Abstract
Stock Top Papers is an open-source Data Science project. Top paper collection for stock price prediction, quantitative trading. Covering top conferences and journals like KDD, WWW, CIKM, AAAI, IJCAI, ACL, EMNLP. The sole purpose of this repository is to help me organize recent academic papers with codes related to _stock price prediction_, _quantitative trading_, _risk modeling_. This is a non-exhausting list, even though I'll try to keep it updated... It is built using Deep Learning. The complete source code is publicly available on GitHub under the Apache License 2.0, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
The sole purpose of this repository is to help me organize recent academic papers with codes related to _stock price prediction_, _quantitative trading_, _risk modeling_. This is a non-exhausting list, even though I'll try to keep it updated... Feel free to suggest decent papers via a PR. If you find this repository helpful consider leaving a
2. Objective
Top paper collection for stock price prediction, quantitative trading. Covering top conferences and journals like KDD, WWW, CIKM, AAAI, IJCAI, ACL, EMNLP.
This project demonstrates how Deep Learning can be applied to a real-world Data Science problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- See the project README for exact requirements
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/marcuswang6/stock-top-papers.git
cd stock-top-papersFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Turn the analysis into an interactive dashboard
- Automate data refresh with a scheduled job
- Add a predictive model on top of the analysis
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- How could the analysis be automated or refreshed with new data?
9. Source Code & License
This project is developed by marcuswang6 and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.
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