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A Fidelity approach to providing advanced risk analytics for investors
New technology and innovative analytical techniques may help investors deepen their understanding of investment risk management as part of portfolio construction processes.
Key Takeaways
- Supported by advances in data science, machine learning, and computing power, advances in investment risk modeling have the potential to better inform and strengthen portfolio construction.
- New approaches may help address existing complexities in modeling investment risk-return tradeoffs that result in underappreciation of portfolio loss sensitivity. These include the use of point estimates as estimations for model inputs and outputs, overreliance on the assumption of normal return distributions, and methodology premised on constant asset correlations through time.
- Fidelity believes that evolving and innovative methods of risk analysis—together with intuitive tools to use them—should be available to a wide range of investors for direct investment applications.
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A Fidelity Approach to Providing Advanced Risk Analytics for Investors
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