The FIRE Summit 2024

Permanent URI for this collectionhttp://hdl.handle.net/1903/33513

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    Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field And CNNs for Stock Return Predictions
    (2024) Bapanapalli, Abhinav; Mahmud, Ateef; Nadig, Chiraag; Thakker, Krish; Jabeen, Shabnam
    Predicting stock price movements is a complex challenge faced by many traders and analysts. Our research leverages Quantum Gramian Angular Field (QGAF) transformations combined with Convolutional Neural Networks (CNNs) to classify stock price trends as "up" or "down." By transforming 1D time-series stock data into 2D images, we enable CNNs to extract features more effectively, showcasing the potential of quantum machine learning in financial forecasting.