Version 1
: Received: 4 July 2024 / Approved: 4 July 2024 / Online: 6 July 2024 (05:27:26 CEST)
How to cite:
Mtetwa, J. T.; Ogudo, K.; Pudaruth, S. Enhancing Stock Price Forecasting with Generative Adversarial Networks and Conformal Prediction: A Novel Approach for Quantifying Uncertainty. Preprints2024, 2024070479. https://doi.org/10.20944/preprints202407.0479.v1
Mtetwa, J. T.; Ogudo, K.; Pudaruth, S. Enhancing Stock Price Forecasting with Generative Adversarial Networks and Conformal Prediction: A Novel Approach for Quantifying Uncertainty. Preprints 2024, 2024070479. https://doi.org/10.20944/preprints202407.0479.v1
Mtetwa, J. T.; Ogudo, K.; Pudaruth, S. Enhancing Stock Price Forecasting with Generative Adversarial Networks and Conformal Prediction: A Novel Approach for Quantifying Uncertainty. Preprints2024, 2024070479. https://doi.org/10.20944/preprints202407.0479.v1
APA Style
Mtetwa, J. T., Ogudo, K., & Pudaruth, S. (2024). Enhancing Stock Price Forecasting with Generative Adversarial Networks and Conformal Prediction: A Novel Approach for Quantifying Uncertainty. Preprints. https://doi.org/10.20944/preprints202407.0479.v1
Chicago/Turabian Style
Mtetwa, J. T., Kingsley Ogudo and Sameerchand Pudaruth. 2024 "Enhancing Stock Price Forecasting with Generative Adversarial Networks and Conformal Prediction: A Novel Approach for Quantifying Uncertainty" Preprints. https://doi.org/10.20944/preprints202407.0479.v1
Abstract
This study introduces a novel method that combines Generative Adversarial Networks (GANs) with conformal prediction to revolutionize the domain of stock ticker forecasting. Our approach enhances prediction accuracy and offers dependable prediction intervals to gauge uncertainty, surpassing current GAN-based techniques in unpredictable market scenarios. We made a validation using AAPL stock data, showing the ability of our model to generate accurate forecasts and reliable assessments of prediction uncertainty. The prediction intervals generated provide investors and risk managers with a powerful tool to make informed decisions. Our approach represents a notable advancement in financial prediction as it improves the clarity and reliability of forecasts. By incorporating conformal prediction into the GAN framework, the reliability of forecasts is improved, providing a thorough evaluation of uncertainty that is crucial for successful risk management. This framework has the potential to transform the field of predictive analytics in finance. It shows a solution that balances accuracy and uncertainty quantification. The results clearly show the effectiveness of this method, paving the way for its potential application in a wide range of financial prediction scenarios.
Keywords
Generative Adversarial Networks; Conformal Prediction; Stock Forecasting; Uncertainty Quantification; Financial Time Series
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
