Predicting Stock Movement with BERTweet and Transformers

• URL: http://arxiv.org/abs/2503.10957v1
• Date: Thu, 13 Mar 2025 23:46:24 GMT
• Title: Predicting Stock Movement with BERTweet and Transformers
• Authors: Michael Charles Albada, Mojolaoluwa Joshua Sonola,
• Abstract summary: In this paper, we demonstrate the efficacy of BERTweet, a variant of BERT pre-trained specifically on a Twitter corpus.<n>We set a new baseline for Matthews Correlation Coefficient on the Stocknet dataset without auxiliary data sources.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Applying deep learning and computational intelligence to finance has been a popular area of applied research, both within academia and industry, and continues to attract active attention. The inherently high volatility and non-stationary of the data pose substantial challenges to machine learning models, especially so for today's expressive and highly-parameterized deep learning models. Recent work has combined natural language processing on data from social media to augment models based purely on historic price data to improve performance has received particular attention. Previous work has achieved state-of-the-art performance on this task by combining techniques such as bidirectional GRUs, variational autoencoders, word and document embeddings, self-attention, graph attention, and adversarial training. In this paper, we demonstrated the efficacy of BERTweet, a variant of BERT pre-trained specifically on a Twitter corpus, and the transformer architecture by achieving competitive performance with the existing literature and setting a new baseline for Matthews Correlation Coefficient on the Stocknet dataset without auxiliary data sources.

Related papers

• Post-Training Language Models for Continual Relation Extraction [0.0]
  This study investigates the application of pre-trained language models (PLMs), specifically large language models (LLMs) to knowledge graphs. We evaluate decoder-only models (eg, Mistral-7B and Llama2-7B) and encoder-decoder models (eg, Flan-T5 Base) on the TACRED and FewRel datasets.
  arXiv  Detail & Related papers  (2025-04-07T16:01:22Z)
• READ: Reinforcement-based Adversarial Learning for Text Classification with Limited Labeled Data [7.152603583363887]
  Pre-trained transformer models such as BERT have shown massive gains across many text classification tasks.<n>This paper proposes a method that encapsulates reinforcement learning-based text generation and semi-supervised adversarial learning approaches.<n>Our method READ, Reinforcement-based Adversarial learning, utilizes an unlabeled dataset to generate diverse synthetic text through reinforcement learning.
  arXiv  Detail & Related papers  (2025-01-14T11:39:55Z)
• DreamMask: Boosting Open-vocabulary Panoptic Segmentation with Synthetic Data [61.62554324594797]
  We propose DreamMask, which explores how to generate training data in the open-vocabulary setting, and how to train the model with both real and synthetic data. In general, DreamMask significantly simplifies the collection of large-scale training data, serving as a plug-and-play enhancement for existing methods. For instance, when trained on COCO and tested on ADE20K, the model equipped with DreamMask outperforms the previous state-of-the-art by a substantial margin of 2.1% mIoU.
  arXiv  Detail & Related papers  (2025-01-03T19:00:00Z)
• PILOT: A Pre-Trained Model-Based Continual Learning Toolbox [65.57123249246358]
  This paper introduces a pre-trained model-based continual learning toolbox known as PILOT.<n>On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt.<n>On the other hand, PILOT fits typical class-incremental learning algorithms within the context of pre-trained models to evaluate their effectiveness.
  arXiv  Detail & Related papers  (2023-09-13T17:55:11Z)
• Investigating Masking-based Data Generation in Language Models [0.0]
  A feature of BERT and models with similar architecture is the objective of masked language modeling. Data augmentation is a data-driven technique widely used in machine learning. Recent studies have utilized masked language model to generate artificially augmented data for NLP downstream tasks.
  arXiv  Detail & Related papers  (2023-06-16T16:48:27Z)
• A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition [11.928873764689458]
  We conduct a comprehensive evaluation of popular deep learning approaches for emotion recognition. We show that long-range dependencies in the speech signal are critical for emotion recognition. Speed/rate augmentation offers the most robust performance gain across models.
  arXiv  Detail & Related papers  (2022-11-09T17:27:03Z)
• Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification [83.23129279407271]
  We propose a method to efficiently retain the knowledge available in a neural network pre-trained on a set of securities. In our method, the prior knowledge encoded in a pre-trained neural network is maintained by keeping existing connections fixed. This knowledge is adjusted for the new securities by a set of augmented connections, which are optimized using the new data.
  arXiv  Detail & Related papers  (2022-07-23T18:54:10Z)
• Curriculum-Based Self-Training Makes Better Few-Shot Learners for Data-to-Text Generation [56.98033565736974]
  We propose Curriculum-Based Self-Training (CBST) to leverage unlabeled data in a rearranged order determined by the difficulty of text generation. Our method can outperform fine-tuning and task-adaptive pre-training methods, and achieve state-of-the-art performance in the few-shot setting of data-to-text generation.
  arXiv  Detail & Related papers  (2022-06-06T16:11:58Z)
• BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search engines [49.75878234192369]
  We present WEAVER, a simple, yet efficient post-processing method that infuses old knowledge into the new model. We show that applying WEAVER in a sequential manner results in similar word embedding distributions as doing a combined training on all data at once.
  arXiv  Detail & Related papers  (2022-02-21T10:34:41Z)
• Transferring BERT-like Transformers' Knowledge for Authorship Verification [8.443350618722562]
  We study the effectiveness of several BERT-like transformers for the task of authorship verification. We provide new splits for PAN-2020, where training and test data are sampled from disjoint topics or authors. We show that those splits can enhance the models' capability to transfer knowledge over a new, significantly different dataset.
  arXiv  Detail & Related papers  (2021-12-09T18:57:29Z)
• Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction [13.354066085659198]
  Contrastive learning is not widely utilized in natural language processing due to the lack of a general method of data augmentation for text data. In this work, we explore the method of employing contrastive learning to improve the text representation from the BERT model for relation extraction. The experimental results on three relation extraction benchmark datasets demonstrate that our method can improve the BERT model representation and achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-04-28T17:50:24Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.