Alternative data vendors sell narrative. Your job is to prove incremental alpha after costs, latency, and coverage bias. The papers below cover due diligence frameworks, empirical evidence on alt data value, and how quickly signals decay once discovered.
Industry and survey literature
Alternative Data in Investment Management: Usage, Challenges and Valuation
Practitioner-oriented survey of alt data categories, vendor landscape, and integration into active equity workflows. Publisher page
Alternative Thinking 2020: Alternative Data
Skeptical institutional view on alt data hype, capacity, and what actually survives transaction costs.
Academic evidence on signal decay
Many alt data signals are repackaged price, volume, or known factors. These papers help you test whether a dataset adds incremental information.
Characteristics Are Covariances: A Unified Model of Risk and Return
Instrumented PCA framework. Useful baseline when testing if alt data features are just latent factor exposures. NBER working paper
Open Source Cross-Sectional Asset Pricing
Open-source replication of 319 cross-sectional signals. Run your alt data signal against this library before claiming novelty. GitHub repo
101 Formulaic Alphas
Shows how crowded simple signals are. If your alt data factor correlates with these, edge is likely small.
Due diligence checklist
- Survivorship in panel construction: are delisted names included?
- Timestamp: when was the data knowable to the market?
- Coverage bias: does the vendor only cover large, liquid names?
- All-in cost at your target universe size and rebalance frequency
- Null test: shuffled labels should destroy predictive power
For new datasets, cross-check against open anomaly data at Open Asset Pricing (Chen-Zimmermann) before committing budget.