Quantitative Frameworks for Scalable Data Modeling and Alpha Extraction

Quantitative Frameworks for Scalable Data Modeling and Alpha Extraction

Maiden Century & Consumer Edge

In this collaborative ebook, Maiden Century and Consumer Edge demonstrate how quant funds can move beyond legacy market data workflows to extract meaningful alpha from alternative data. Using Maiden Century's QTIP platform and Consumer Edge's transaction-based datasets, the paper walks through the end-to-end process of converting raw alternative data into backtestable, quant-ready signals—delivering 19–25% annualized market-neutral returns with Sharpe ratios of 2–3x across three distinct strategy profiles.

• Quant-Specific Challenges: The paper addresses the core friction points quants face with alternative data—point-in-time integrity, entity mapping, survivorship bias, accuracy variance, and the disconnect between revenue signals and stock price.
• QTIP Pipeline: Maiden Century's QTIP product handles ingestion, entity resolution, corporate actions, metric selection, fiscal period alignment, and feature engineering—scaling the discretionary research process across hundreds of tickers.
• Proven Performance: Three Consumer Edge profiles (US Daily Signal, Vela & Orion, CEI Card) have generated 19–25% annualized market-neutral returns since 2019–2021, with consistent Sharpe ratios of 2–3x.
• Synthetic Point-in-Time (SPIT): For newer datasets with limited history, Maiden Century's SPIT methodology reconstructs backtestable signals, opening up evaluation of datasets before they become widely deployed and discounted.
• Customizable MRD Files: Machine-readable data tables—including Historical Spread, Periodic Index, Forecast Summary, Metric Relevance, and Napkin Math—enable quants to build proprietary signals using Maiden Century's inputs as a foundation.

Download the whitepaper to learn how QTIP and Consumer Edge data can accelerate your alternative data evaluation and unlock scalable alpha.