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19 lip 2021 · In this paper, we are interested in predicting the short-term movement of stock prices after financial news events using only the headlines of the news. To achieve this goal, we introduce a new text mining method called Fine-Tuned Contextualized-Embedding Recurrent Neural Network (FT-CE-RNN).
24 sie 2024 · In this paper, we propose a multimodal deep fusion model to predict stock trends, leveraging daily stock prices, technical indicators, and sentiment in daily news headlines published by media outlets.
Reviews the literature on data-driven neural networks in the field of stock forecasting. Outlines the commonly used datasets and various evaluation metrics in the field of stock forecasting. Explores unresolved issues and potential future research directions in stock forecasting.
23 lip 2023 · In this paper, inspired by the promising learning capabilities of hybrid ensemble methods, we propose a novel stacking ensemble approach for stock market prediction that jointly considers news headlines, multi-variate time series data, and multiple base models as predictors.
28 kwi 2022 · In this paper we examine whether daily news sentiment of several companies and Twitter sentiment from their CEOs have an impact on their market performance and whether traditional news sources and Twitter activity of heads of government impact the benchmark indexes of major world economies over a period spanning the outbreak of the SAR-COV-2 pan...
30 lis 2023 · The methodology combines statistical techniques to assess sentiment’s predictive power for stock opening and closing prices, while wavelet coherence analysis unveils the temporal dynamics of these relationships.
9 lut 2024 · FNSPID encompasses a wide range of financial news in English and Russian, covering 1999 to 2023. FNSPID correlates news with stock prices, serving as a valuable resource for sentiment analysis and stock price prediction.
