Hybrid Temporal‑Relational Deep Learning for Enhanced Quantitative Investment

Optimized day trading via reinforcement learning and technical analysis using attention-LSTM, CNN, and explainable state modeling

A Hybrid CNN-LSTM Deep Learning Framework for Stock Price Prediction: Evidence from HDFC Bank on the National Stock Exchange of India

Accurate forecasting of stock prices prediction has historically been considered one of the most complex issues in capital-related research. Markets are inherently messy they are nonlinear, constantly changing and full of noise. This study proposes and tests a hybrid CNN-LSTM model that blends Convolutional Neural Netw...

Deep residual bidirectional learning for multi-horizon financial forecasting

A multi-horizon CNN-GRU architecture for gold price forecasting

These findings confirm that fusing convolutional pattern mining with streamlined recurrent gating mechanisms significantly enhances predictive precision while optimizing computational efficiency for high-dimensional financial datasets.

Adaptive mixture of experts framework for multi horizon stock market forecasting using heterogeneous deep learning models

Accurate stock price forecasting remains a fundamental challenge in computational finance due to the inherent non stationarity, volatility clustering, and complex temporal dependencies characterizing financial time series. This paper presents an Adaptive Mixture of Experts (AMoE) framework that dynamically integrates f...

Stock price prediction using multi-neighbor graph convolutional networks and chaotic spatio-temporal modeling