rohanrao/nifty50-stock-market-data: papers linking to it

Kaggle · www.kaggle.com/datasets/rohanrao/nifty50-stock-market-data

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to rohanrao/nifty50-stock-market-data (https://www.kaggle.com/datasets/rohanrao/nifty50-stock-market-data), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

4 papers in the tracked journals link to the Kaggle dataset rohanrao/nifty50-stock-market-data in their Crossref records, the first in 2021; most of them (2) in 2024.
By category: AI (3) and Economics (1); the journals with the most are Computational Economics (1), Knowledge and Information Systems (1) and Journal Of Big Data (1).
2 of the papers build on one another: a later paper cites an earlier one that also links rohanrao/nifty50-stock-market-data.

By year#

20212026

Papers linking to rohanrao/nifty50-stock-market-data by publication year
Year	Papers
2021	1
2024	2
2026	1

Journals#

Journals whose papers link to rohanrao/nifty50-stock-market-data most
Journal	Papers
Computational Economics	1
Knowledge and Information Systems	1
Journal Of Big Data	1
Knowledge-Based Systems	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

LSTM based decision support system for swing trading in stock market Knowledge-Based Systems, 2021
- Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions Journal Of Big Data, 2024 — cites the paper above

Papers linking to rohanrao/nifty50-stock-market-data, most cited first
Published	Title	Journal	Authors	Citations	Link
2021-12-23	LSTM based decision support system for swing trading in stock market	Knowledge-Based Systems	Shouvik Banik, Nonita Sharma, Monika Mangla et al.	120	cites it
2024-10-29	Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions	Journal Of Big Data	Jayaraman Kumarappan, R Elakkiya, V. Subramaniyaswamy et al.	31	cites it
2024-12-10	GA-LSTM: Performance Optimization of LSTM driven Time Series Forecasting	Computational Economics	Uphar Singh, Kumar Saurabh, Neelaksh Trehan et al.	23	cites it
2026-05-18	Stock market price prediction with efficient generative artificial intelligence with predictor network	Knowledge and Information Systems	Adusumalli Balaji, Siddabathuni Suresh Babu, Hari Krishna Deevi et al.	0	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). rohanrao/nifty50-stock-market-data: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-rohanrao-nifty50-stock-market-data

@misc{scholar4ai-code-kaggle-rohanrao-nifty50-stock-market-data,
  author = {{Scholar4AI}},
  title = {rohanrao/nifty50-stock-market-data: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-rohanrao-nifty50-stock-market-data}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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