The growing popularity of text-as-data in various domain-specific applications and research has often relied on manually selected keywords or annotations. Although labor-intensive, expensive and time-consuming, the effectiveness of these efforts is not always guaranteed, especially in the early stages of research. This predicament raises the question of the extent to which large language models (LLMs) can aid in verifying the potential of a nascent research idea. This paper seeks to explore the reliability of LLM-suggested keywords in the automatic construction of the Economic Policy Uncertainty (EPU) index. Our findings confirm that LLMs can effectively automate the construction of EPU index. Furthermore, we delve into the potential of LLMs in enhancing the indicator construction process.

In this article, we provide an overview of ACM TWEB’s special issue, Financial Technology on the Web . This special issue covers diverse topics: (1) a new architecture for leveraging online news to investment and risk management, (2) a cross-platform analysis of the post quality and users’ behaviors, and (3) an empirical study on disentangling decentralized finance compositions. In addition to a guide for the special issue, we also share a brief opinion on the future of financial technology on the Web.

The economic policy uncertainty (EPU) index is one of the important text-based indexes in finance and economics fields. The EPU indexes of more than 26 countries have been constructed to reflect the policy uncertainty on country-level economic environments and serve as an important economic leading indicator. The EPU indexes are calculated based on the number of news articles with some manually-selected keywords related to economic, uncertainty, and policy. We find that the keyword-based EPU indexes contain noise, which will influence their explainability and predictability. In our experimental dataset, over 40% of news articles with the selected keywords are not related to the EPU. Instead of using keywords only, our proposed models take contextual information into account and get good performance on identifying the articles unrelated to EPU. The noise free EPU index performs better than the keyword-based EPU index in both explainability and predictability.

The volatility of stock price reflects the risk of stock and influences the risk of investor's portfolio. It is also a crucial part of pricing derivative securities. Researchers have paid their attention to predict the stock volatility with different kinds of textual data. However, most of them focus on using word information only. Few touch on capturing the numeral information in textual data, providing fine-grained clues for financial document understanding. In this paper, we present a novel dataset, ECNum, for understanding the numerals in the transcript of earnings conference calls. We propose a simple but efficient method, Numeral-Aware Model (NAM), for enhancing the capacity of numeral understanding of neural network models. We employ the distilled information in the stock volatility forecasting task and achieve the best performance compared to the previous works in short-term scenarios.