Advanced Risk Analysis

ETF Risk Analysis Setup

Configure the analysis parameters to estimate risk and potential price movements for an ETF.

ETF Ticker Symbol:

Historical Data Period: ⓘ

Data Interval: ⓘ

Lookback Days (Features): ⓘ

Prediction Horizon (Days): ⓘ

Test Data Size (%): ⓘ

Compare Multiple ETFs:

Enable Comparison Mode

Find ETF Recommendations:

Recommend ETFs based on risk profile

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Risk Analysis Results

Next Period Prediction (Horizon: )

Current Price

Predicted Price

95% Prediction Interval

68% Prediction Interval

Risk Assessment (Test Period)

Risk Level

Avg Uncertainty

Worst Case Return

Potential Upside

ETF Risk Comparison

ETF	Avg Risk	Worst Case	Normal VaR (95%)	Conformal VaR	Risk Level

Comparison with Value at Risk (VaR) - Test Period

Normal VaR (95%)

Historical VaR (95%)

Actual Worst Return

Conformal Worst Pred.

Why Conformal Prediction Matters

Traditional Value at Risk (VaR) methods often assume returns follow a normal distribution, which can underestimate risk during extreme market events ("fat tails"). Conformal Prediction provides uncertainty estimates without relying on these strong assumptions, potentially offering more reliable bounds, especially during volatile periods.

The comparison shows how traditional VaR might differ from the actual worst outcome and the lower bound suggested by the conformal prediction model on the test data.

Price Prediction with Confidence Intervals (Test Set)

Normalized Risk Over Time (Test Set)

Seasonality Analysis

Based on average historical monthly performance.

Best Month

Worst Month

Seasonality Score

The Seasonality Score reflects the typical range between the best and worst months. Higher scores suggest stronger historical seasonal patterns (though past patterns may not repeat).

About Conformal Prediction for Risk Assessment

Conformal Prediction is a technique that generates prediction intervals (a range where a future value is likely to fall) with mathematically guaranteed coverage rates, under mild assumptions. Unlike methods relying on specific distributions (like Normal for VaR), it adapts to the observed data patterns.

Key Advantages Over Traditional VaR

Distribution-Free: Doesn't assume data follows a specific distribution (e.g., Normal).
Valid Coverage: Provides intervals (e.g., 95%) that are guaranteed to contain the true value 95% of the time on average, assuming data exchangeability.
Adaptive: Intervals naturally widen during periods of higher uncertainty or volatility observed in the data.
Model Agnostic: Can wrap around various underlying prediction models (like Linear Regression used here).

Potential Applications

Understanding prediction intervals can inform portfolio management:

Risk Sizing: Wider intervals relative to price suggest higher uncertainty/risk.
Option Pricing Context: Provides a data-driven range for potential price movements.
Scenario Analysis: The lower/upper bounds provide plausible best/worst-case scenarios based on the model and historical data.

Important Disclaimer

Past performance is not indicative of future results. Conformal prediction provides intervals based on past data and model assumptions; it does not guarantee future outcomes. Financial markets are complex and subject to unforeseen events.

All investments involve risk, including the possible loss of principal. This tool is for educational and informational purposes only and is NOT investment advice. Consult a qualified financial advisor before making investment decisions.

Advanced Risk Analysis with Conformal Prediction