Behavioral Finance Time Series from On-Chain and Off-Chain Data

Workshop data from ICCS 2018 uses genetic programming and signal processing to study time-varying exposures where trust is implied. The datasets are behavioral finance time series derived from on-chain data (e.g., fees) and off-chain data (e.g., clickstreams). The work elaborates on complexity metrics of causality through parametric network graphs and discusses social memory irreversibility.

Use Cases

• Modeling trust asymmetries based on behavioral finance time series mentioned in the description
• Analyzing causal complexity metrics based on parametric network graphs
• Studying systemic risk in cryptocurrency markets based on on-chain and off-chain data features

Strengths

• Data integrates multiple sources, including on-chain (e.g., fees) and off-chain (e.g., clickstreams) data.
• Description references specific analytical methods like genetic programming, vector fields, and signal processing.

Limitations

• Row count and file size are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Percy Venegas, Ministry of Economy

Collection Method

Likely gathered from blockchain networks and associated off-chain sources.