Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Document Type
Event
Start Date
22-4-2026 4:00 PM
Description
Three feedforward neural network (FFNN) architectures — bottleneck, parallel multi-path, and residual parallel — were trained on ten years of daily S&P 500 (SPY ETF) price and volume data to predict next-day market direction (Up/Down). All three demonstrated predictive ability above random chance. Architectural choice directly determined class prediction bias: the bottleneck concentrated errors on Up days, the parallel architecture distributed them evenly, and residual connections inverted the bias toward Down days. Model 2 (parallel) achieved the highest test accuracy (58.2%) and the most balanced class predictions among the three configurations.
Included in
GRM-169-137 Predicting the Stock Market's Next Move: How Neural Network Architecture Shapes Forecasting Accuracy
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Three feedforward neural network (FFNN) architectures — bottleneck, parallel multi-path, and residual parallel — were trained on ten years of daily S&P 500 (SPY ETF) price and volume data to predict next-day market direction (Up/Down). All three demonstrated predictive ability above random chance. Architectural choice directly determined class prediction bias: the bottleneck concentrated errors on Up days, the parallel architecture distributed them evenly, and residual connections inverted the bias toward Down days. Model 2 (parallel) achieved the highest test accuracy (58.2%) and the most balanced class predictions among the three configurations.