This paper provides a comprehensive review of stock market prediction techniques from two main perspectives: methods based solely on historical stock data, and approaches that incorporate both numerical and textual information.
The results highlight the significance of using alternative information on sentiment along with traditional market indicators and show that the machine learning techniques outperform other techniques especially LSTM.
S. Durga, B. Ratnavalli, Visalakshi Naraparedd et al.· International Academic Journ...· 0 citations
Accurate stock market forecasting remains challenging due to the nonlinear, noisy, and volatile nature of financial markets. Beyond historical prices, market movements are influenced by financial news and investor sentiment. This study proposes a hybrid financial bidirectional encoder representations from transformers...
Zakaria Al Bakkari, I. El Azami, Adil El Makrani· Bulletin of Electrical Engin...· 0 citations
Forecasting financial market movements requires integrating numerical time-series data with semantic information from financial narratives that shape investor behavior. While deep learning has advanced time-series prediction, existing methods often exploit trivial price autocorrelation rather than capturing genuine pre...
Forecasting the financial markets is challenging due to the dynamic and intricate nature of economic systems. News headlines, social media conversations, and price trends from the past all have an impact on public and investor sentiment, which in turn affects the market. Literary sources frequently contain contextual i...
P. Thara, Tarun Tripathi, Ptam Satish et al.· ITM Web of Conferences· 0 citations
This thesis investigates whether social media sentiment can improve the accuracy of stock price prediction beyond traditional historical data. While financial markets have long relied on structured numerical indicators, the growing influence of public discourse on platforms like Twitter has introduced new opportunities...
A dominance of negative emotions in financial news is revealed, with fear and sadness exerting a significant influence on adverse market movements, and emotion-augmented models outperform baseline models that rely solely on historical price data.
H. Raza, Mahnoor Altaf· Review of Behavioral Finance· 0 citations
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