SuperJayLiu

SuperJayLiu / AI-for-Economics-and-Finance-Research

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An "all-you-need-to-know" practical guide to using AI and LLMs responsibly and effectively for economics and finance research: from core skills and ChatGPT/Claude Projects to coding, writing, reproducibility, and automation. 小红书账号：SuperJay

www.chaojieliu.com academic-presentation academic-project academic-research academic-writing ai
10
7
89% credibility
Found May 28, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

This GitHub repository is a practical handbook and resource library designed specifically for economics and finance researchers who want to use AI tools responsibly in their academic work. Rather than collecting random prompts or ranking AI tools, it provides a structured workflow that guides researchers from learning core concepts, through copying ready-to-use skills for specific tasks (literature review, empirical methods, data cleaning, coding, writing, and presentations), to setting up safe research environments with version control and clear data rules. The repository emphasizes verification at every step, warns against common AI mistakes like fake citations and overclaimed causality, and includes both English and Chinese versions. It is maintained by an academic researcher at the University of Bristol and is linked to a working paper on SSRN.

How It Works

1
📚 You hear about a practical AI guide for economics research

A colleague mentions a GitHub handbook that helps researchers use AI responsibly for academic work, with ready-to-use templates and clear safety rules.

2
🔍 You explore the handbook and find your starting point

You open the handbook and discover it speaks directly to your situation—whether you're a PhD student, research assistant, or professor—and points you to exactly where to begin.

3
📋 You copy a skill that matches your research task

You pick one copy-ready instruction—maybe for literature review, methods writing, or data cleaning—and paste it into your AI assistant with your project details.

4
✅ You verify the AI output before using it

You check every citation, code change, and claim against your sources, data, and design—because AI output is helpful but not evidence on its own.

5
Your path branches depending on how deeply you want to use AI
🛑
Light use: AI helps with writing and coding

You use AI for literature summaries, code explanations, and slide preparation while keeping your materials private and your judgment central.

🔒
Deep use: set up a safe research project

You create a private project folder with clear rules about what AI can and cannot touch, keeping your data protected and your work traceable.

6
📝 You record what AI helped with and what you checked

You write a brief note in your project log about what AI produced, what you verified, and what remains uncertain—building a clear trail of your research decisions.

🎉 Your research moves forward responsibly

You have a clearer paper, verified code, and a documented workflow that you and your coauthors can trust and reproduce—without cutting corners on academic integrity.

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AI-Generated Review

What is AI-for-Economics-and-Finance-Research?

This is a practical handbook for economists and finance researchers who want to use AI tools responsibly in their work. It is not a prompt collection or tool ranking. Instead, it provides copy-ready skills, templates, and workflows that guide researchers through the entire research pipeline: from literature review and empirical methods to coding, writing, verification, and presentation. The repository is bilingual, offering both English and Chinese versions, and includes a downloadable skill pack that can be imported directly into ChatGPT Projects, Claude Projects, Codex, or Claude Code. It emphasizes a core principle: AI can automate labor but cannot replace scholarly responsibility.

Why is it gaining traction?

The hook here is specificity. Most AI guides for academics are generic. This one is built for economics and finance workflows, covering domain-specific concerns like CRSP/Compustat merges, staggered difference-in-differences, look-ahead bias, factor-mining risk, and journal disclosure requirements. The repository also stands out for its emphasis on verification and safety. Rather than just showing what AI can do, it systematically teaches researchers what to check, how to log AI use, and when to refuse AI output entirely. The "one paper, one repo, one AI project" rule and the structured approval gates for agent workflows address real concerns that arise when AI starts editing research files.

Who should use this?

PhD students in economics or finance who are new to using AI in research will find the clearest benefit. Research assistants setting up their first empirical project will appreciate the step-by-step setup guides for Git, GitHub, and coding agents. Junior faculty revising papers or preparing referee responses can use the writing and review skills directly. Finance researchers working with WRDS, CRSP, or Compustat data will find the data pipeline and merge templates particularly useful. Instructors teaching AI research methods or preparing workshops can adapt the presentation materials and exercises.

Verdict

This is a well-structured, thoughtful resource that fills a real gap for economics and finance researchers navigating AI adoption. The bilingual coverage, verification-first philosophy, and domain-specific skill templates make it genuinely useful for serious academic work. However, the credibility score of 0.9% and low star count reflect a new, relatively untested repository with limited community validation. Use it as a practical supplement to your workflow, verify claims against official documentation, and apply the safety rules before letting AI touch sensitive research materials.

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