Enhanced Quantitative Financial Analysis Using CNN-LSTM Cross-Stitch Hybrid Networks for Feature Integration

This research paper provides innovative approaches to support financial prediction, or it is a different kind of economic prediction that extends over collecting different economic information. Financial prediction is a concept that has been employed. The present study offers a unique approach to predicting finances by integrating many financial issues utilizing a cross-stitch hybrid approach. The method uses information from several financial databases, including market data, corporate reports, and macroeconomic indicators, to create a comprehensive dataset. Employing MinMax normalization the features are equally scaled to provide uniform input for the algorithm. The combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) systems form the basis of the framework. To understand the time-dependent nature of financial information, LSTM networks (long short-term memory) are utilized to record and simulate the temporal interactions and patterns. Concurrently, spatial features are extracted using CNNs; these components help identify patterns that are difficult to identify with conventional techniques. Better handling of risks, more optimal approaches to investing, and more informed decision-making are made possible by the enhanced forecasting potential that this method—which is described above—offers. Potential pilot studies will focus on innovative uses in financial decision-making and advancements in cross-stitching structure. This paper proposes a sophisticated approach that can help stakeholders, such as investors, analysts of data, and other financial intermediaries, traverse the complexities of financial markets.

@article{Gongada2024,
  title     = {Enhanced Quantitative Financial Analysis Using CNN-LSTM Cross-Stitch Hybrid Networks for Feature Integration},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Taviti Naidu Gongada and B. Kumar Babu and Janjhyam Venkata Naga Ramesh and P. N. V. Syamala Rao M and K. Aanandha Saravanan and K Swetha and Mano Ashish Tripathi},
  doi       = {10.14569/IJACSA.2024.0150977},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150977}
}