The Bull and the Bear: Summarizing Stock Market Discussions

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 6909–6913 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 6909 The Bull and the Bear: Summarizing Stock Market Discussions Ayush Kumar*, Dhyey Jani*, Jay Shah*, Devanshu Thakar*, Varun Jain, Mayank Singh Indian Institute of Technology Gandhinagar Palaj, Gandhinagar, Gujarat, India 382355 {ayush.kumar, jani.dhyey, shah.jay, nilesh.thakar, varun.jain, singh.mayank}@iitgn.ac.in Abstract Stock market investors debate and heavily discuss stock ideas, investing strategies, news and market movements on social media platforms. The discussions are significantly longer in length and require extensive domain expertise for understanding. In this paper, we curate such discussions and construct a first-of-its-kind of abstractive summarization dataset. Our curated dataset consists of 7888 Reddit posts and manually constructed summaries for 400 posts. We robustly evaluate the summaries and conduct experiments on SOTA summarization tools to showcase their limitations. We plan to make the dataset publicly available. The sample dataset is available here: https://dhyeyjani.github.io/RSMC. Keywords: Stock Market, Summarization, Reddit 1. Introduction Commodity and equity trading on the open market is often viewed as a demanding and time-consuming ac- tivity. Stock markets have grown in popularity in recent years, with a dramatic increase in daily active users. With an ever-increasing volume of daily transactions, the stock market is becoming highly volatile to various fundamental features of the entity that underpins the trading commodity. To keep up with this pace, the in- vestor community discusses trading strategies, invest- ment and global news, pitfalls and movements, mar- ket sentiments, etc., on various social media platforms like Twitter, Reddit, Yahoo.com, etc. However, under- standing and participating in these discussions require significant domain expertise and knowledge. Figure 1 presents a Reddit post that technically anal- yses the stock market crash of Feb/March 2020. As expected, it contains several domain-specific entities like VIX, SPX, RBLX, GME, NASDAQ, Microsoft, Ap- ple, Amazon, Alphabet and Facebook. Entities such as VIX, SPX, and RBLX are stock market indexes, whereas GME is the stock acronym of video game re- tailer GameStop Corp. NASDAQ, Microsoft, Apple, Amazon, Alphabet and Facebook are the names of or- ganizations. The post also contains several date and time instances such as March 2021, Nov 2020, May 2020, Jan 2022 Nov 2018, Feb 2020, etc. In addition, it contains relative trend markers like 40%, 50%, etc. The post illustrates the textual complexities due to domain- specific terms with usual complexities associated with social media data such as shorthand notations, usage of symbols, emojis, etc. The majority of the stock market-related datasets12 fo- cus on the entire historical daily price and volume data *These authors contributed equally. 1https://www.kaggle.com/borismarjanovic/price-volume- data-for-all-us-stocks-etfs 2https://www.kaggle.com/rohanrao/nifty50-stock- market-data Figure 1: A stock market discussion post in Reddit. (numerical values). It has shown that the tone of dis- cussions on the subreddit r/wallstreetbets displays sig- nificant predictive associations with GameStop returns, volatility, bid-ask spreads as well as volume (Anand and Pathak, 2021). However, to the best of our knowl- edge, we do not find any dataset that contains tex- …

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