FinLlama: LLM-Based Financial Sentiment Analysis for Algorithmic Trading
Authors: Giorgos Iacovides, Thanos Konstantinidis, Mingxue Xu, Danilo Mandic


Reviewer paper summaries: The paper introduces FinLlama, a financial sentiment analysis model fine-tuned based on the Llama 2 7B. The authors applied PEFT and 8-bit quantization via LoRA to improve efficiency and make it available for single GPU settings. The proposed method was evaluated in a portfolio construction task where the sentiment score was applied as the asset ranking metric directly. The results show that the proposed method effectively outperformed the baselines.
Summary of strengths: 1. The proposed approach is straightforward yet effective in financial sentiment analysis
2. The use of PEFT and 8-bit quantization via LoRA minimizes computational requirements, making it feasible for researchers without high-end GPUs.
3. The authors provide a detailed description of the training process, and the finetuning datasets used are publicly accessible, which improves the accessibility of work.
Summary of weaknesses: 1. The authors did not release the test dataset or the code, which hinders future researchers from validating the results and building upon this work.    
2. The contribution is very incremental. While the paper demonstrates a successful fine-tuning recipe for financial sentiment analysis, the authors didn't show new techniques or insights. The method is a regular sentiment analysis-based portfolio construction with a fine-tuning of Llama in financial sentiment analysis datasets.
Comments, suggestions and typos: 1. In the abstract and Sec. 3.1, the authors mention that the FinLlama acts as a generator-discriminator, however, the generator-discriminator scheme usually depicts two models in a two-person adversarial game. It is confusing why FinLlama can be called a "generator-discriminator" scheme. Please elaborate.
2. Whether the return is the same-day return or the next-day return of the date of input news? The authors should make it more explicit in the method part.
3. Line 081, seems to be a redundant period.
4. Figure 1 seems to be redundant and deviates from the major topic of the paper, it also occupies a large area of the paper. Please considering remove it.
5. The Table 1 Definition also seems too tedious as they are generally acceptable concepts in either the NLP or financial ML community. Considering remove them.
6. It is surprising that solely using daily sentiment scores without any market or fundamental data can lead to such a high performance even using a non-deep learning approach. News is usually regarded as “alternative data” that has a lower signal-to-noise ratio compared to the major market and economic indicators. It would be useful to have a discussion about this phenomenon. The author could also provide a comparison to factor-based approaches in the same period and analyze their difference in portfolios to gain more insights.
Overall assessment scores: 2