<a id="skfolio-datasets-make-synthetic-characteristics"></a>

# skfolio.datasets.make_synthetic_characteristics

<a id="skfolio.datasets.make_synthetic_characteristics"></a>

### skfolio.datasets.make_synthetic_characteristics(n_assets=500, n_observations=2520, \*, n_industries=10, start_date='2015-01-01', systematic_variance_ratio=0.5, late_listing_proba=0.15, delisting_proba=0.15, missing_ratio=0.01, random_state=None)

Generate a synthetic characteristics [`AssetPanel`](https://skfolio.org/generated/skfolio.containers.AssetPanel.html.md#skfolio.containers.AssetPanel).

The panel generated contains the minimal set of fields required by the default
`skfolio` descriptors and [`CharacteristicsFactorModel`](https://skfolio.org/generated/skfolio.prior.CharacteristicsFactorModel.html.md#skfolio.prior.CharacteristicsFactorModel). It is
designed so that fitting a characteristics factor model produces realistic
diagnostics: a cross-sectional regression $R^2$ away from the degenerate
values of $0$ and $1$, non-trivial information coefficients and
idiosyncratic returns with fat tails.

Returns are drawn from a factor structure

$$
r_{i,t} = \beta_i\,f^{\mathrm{mkt}}_t + f^{\mathrm{ind}(i)}_t
          + \sum_k B_{i,k}\,f^{k}_t + \varepsilon_{i,t},
$$

where the per-asset loadings $B$ are persistent traits. Characteristics are
then constructed so that the descriptor of each style is a noisy proxy of the
corresponding loading, while accounting identities (for example
$\text{market\_cap} = \text{adj\_close} \times
\text{adj\_shares\_outstanding}$) are preserved.

Fields produced:
`returns`, `adj_close`, `adj_volume`, `adj_shares_outstanding`, `market_cap`,
`ebitda_ttm`, `enterprise_value`, `net_income_ttm`, `sales_ttm`, `dividends_ttm`,
`net_buybacks_ttm`, `book_equity`, `operating_cash_flow_ttm`, `total_debt`,
`total_assets`, `industry`, `cost_of_revenue_ttm`, `capex_ttm`, `short_interest`,
`eps_ntm`, `dps_ntm`, `eps_ntm_std`.

* **Parameters:**
  **n_assets** *int, default=500*
  : Number of assets (coverage universe).

  **n_observations** *int, default=2520*
  : Number of observations.

  **n_industries** *int, default=10*
  : Number of industry groups. Must not exceed 16.

  **start_date** *str, default=”2015-01-01”*
  : First observation date. Observations follow a business-day calendar.

  **systematic_variance_ratio** *float, default=0.5*
  : Share of cross-sectional return variance explained by the factor structure.
    The realized cross-sectional regression $R^2$ of a fitted model is close
    to this value. Must lie in the open interval $(0, 1)$.

  **late_listing_proba** *float, default=0.15*
  : Probability that an asset lists after the first observation.

  **delisting_proba** *float, default=0.15*
  : Probability that an asset delists before the last observation.

  **missing_ratio** *float, default=0.01*
  : Fraction of active fundamental observations set to NaN to emulate reporting
    gaps. Price, volume, shares and market cap are left intact.

  **random_state** *int, optional*
  : Seed for the random number generator.
* **Returns:**
  **panel** *AssetPanel*
  : Synthetic asset panel with the fields listed above. `industry` is a
    [`FieldCategorical`](https://skfolio.org/generated/skfolio.containers.FieldCategorical.html.md#skfolio.containers.FieldCategorical).

### Notes

The generator is driven by a small set of time-invariant latent asset traits
(size, value, quality, risk, growth and liquidity). These traits set the factor
loadings $B$, the market beta and the idiosyncratic volatility level and
anchor the level of every fundamental and market field so that accounting
identities (such as $\text{market\_cap} = \text{adj\_close} \times
\text{adj\_shares\_outstanding}$) hold. Each characteristic is therefore a noisy
proxy of the trait that drives its matching style factor.

Factor returns combine a fat-tailed market factor, zero-mean industry factors and
mean-reverting style factors. Idiosyncratic returns mix a transitory shock with a
slow persistent component and a fast mean-reverting component. These give the
momentum and short-term reversal factors a realistic positive Sharpe without
changing the idiosyncratic variance.

To support alpha-research examples, the idiosyncratic shock also contains a small
predictable component driven by a persistent latent bearish signal
$z_{i,t}$. Short interest and analyst forecast dispersion are constructed as
noisy increasing functions of $z_{i,t}$, while the next-period return
contribution is

$$
\varepsilon^{\mathrm{signal}}_{i,t+1}
= -\sigma_i\sqrt{w_{\mathrm{signal}}}\,z_{i,t}.
$$

Consequently, high values of either descriptor predict lower future
idiosyncratic returns without same-period look-ahead.

Forward-looking and lower-coverage fields (`eps_ntm`, `dps_ntm`, `eps_ntm_std`,
`enterprise_value`, `ebitda_ttm` and `cost_of_revenue_ttm`) carry partial coverage
to mirror real data, while price, volume, shares and market cap are always
populated on active assets.

### Examples

```pycon
>>> from skfolio.datasets import make_synthetic_characteristics
>>> panel = make_synthetic_characteristics(n_assets=200, n_observations=1000)
>>> panel.n_assets, panel.n_observations
(200, 1000)
```