Call for papers: Large language models in economics and finance
Interest in large language models (LLMs) has skyrocketed since the introduction of ChatGPT towards the end of 2022. Since then, it has captured the attention of researchers, who have already extensively considered how access to them might impact economic and financial decision-making. Progress in large language models has not stopped. In September of 2023, OpenAI introduced GPT with Vision, which substantially increased its use in economics and finance: language models can now analyze not only textual data but also images.
Given these rapid advancements, Economics and Business Review will publish a thematic issue showing empirical or theoretical studies on the use of large language models in economics and finance. In particular, we solicit submissions dealing with issues such as:
- The simulation of economic behaviour via large language models (see, e.g., Horton, 2023);
- The use of large language models to analyse large corpora of economic and financial data (see, e.g., Hansen and Kazinnik, 2023);
- Differences in the quality of advice concerning economic matters and/or adherence to it between language models (e.g., OpenAI, Google Gemini, Anthropic);
- The application of multimodal large language models – that are able to analyze images, sound, or video (e.g., GPT with Vision, Gemini) – in economics in finance;
- Macro- or microeconomic consequences of the widespread access to large language models.
We especially encourage the submission of papers that investigate how people use large language models, in line with the literature on machine behaviour (Rahwan et al., 2019). However, all submitted papers must focus on issues related to economics or finance.
Length of articles
Submitted papers can be full-length papers or brief reports. The latter have been introduced in EBR in 2023, and allow to communicate with readers in a more compact way. We would like to encourage users to submit manuscripts in the brief report format. Please note that we do not strictly require adherence to this format after revision: if submitters find that it is not possible to incorporate all of the revisions suggested by reviewers in the revise & resubmit stage, then it is possible to switch to a full-length format. However, the default approach is to adhere to the initial space constraints.
Open science recommendations
In line with best scientific practices, we encourage authors of empirical papers to: (1) provide open access to their data (e.g., on a website such as OSF), and to (2) pre-register their studies prior to conducting them (e.g., on a website such as AsPredicted).
Deadlines and early access
Full-texts should be submitted before the 31st of December 2024 via the submission system. The Editorial Board will not consider extended abstracts. The authors will be informed within 10 working days whether their manuscript is sent to reviewers.
Given that large language models are a “hot” topic, all papers intended to be published in this issue will be available online immediately after being accepted, prior to the special issue being published.
Also, if there are too many excellent papers, they may be published in the following EBR issues.
Editor handling this thematic issue
Paweł Niszczota
References
Hansen, A. L., & Kazinnik, S. (2023). Can ChatGPT Decipher Fedspeak? (SSRN Scholarly Paper 4399406). https://doi.org/10.2139/ssrn.4399406
Horton, J. J. (2023). Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus? (Working Paper 31122). National Bureau of Economic Research. https://doi.org/10.3386/w31122
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., Jennings, N. R., Kamar, E., Kloumann, I. M., Larochelle, H., Lazer, D., McElreath, R., Mislove, A., Parkes, D. C., Pentland, A. ‘Sandy’, … Wellman, M. (2019). Machine behaviour. Nature, 568(7753), Article 7753. https://doi.org/10.1038/s41586-019-1138-y
