# FinML-Chain: A Blockchain-Integrated Dataset for Enhanced Financial Machine Learning

arXiv: https://arxiv.org/abs/2411.16277 · HTML: https://mlsanity.com/paper/2411.16277/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 19 of 19)

- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Neural Additive Models: Interpretable Machine Learning with Neural Nets](https://mlsanity.com/paper/2004.13912/) (2020)
- [Croissant: A Metadata Format for ML-Ready Datasets](https://mlsanity.com/paper/2403.19546/) (2024)
- [Certified Monotonic Neural Networks](https://mlsanity.com/paper/2011.10219/) (2020)
- [Empirical Analysis of EIP-1559: Transaction Fees, Waiting Time, and Consensus Security](https://mlsanity.com/paper/2201.05574/) (2022)
- [KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments](https://mlsanity.com/paper/2003.04967/) (2020)
- [EX-Graph: A Pioneering Dataset Bridging Ethereum and X](https://mlsanity.com/paper/2310.01015/) (2023)
- [Decoding Social Sentiment in DAO: A Comparative Analysis of Blockchain Governance Communities](https://mlsanity.com/paper/2311.14676/) (2023)
- [A Dataset of Uniswap daily transaction indices by network](https://mlsanity.com/paper/2312.02660/) (2023)
- [How to address monotonicity for model risk management?](https://mlsanity.com/paper/2305.00799/) (2023)
- [Analyzing Reward Dynamics and Decentralization in Ethereum 2.0: An Advanced Data Engineering Workflow and Comprehensive Datasets for Proof-of-Stake Incentives](https://mlsanity.com/paper/2402.11170/) (2024)
- [Foundations of Transaction Fee Mechanism Design](https://mlsanity.com/paper/2111.03151/) (2021)
- [Blockchains: a Systematic Multivocal Literature Review](https://mlsanity.com/paper/1911.11770/) (2019)
- [Dynamic Posted-Price Mechanisms for the Blockchain Transaction Fee Market](https://mlsanity.com/paper/2103.14144/) (2021)
- [Monitoring Data Requests in Decentralized Data Storage Systems: A Case Study of IPFS](https://mlsanity.com/paper/2104.09202/) (2021)
- [Distributed Runtime Verification of Metric Temporal Properties for Cross-Chain Protocols](https://mlsanity.com/paper/2204.09796/) (2022)
- [A Graph Diffusion Scheme for Decentralized Content Search based on Personalized PageRank](https://mlsanity.com/paper/2204.12902/) (2022)
- [Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods](https://mlsanity.com/paper/2305.08105/) (2023)
- [Global Public Sentiment on Decentralized Finance: A Spatiotemporal Analysis of Geo-tagged Tweets from 150 Countries](https://mlsanity.com/paper/2409.00843/) (2024)

## Built on by (most cited first, 1 of 1)

- [Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols](https://mlsanity.com/paper/2604.20374/) (2026)

## Abstract (arXiv)

Machine learning is critical for innovation and efficiency in financial markets, offering predictive models and data-driven decision-making. However, challenges such as missing data, lack of transparency, untimely updates, insecurity, and incompatible data sources limit its effectiveness. Blockchain technology, with its transparency, immutability, and real-time updates, addresses these challenges. We present a framework for integrating high-frequency on-chain data with low-frequency off-chain data, providing a benchmark for addressing novel research questions in economic mechanism design. This framework generates modular, extensible datasets for analyzing economic mechanisms such as the Transaction Fee Mechanism, enabling multi-modal insights and fairness-driven evaluations. Using four machine learning techniques, including linear regression, deep neural networks, XGBoost, and LSTM models, we demonstrate the framework's ability to produce datasets that advance financial research and improve understanding of blockchain-driven systems. Our contributions include: (1) proposing a research scenario for the Transaction Fee Mechanism and demonstrating how the framework addresses previously unexplored questions in economic mechanism design; (2) providing a benchmark for financial machine learning by open-sourcing a sample dataset generated by the framework and the code for the pipeline, enabling continuous dataset expansion; and (3) promoting reproducibility, transparency, and collaboration by fully open-sourcing the framework and its outputs. This initiative supports researchers in extending our work and developing innovative financial machine-learning models, fostering advancements at the intersection of machine learning, blockchain, and economics.