etf-analysis

To compare ETFs tracking the same index, evaluate tracking error, fee drag, liquidity, and premium/discount behavior. This ETF analysis methodology applies a quant decision model to score and rank products based on metrics like scale, fees, and daily trading volume for your intended holding period.

Assessing ETF liquidity risk and premium/discount behavior in China's market involves monitoring daily trading volume, bid-ask spreads, and structural mechanics specific to A-share and QDII ETFs. This framework calculates these liquidity indicators to identify implementation risks and approximate fund flows for practical arbitrage checks.

Calculate ETF tracking error using Tushare data templates by retrieving ETF list, NAV, daily, and index data. These code templates compute tracking error metrics and monitor premium/discount rates to provide decision-grade comparisons across different ETF types and index families.

Yes, you can use ETF analysis for core-satellite portfolio construction and factor exposure assessment. The framework provides quant decision models that support portfolio construction, rotation screening, and factor exposure analysis to align ETF selection with your specific investment goals and risk tolerance.

Yes, this ETF comparison framework accounts for QDII-specific risks including FX limits and operational constraints. It addresses how A-share ETF mechanics and QDII-specific structural risks affect implementation expectations, ensuring your ETF selection avoids mismatched use cases across different underlying assets.

Avoid mismatched ETF structures by classifying underlying assets and structure types including plain, LOF, 联接基金, and leveraged/inverse ETFs. Understanding these structural differences prevents inappropriate use cases and ensures your ETF selection aligns with your specific portfolio construction and risk management requirements.