the model's effectiveness in stock price forecasting by performing well on the test set. The findings have implications for risk assessment and financial decision-making and emphasize the significance of using deep learning techniques for stock price prediction.This experiment successfully predicted stock price patterns with good accuracy by modeling complex temporal relationships using a Bidirectional Long Short-Term Memory (BiLSTM) neural network. Enhancing the model's resistance to various market conditions and using sentiment analysis to more accurately forecast future events are two more areas for future research. Overall, this study improves stock price forecasting techniques, facilitating well-informed financial choices.},
      keywords = {Deep learning; BiLSTM, stock price prediction; financial decision-making; risk management.},
      month = {April},
  }