## Training dataset

Twitter Financial News Emotion Training Set: This dataset is a set of news tweets related to the financial sector, in English only. Its main purpose is to categorize financial sentiment in Twitter discussions. The dataset includes 9,540 samples for training, each with one of three labels: Bearish, Bullish, or Neutral. FiQA Dataset[51]: This dataset is available through HuggingFace and contains 961 samples. Each sample was labeled positive, neutral, or negative, indicating the emotion in the corresponding text. Test data set Twitter Financial News Sentiment validation set
(Twitter Val) : This dataset, available via HuggingFace, contains 2,390 samples with three labels: bearish, bullish, or neutral.
numerical Sensitive dataset: A dataset of 117 samples automatically selected from Twitter Val. These samples contain at least two numerical values associated with financial indicators, but do not contain obvious indicators such as ”up,” ”down,” ”increase,” or ”decrease.” Context: A sample of 20 randomly selected samples from the Twitter Val. These samples lacked the necessary context to make emotional predictions.
Financial PhraseBank (FPB) dataset[52]: This dataset contains 4,840 samples of randomly extracted financial news articles from the LexisNexis database. The samples were carefully annotated by a team of 16 annotators with backgrounds in finance and business, ensuring high-quality annotations.

## Model training

The training parameters are shown in Table 2. For our InstructFinGPT-7B model, we used the LLaMA-7B model for initialization and instruction adjustment fine-tuning on 10 epochs. The training process uses the AdamW optimizer [Loshchilov and Hutter, 2017] with a batch size of 32, an initial learning rate of 1e-5, and a weight decay of 0.1. To maintain efficiency, we set the maximum input text length to 512 tags. We used DeepSpeed [Rasley et al., 2020] for fine-tuning on eight A100 (40GB) GPUs with a total training time of 58 minutes.
Table 2: Training parameters.