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

Abstract: 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.

Taviti Naidu Gongada, B. Kumar Babu, Janjhyam Venkata Naga Ramesh, P. N. V. Syamala Rao M, K. Aanandha Saravanan, K Swetha and Mano Ashish Tripathi. “Enhanced Quantitative Financial Analysis Using CNN-LSTM Cross-Stitch Hybrid Networks for Feature Integration”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.9 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150977

@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},
doi = {10.14569/IJACSA.2024.0150977},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150977},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {9},
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}
}