NOTE: Please refer this pdf to note down which points needed to be change marked as highlighted VIVA-Tech IJRI V1, E8 Article - 28 ">

InvestIQ: Empowering Investors with Machine Learning Insights


EOI: 10.11242/viva-tech.01.08.037

Download Full Text here



Citation

Aditya A. Sonawane, Karishma Raut, Minakshi Gaonkar, " InvestIQ: Empowering Investors with Machine Learning Insights ", VIVA-IJRI Volume 1, Issue 7, Article 28, pp. 1-7, 2025. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

" Stock market trading is essential in finance, offering significant potential for wealth growth but also carrying substantial risks. Successful investment requires thorough research into historical stock prices and real-time developments. This paper examines various approaches to stock movement prediction, highlighting advancements in machine learning techniques such as Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks for predicting stock prices across multiple markets and timeframes. It explores the use of Python-based algorithms and the Dash framework for visualizing financial data, aiming to enhance understanding and decision-making in stock market dynamics. This comprehensive resource blends technology, finance, and data science, offering a dynamic exploration of stock market analysis. It serves as a transformative guide for developing robust financial models and predicting future market movements. "

Keywords

close price, finance, graphs, market research, price prediction, stocks.

References

  1. A. Sharma and R. Gupta, "Stock Market Price Prediction: A Hybrid LSTM and Sequential SelfAttention based Approach," arXiv preprint arXiv:2308.04419, Aug. 2023.
  2. M. Wang, X. Li, and Y. Zhang, "FinBERT-LSTM: Deep Learning-Based Stock Price Prediction Using News Sentiment Analysis," arXiv preprint arXiv:2211.07392, Nov. 2022.
  3. H. Patel and K. Shah, "Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models," arXiv preprint arXiv:2010.13891, Oct. 2020.
  4. S. Kumar and A. Yadav, "Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models," arXiv preprint arXiv:2009.10819, Sep. 2020.
  5. J. Lee and D. Kim, "Support Vector Machine-Based Stock Market Prediction Using Long Short-Term Memory and Convolutional Neural Network with Aquila Circle Inspired Optimization," International Journal of Computer Applications, vol. 190, no. 3, pp. 1-9, June 2024.
  6. K. Wong and T. Chan, "An Exploratory Comparison of Stock Prices Prediction Using Multiple Machine Learning Approaches Based on Hong Kong Share Market," Proceedings of the ACM International Conference on AI and Finance, Dec. 2023.
  7. L. Zhang and B. Chen, "A Hybrid Model for Stock Price Prediction Based on Multi-View Heterogeneous Data," Financial Innovation Journal (Springer), vol. 10, no. 1, pp. 1-14, Feb. 2024.
  8. R. Singh and P. Verma, "Stock Price Prediction Based on SVM, LSTM, ARIMA," Journal of Business and Management Research, vol. 5, no. 4, pp. 89-98, Dec. 2022.
  9. C. Brown and X. Li, "Stock Market Prediction Using Machine Learning Techniques: A Comparative Study," IEEE Transactions on Computational Intelligence and AI in Finance, vol. 8, no. 2, pp. 233245, Jan. 2023.
  10. T. Anderson and H. Liu, "Deep Learning for Stock Price Prediction: A Study on the Impact of News Headlines," IEEE Access, vol. 11, pp. 56789-56801, Mar. 2023.
  11. V. Ramesh and J. Park, "Enhancing Stock Price Prediction Accuracy Using Hybrid SVM-LSTM Model," Journal of Financial Data Science, vol. 12, no. 3, pp. 120-135, July 2023.
  12. F. Gomez and S. Chatterjee, "Financial Time Series Forecasting with Deep Learning: A Review," IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 4, pp. 2901-2915, Apr. 2023.
  13. M. Johnson and L. Nguyen, "Predicting Stock Market Movements with Ensemble Learning and Sentiment Analysis," IEEE Transactions on Big Data, vol. 10, no. 2, pp. 154-167, June 2023.
  14. X. Wang and T. Sun, "Application of LSTM Networks in Predicting Stock Prices: A Case Study of the S&P 500," Proceedings of the IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 78-85, May 2023.
  15. J. Kim and Y. Lee, "Support Vector Machines for Stock Price Prediction: A Comprehensive Review," IEEE Transactions on Computational Finance, vol. 9, no. 1, pp. 201-215, Feb. 2023.