Playing with News Context for Algorithmic Trading
Authors: Surupendu Gangopadhyay


Reviewer paper summaries: The authors introduced an RL framework for minute-level high-frequency trading in the index futures market by combining past 1-hour news titles with 5-minute technical indicators. They proposed two approaches to utilizing the news data: a sentiment-aware approach, which uses FinBERT to generate sentiment scores from news titles, and a context-aware approach, which employs LLMs for sentence embedding of the titles. The proposed methods showed good returns and a reduced MDD in the NIFTY 50 index dataset compared to the PPO and DQN baselines.
Summary of strengths: 1. The authors clearly illustrated their method and experiment setups.
2. The proposed method is simple yet achieved good returns
3. The proposed method can be applied in minute-level high-frequency trading which can be useful in practice
Summary of weaknesses: 1. The proposed method only used the title of the news which omitted rich information from the main body, and only 10 latest news are considered in a context-aware approach which may not be able to summarize the trend of the market. Moreover, the news is restricted to around trading hours, while a lot of breaking news and important news such as financial statement releases occur beyond trading hours.
2. The volatility and MDD of the proposed method are high and higher than price data-only methods. The authors also didn't use the popular risk-adjust returns such as the Sharpe Ratio in their metrics. The baselines are also restricted to the basic DQN and PPO.
3. The trading frictions are not considered which has a strong impact on HFT.
Comments, suggestions and typos: 1. In Table 3, Avg. volatility column, 2.02 is the lowest but 2.16 was bolded.
2. Efficiency is important in HFT, an efficiency and inference cost analysis of the proposed method can provide valuable references for practitioners.
3. Given the setting of high-frequency trading and title-only input, why use a high-cost LLM instead of a smaller one which may be enough for the short inputs? 
4. The authors can introduce an ablation study using small-size language models like BERT and even the low-cost traditional NLP techniques as a comparison.
Overall assessment scores: 1.5