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Analysis of institutional authors

Perez H.Author

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November 28, 2025
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Proceedings Paper
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Multivariate Regime Identification and Prediction in Financial Markets via Gaussian Mixture and Gradient Boosting Methods

Publicated to: Syntactic ASP Forgetting with Forks. 16203 LNCS 340-353 - 2026-01-01 16203 LNCS(), DOI: 10.1007/978-3-032-08462-0_27

Authors:

Sánchez-Fernández Á; Díez-González J; Huerga-Pérez N; Perez H
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Affiliations

Department of Mechanical; Computer and Aerospace Engineering; Universidad de León; León; 24071; Spain - Author

Abstract

Financial markets rarely behave uniformly over time, instead exhibiting periods of different market conditions. These structural shifts pose significant challenges for traditional forecasting models, particularly due to heteroskedasticity and non-linearity. While market regime frameworks seek to address this complexity, many existing approaches are constrained by univariate inputs, rigid transitions, or lack predictive capability. This paper proposes a two-phase framework that overcomes these limitations by integrating unsupervised and supervised machine learning techniques. In the first phase, a Gaussian Mixture Models (GMM) is used to detect latent market regimes from a rich set of macro-financial indicators, capturing overlapping probabilistic states with interpretable economic characteristics. We identify six distinct regimes—including high-inflation, crisis, and expansionary environments—each associated with unique risk-return profiles. In the second phase, we train an XGBoost-based Gradient Boosting Machine (GBM) classifier on historical regime labels to forecast future regime states. The predictive model achieves high out-of-sample classification accuracy (92.2%) and outperforms the market in a simple long-short trading strategy based on anticipated regime shifts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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Keywords

Adaptive boostingBoosting methodCommerceElectronic tradingFinancial forecastingFinancial marketsForecastingForecasting modelsGaussian distributionGaussian mixture modelGaussian mixture modelsGaussian-mixturesGradient boostingLearning systemsMachine learningMarket conditionMarket regime detectionMultivariate time seriesStructural shifts

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Syntactic ASP Forgetting with Forks due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2026, it was in position 70/78, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence. Notably, the journal is positioned above the 90th percentile.

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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-03:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 1.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 1 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 2.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (PEREZ GARCIA, HILDE).

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