English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We propose a new approach to portfolio optimization that utilizes a uniquecombination of synthetic data generation and a CVaR-constraint. We formulatethe portfolio optimization problem as an asset allocation problem in which eachasset class is accessed through a passive (index) fund. The asset-class weightsare determined by solving an optimization problem which includes aCVaR-constraint. The optimization is carried out by means of a Modified CTGANalgorithm which incorporates features (contextual information) and is used togenerate synthetic return scenarios, which, in turn, are fed into theoptimization engine. For contextual information we rely on several points alongthe U.S. Treasury yield curve. The merits of this approach are demonstratedwith an example based on ten asset classes (covering stocks, bonds, andcommodities) over a fourteen-and-half year period (January 2008-June 2022). Wealso show that the synthetic generation process is able to capture well the keycharacteristics of the original data, and the optimization scheme results inportfolios that exhibit satisfactory out-of-sample performance. We also showthat this approach outperforms the conventional equal-weights (1/N) assetallocation strategy and other optimization formulations based on historicaldata only.

A Modified CTGAN-Plus-Features Based Method for Optimal Asset Allocation