asset.label

datasets

Estimation of the most-left informative set of gross returns
(i.e., the informative set).
The procedure to compute the informative set adjusts the method
proposed by
Mariani et al. (2022a) <doi:10.1007/s11205-020-02440-6>
and
Mariani et al. (2022b) <doi:10.1007/s10287-022-00422-2>
to gross returns of financial assets.
This is accomplished through an adaptive algorithm
that identifies sub-groups of gross returns in
each iteration by approximating their distribution with a
sequence of two-component log-normal mixtures.
These sub-groups emerge when a significant change
in the distribution occurs below the median of the
financial returns, with their boundary termed as
the “change point" of the mixture.
The process concludes when no further change points are detected.
The outcome encompasses parameters of the leftmost mixture
distributions and change points of the
analyzed financial time series.
The functionalities of the INFOSET package include: (i) modelling asset distribution
detecting the parameters which describe left tail behaviour (infoset function), (ii) clustering, (iii) labeling of the financial
series for predictive and classification purposes through a Left Risk measure based on the first change point (LR_cp function)
(iv) portfolio construction (ptf_construction function).
The package also provide a specific function to construct rolling windows of different length size and overlapping time.

Gloria Polinesi

INFOSET

Computing a New Informative Distribution Set of Asset Returns

Francesca Mariani

Maria Cristina Recchioni

asset.label function

A data frame with 44 observations (rows) on 3 variables (columns)<dl>
<dt>id</dt>
<dd>name of ETF</dd><dt>label</dt>
<dd>from 1 to 5 according to the specific asset class</dd><dt>class</dt>
<dd>specific asset class (5 categories)</dd>
</dl>

Format

Data for clustering and labeling ETFs — asset.label

A data frame with 44 observations (rows) on 3 variables (columns)<dl>
<dt>id</dt>
<dd>name of ETF</dd>

<dt>label</dt>
<dd>from 1 to 5 according to the specific asset class</dd>

<dt>class</dt>
<dd>specific asset class (5 categories)</dd>


</dl>

asset.label: Data for clustering and labeling ETFs

Description

Usage

Arguments

Format

Examples