Search Results for author: Sandra Paterlini

Found 6 papers, 0 papers with code

Stock Price Prediction Using Temporal Graph Model with Value Chain Data

no code implementations7 Mar 2023 Chang Liu, Sandra Paterlini

Stock price prediction is a crucial element in financial trading as it allows traders to make informed decisions about buying, selling, and holding stocks.

Stock Price Prediction

A generalized precision matrix for t-Student distributions in portfolio optimization

no code implementations25 Mar 2022 Karoline Bax, Emanuele Taufer, Sandra Paterlini

In particular, when focusing on the minimum-variance portfolio, the covariance matrix or better its inverse, the so-called precision matrix, is the only input required.

Portfolio Optimization

Environmental, Social, Governance scores and the Missing pillar -- Why does missing information matter?

no code implementations29 Jun 2021 Özge Sahin, Karoline Bax, Claudia Czado, Sandra Paterlini

Environmental, Social, and Governance (ESG) scores measure companies' performance concerning sustainability and societal impact and are organized on three pillars: Environmental (E), Social (S), and Governance (G).

ESG, Risk, and (Tail) Dependence

no code implementations15 May 2021 Karoline Bax, Özge Sahin, Claudia Czado, Sandra Paterlini

While environmental, social, and governance (ESG) trading activity has been a distinctive feature of financial markets, the debate if ESG scores can also convey information regarding a company's riskiness remains open.

New estimation approaches for graphical models with elastic net penalty

no code implementations1 Feb 2021 Davide Bernardini, Sandra Paterlini, Emanuele Taufer

Finally, the third estimator relies on a 2-stages procedure that estimates the edge set first and then the precision matrix elements.

Methodology

Sparse Portfolio Selection via the sorted $\ell_{1}$-Norm

no code implementations6 Oct 2017 Philipp J. Kremer, Sangkyun Lee, Malgorzata Bogdan, Sandra Paterlini

We introduce a financial portfolio optimization framework that allows us to automatically select the relevant assets and estimate their weights by relying on a sorted $\ell_1$-Norm penalization, henceforth SLOPE.

Portfolio Optimization

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