# skfolio.descriptor.ShortInterest ### *class* skfolio.descriptor.ShortInterest Short interest descriptor. Computes the ratio of shares sold short to common shares outstanding: $$ \text{short\_interest}(t) = \frac{\text{short\_interest}(t)} {\text{adj\_shares\_outstanding}(t)} $$ Short interest measures the fraction of common shares outstanding that have been borrowed and sold short. High values indicate stronger bearish positioning and may proxy for informed negative sentiment [[1]](#r3408170d7b7b-1) [[2]](#r3408170d7b7b-2). * **Parameters:** **None** * **Attributes:** **n_assets_** *int* : Number of assets seen during fitting. **asset_names_** *ndarray of shape (n_assets,)* : Asset names seen during fitting. ### Methods | [`fit_transform`](#skfolio.descriptor.ShortInterest.fit_transform)(X[, y]) | Compute short interest. | |--------------------------------------------------------------------------------|------------------------------------------------| | [`get_metadata_routing`](#skfolio.descriptor.ShortInterest.get_metadata_routing)() | Get metadata routing of this object. | | [`get_params`](#skfolio.descriptor.ShortInterest.get_params)([deep]) | Get parameters for this estimator. | | [`partial_fit_transform`](#skfolio.descriptor.ShortInterest.partial_fit_transform)(X[, y]) | Stateless class delegation to `fit_transform`. | | [`set_params`](#skfolio.descriptor.ShortInterest.set_params)(\*\*params) | Set the parameters of this estimator. | #### SEE ALSO [`DaysToCover`](https://skfolio.org/generated/skfolio.descriptor.DaysToCover.html.md#skfolio.descriptor.DaysToCover) : EWMA-smoothed days to cover (short interest / volume). ### Notes `short_interest` is the number of shares held short. Non-missing values must be finite and non-negative. `adj_shares_outstanding` is common shares outstanding. Non-missing values must be finite and strictly positive. Both fields must use the same split-adjustment basis. ### References * **[1]** “An investigation of the informational role of short interest in the Nasdaq market” The Journal of Finance. Desai, H., Ramesh, K., Thiagarajan, S. R., & Balachandran, B. V. (2002). * **[2]** “Short interest and aggregate stock returns” Journal of Financial Economics. Rapach, D. E., Ringgenberg, M. C., & Zhou, G. (2016). ### Examples ```pycon >>> from skfolio.datasets import make_synthetic_characteristics >>> from skfolio.descriptor import ShortInterest >>> >>> X = make_synthetic_characteristics() >>> >>> descriptor = ShortInterest() >>> short_interest = descriptor.fit_transform(X) ``` #### fit_transform(X, y=None, \*\*fit_params) Compute short interest. * **Parameters:** **X** *AssetPanel* : Input panel containing `short_interest` and `adj_shares_outstanding`. **y** *None* : Ignored. Present for compatibility with scikit-learn’s API. **\*\*fit_params** *dict* : Additional fit parameters. Ignored. * **Returns:** **short_interest** *ndarray of shape (n_observations, n_assets)* : Short interest divided by adjusted shares outstanding for each observation and asset. #### get_metadata_routing() Get metadata routing of this object. Please check [User Guide](https://skfolio.org/user_guide/metadata_routing.html.md#metadata-routing) on how the routing mechanism works. * **Returns:** **routing** *MetadataRequest* : A `MetadataRequest` encapsulating routing information. #### get_params(deep=True) Get parameters for this estimator. * **Parameters:** **deep** *bool, default=True* : If True, will return the parameters for this estimator and contained subobjects that are estimators. * **Returns:** **params** *dict* : Parameter names mapped to their values. #### partial_fit_transform(X, y=None, \*\*fit_params) Stateless class delegation to `fit_transform`. #### set_params(\*\*params) Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `__` so that it’s possible to update each component of a nested object. * **Parameters:** **\*\*params** *dict* : Estimator parameters. * **Returns:** **self** *estimator instance* : Estimator instance.