# Risk profile

`kaal:entity:risk-profile`

**Status.** derived

This node is assembled mechanically from the 3 claims that carry the concept tag `risk-profile`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

3 claims across 2 works, 2016 to 2019.

**2016**

- [2811729-026](https://wulfkaal.github.io/claims/2811729-026) [mechanism/argued] -- Because unconstrained mutual funds share investment strategy and risk attributes with private funds, the average unconstrained fund's risk profile is substantially more complex and generally involves more risk than the average mutual fund, and is closer to that of a private fund.
  > Because of these shared investment strategy and risk attributes, the average UMF's risk profile is substantially more complex, and generally involves more risks, than the average mutual fund, and is rather more similar to that of a private fund.
  Wulf A. Kaal, Unconstrained Mutual Funds and Retail Investor Protection (2016). SSRN: https://ssrn.com/abstract=2811729
- [2811729-028](https://wulfkaal.github.io/claims/2811729-028) [mechanism/argued] -- Existing evidence about risk-shifting by the average derivative-using mutual fund is less relevant to unconstrained mutual funds, because their derivative use is closer to that of a typical private fund.
  > show that derivative use by UMFs is closer to that of a typical private fund, which may mean that the absence of evidence on risk-shifting by the average mutual fund that engages in derivatives transactions is less relevant.
  Wulf A. Kaal, Unconstrained Mutual Funds and Retail Investor Protection (2016). SSRN: https://ssrn.com/abstract=2811729

**2019**

- [3409548-007](https://wulfkaal.github.io/claims/3409548-007) [mechanism/asserted] -- Machine learning improves portfolio diversification by searching for instruments that are uncorrelated with each other and that still match the requirements of the target risk profile.
  > ML can help in accurate portfolio diversification by looking for uncorrelated instruments that match requirements of the risk profile.
  Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548

## Verify

Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust.

    curl -s https://wulfkaal.github.io/entities/risk-profile.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.