Market Data
Understanding the characteristics, limitations, and proper handling of financial market data is fundamental to any quantitative research. This area focuses on data sourcing, cleaning, normalization, and quality assessment.
Why It Matters
The quality and integrity of market data directly impacts the reliability of any research built upon it. Understanding data limitations, biases, and proper preprocessing is essential for producing trustworthy results.
Research Questions
- How does data quality affect backtesting reliability?
- What normalization methods preserve the most useful information?
- How should missing data be handled in different research contexts?
- What are the implications of survivorship bias in historical datasets?
Methods
Experiments
Ongoing research comparing different data normalization approaches and their impact on downstream model performance. Also investigating automated data quality assessment pipelines.
Validation
Data quality is assessed through statistical profiling, cross-source validation, and automated anomaly detection. All datasets are documented with known limitations and potential biases.