Forecasting Binary Economic Events in Modern Mercantilism: Traditional methodologies coupled with PCA and K-means Quantitative Analysis of Qualitative Sentimental Data
Fuente:
arXiv
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908487541325824 |
|---|---|
| author | Kot, Sebastian |
| author_facet | Kot, Sebastian |
| contents | This paper examines Modern Mercantilism, characterized by rising economic nationalism, strategic technological decoupling, and geopolitical fragmentation, as a disruptive shift from the post-1945 globalization paradigm. It applies Principal Component Analysis (PCA) to 768-dimensional SBERT-generated semantic embeddings of curated news articles to extract orthogonal latent factors that discriminate binary event outcomes linked to protectionism, technological sovereignty, and bloc realignments. Analysis of principal component loadings identifies key semantic features driving classification performance, enhancing interpretability and predictive accuracy. This methodology provides a scalable, data-driven framework for quantitatively tracking emergent mercantilist dynamics through high-dimensional text analytics |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09243 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forecasting Binary Economic Events in Modern Mercantilism: Traditional methodologies coupled with PCA and K-means Quantitative Analysis of Qualitative Sentimental Data Kot, Sebastian General Economics Economics Machine Learning Methodology This paper examines Modern Mercantilism, characterized by rising economic nationalism, strategic technological decoupling, and geopolitical fragmentation, as a disruptive shift from the post-1945 globalization paradigm. It applies Principal Component Analysis (PCA) to 768-dimensional SBERT-generated semantic embeddings of curated news articles to extract orthogonal latent factors that discriminate binary event outcomes linked to protectionism, technological sovereignty, and bloc realignments. Analysis of principal component loadings identifies key semantic features driving classification performance, enhancing interpretability and predictive accuracy. This methodology provides a scalable, data-driven framework for quantitatively tracking emergent mercantilist dynamics through high-dimensional text analytics |
| title | Forecasting Binary Economic Events in Modern Mercantilism: Traditional methodologies coupled with PCA and K-means Quantitative Analysis of Qualitative Sentimental Data |
| topic | General Economics Economics Machine Learning Methodology |
| url | https://arxiv.org/abs/2508.09243 |