The field of predictive modeling for financial markets is witnessing significant advancements with the integration of multimodal inputs, attention mechanisms, and contextualization methods. Researchers are exploring the use of large language models, reinforcement learning, and particle swarm optimization to improve the accuracy and interpretability of predictive models. The incorporation of historical context, analogical reasoning, and structured semantic signals is also being investigated to enhance the performance of financial sentiment analysis and forecasting models. Noteworthy papers in this area include:

• A multimodal forecasting framework that combines click data with textual logs to generate human-interpretable explanations alongside numeric predictions.
• A novel approach to optimizing pricing and replenishment strategies in fresh food supermarkets by combining Long Short-Term Memory networks with Particle Swarm Optimization.
• A contextualization method that uses a large language model to process the main article, while a small language model encodes the historical context into concise summary embeddings.
• A prompting framework that integrates analogical reasoning with chain-of-thought prompting for sentiment prediction on historical financial news.
• A multi-LLM framework that integrates an expert panel of sentiment forecasting language models, and structured semantic financial signals via a compact meta-classifier.
• A shapelet-based framework that integrates unsupervised pattern extraction with interpretable forecasting for directional forecasting in noisy financial markets.