A hybrid GAN and sentiment-conditioned model is proposed to improve time-series forecasting robustness in non-stationary financial environments by jointly modeling numerical sequences and textual exogenous information.
(2016) ‘The wisdom of Twitter crowds: predicting stock market reactions to FOMC meetings via Twitter feeds’, SSRN Electronic Journal
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Beyond Sequential Prediction: Learning Financial Market Dynamics in Volatile and Non-Stationary Environments through Sentiment-Conditioned Generative Modelling
A hybrid GAN and sentiment-conditioned model is proposed to improve time-series forecasting robustness in non-stationary financial environments by jointly modeling numerical sequences and textual exogenous information.