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NIFTY Financial News Headlines Dataset

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  • Raeid Saqur
  • Ken Kato
  • Nicholas Vinden
  • Frank Rudzicz

Abstract

We introduce and make publicly available the NIFTY Financial News Headlines dataset, designed to facilitate and advance research in financial market forecasting using large language models (LLMs). This dataset comprises two distinct versions tailored for different modeling approaches: (i) NIFTY-LM, which targets supervised fine-tuning (SFT) of LLMs with an auto-regressive, causal language-modeling objective, and (ii) NIFTY-RL, formatted specifically for alignment methods (like reinforcement learning from human feedback (RLHF)) to align LLMs via rejection sampling and reward modeling. Each dataset version provides curated, high-quality data incorporating comprehensive metadata, market indices, and deduplicated financial news headlines systematically filtered and ranked to suit modern LLM frameworks. We also include experiments demonstrating some applications of the dataset in tasks like stock price movement and the role of LLM embeddings in information acquisition/richness. The NIFTY dataset along with utilities (like truncating prompt's context length systematically) are available on Hugging Face at https://huggingface.co/datasets/raeidsaqur/NIFTY.

Suggested Citation

  • Raeid Saqur & Ken Kato & Nicholas Vinden & Frank Rudzicz, 2024. "NIFTY Financial News Headlines Dataset," Papers 2405.09747, arXiv.org.
  • Handle: RePEc:arx:papers:2405.09747
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    References listed on IDEAS

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    5. Pekka Malo & Ankur Sinha & Pekka Korhonen & Jyrki Wallenius & Pyry Takala, 2014. "Good debt or bad debt: Detecting semantic orientations in economic texts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 782-796, April.
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    Cited by:

    1. Raeid Saqur & Anastasis Kratsios & Florian Krach & Yannick Limmer & Jacob-Junqi Tian & John Willes & Blanka Horvath & Frank Rudzicz, 2024. "Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language Models," Papers 2406.02969, arXiv.org.

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