AI & Quantitative Research
Mathematical models and data-driven techniques support disciplined investment research and portfolio decisions.
China ETF · Client View
AINumeric | Quantitative Investment Solutions
AI-Driven China Investment Strategy
Systematic Strategies. Disciplined Risk Management.
We develop quantitative investment strategies combining mathematical modeling, algorithmic portfolio construction, and data-driven market research to support institutional investment decisions. Our research framework draws on machine learning techniques, systematic trading rules, and dynamic portfolio allocation, with an emphasis on transparent evaluation and risk-aware implementation.
Systematic Portfolio Management
Repeatable processes for asset selection, portfolio allocation, and rebalancing.
Risk-Aware Strategy Design
Research that considers diversification, downside exposure, and drawdown management.
Performance Transparency
Historical results, benchmark comparisons, drawdowns, and portfolio exposures for institutional review.
Our Investment Philosophy
We aim to balance return potential, drawdown control, and consistency through systematic research and risk-aware portfolio design.
These are research objectives, not guarantees of performance. Each published strategy should be evaluated using its own reported results and assumptions.
Reporting period: —
Transaction cost is set at 5 bps, reflecting the high liquidity of the selected ETFs.
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Portfolio Growth vs. CSI 500 (中证500)
Portfolio Drawdown vs. CSI 500 (中证500)
Latest Published Portfolio Allocations
Historical simulated holdings from the final backtest date.
Annual Performance
| Year | Strategy | CSI 500 | Active |
|---|
Portfolio Weight Changes
Sector Weight Changes
Historical portfolio allocations grouped by ETF category (主题、指数、跨境、板块等), including Cash.
Historical simulated results are not actual trading results. Returns may exclude slippage, market impact, taxes and other costs. Past performance does not guarantee future returns.