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

Paper: arXiv 2411.16277

Abstract

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.

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 4.0/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper is conceptually focused on proposing a novel framework for integrating blockchain data with financial machine learning, using relatively standard ML models with minimal advanced mathematical derivations. Empirical rigor is low as it presents a benchmark dataset and pipeline rather than a fully backtested, implementation-heavy trading strategy.

Research Flowchart

  flowchart TD
  A["Research Goal: Integrate Blockchain Data with ML<br>for Financial Mechanism Design"] --> B["Key Methodology: Hybrid Data Pipeline<br>On-Chain & Off-Chain Integration"]
  B --> C["Data/Inputs: High-Frequency On-Chain<br>Transaction Fee Data"]
  C --> D["Computational Process: Multi-Model Analysis<br>Linear Reg, DNN, XGBoost, LSTM"]
  D --> E["Key Findings/Outcomes:<br>1. Validated Research Scenario for TFM<br>2. Open-Source Benchmark Dataset & Pipeline<br>3. Framework for Reproducible Fintech Research"]

Cited References (70)

1. Article Title: A Good Academic Article Title (2001)
2. Great Paper Title: You Can Tell from the Colon Use (2015)
3. Book Title (2016)
4. Whitepaper On Blockchain Idea (2016)
5. Bitcoin: A Peer-to-Peer Electronic Cash System (2008)
6. Efficient Processing of Deep Neural Networks: A Tutorial and Survey (2017) — link
7. Xgboost: A scalable tree boosting system (2016)
8. Long short-term memory (1997)

0 of 70 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.