To understand a predictive factors predictive power, we create a simple long/short strategy and simulate its past performance (with daily rebalancing):
The strategy is rebalanced daily, on a continuous basis. There are 0.5% transaction costs applied on each position adjustment.
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Predictive Strength
Measures the net flow of capital into ETFs.
ETF flows act as a real-time gauge of institutional confidence. Sustained inflows signal bullish positioning by large investors (e.g., BlackRock, Fidelity), while outflows often precede bearish trends.
Each $1 of ETF inflow injects liquidity into the market, reducing bid-ask spreads and slippage. Analysis shows that ETF-driven liquidity enables smoother large transactions (e.g., 10,000 BTC trades) with minimal price impact. Conversely, outflows tighten liquidity, exacerbating volatility during sell-offs.
ETF flows create self-reinforcing cycles. Inflows attract more capital by validating bullish narratives, while outflows can spark deleveraging cascades.
ETF Net Flow data is sourced by tracking daily/monthly changes in shares outstanding and net asset value (NAV), adjusting for price movements to isolate pure capital inflows/outflows. This calculation typically involves comparing the current period's AUM changes against historical averages to create a normalized index. The final metric reflects relative capital movement intensity rather than absolute dollar values.
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Get started by validating the historical performance of the strategy with our transparent code snippets.
Copy and paste the code snippets below into your Python environment or download the files below.
Predictive Strength
Predictive Strength