Report NEP-FOR-2025-03-24

This is the archive for NEP-FOR, a report on new working papers in the area of Forecasting. Malte Knüppel issued this report. It is usually issued weekly.

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Other reports in NEP-FOR

The following items were announced in this report:

• Daksh Dave & Gauransh Sawhney & Vikhyat Chauhan, 2025. "Multi-Agent Stock Prediction Systems: Machine Learning Models, Simulations, and Real-Time Trading Strategies," Papers 2502.15853, arXiv.org.
• Furkan Karadac{s} & Bahaeddin Eravc{i} & Ahmet Murat Ozbayou{g}lu, 2025. "Multimodal Stock Price Prediction," Papers 2502.05186, arXiv.org.
• Sihan Tu & Zhaoxing Gao, 2025. "A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting," Papers 2502.15275, arXiv.org.
• Wei Miao & Jad Beyhum & Jonas Striaukas & Ingrid Van Keilegom, 2025. "High-dimensional censored MIDAS logistic regression for corporate survival forecasting," Papers 2502.09740, arXiv.org.
• Meet Satishbhai Sonani & Atta Badii & Armin Moin, 2025. "Stock Price Prediction Using a Hybrid LSTM-GNN Model: Integrating Time-Series and Graph-Based Analysis," Papers 2502.15813, arXiv.org.
• Amit Kumar & Taoran Ji, 2025. "CryptoPulse: Short-Term Cryptocurrency Forecasting with Dual-Prediction and Cross-Correlated Market Indicators," Papers 2502.19349, arXiv.org, revised Feb 2025.
• Zhao, Yu, 2024. "From Offer to Close: A Machine Learning Approach to Forecast Real Estate Transaction Outcomes," OSF Preprints sxmq2_v1, Center for Open Science.
• Zhipeng Liu & Peibo Duan & Mingyang Geng & Bin Zhang, 2025. "A Distillation-based Future-aware Graph Neural Network for Stock Trend Prediction," Papers 2502.10776, arXiv.org.