Analyst monitoring and information asymmetry reduction: U.S. evidence on environmental investment

Liu, Lewis

doi:10.1016/j.igd.2023.100098

Cited by 12 publications

(2 citation statements)

References 51 publications

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“…(2) Ablation experiment based on the classifier ensemble scale After obtaining the feature reconstruction space for each high-dimensional dataset, we explored the effect of the number of classifiers (2,4,6) on the HDELC ensemble algorithm. The initial set of classifiers was decision tree (DT), random dorest (RT), Adaboost, gradient boosted (GB), extreme tree (ET), and support vector machine (SVM).…”

Section: Ablation Experimentsmentioning

confidence: 99%

“…In financial analysis [2], the basic features of each stock include opening price, closing price, high price, low price, volume, etc., and also have features such as macroeconomic indicators, company fundamental information, etc., which are used to construct complex stock market classification prediction models. Multiple data such as physiological signal data (heart rate, blood pressure, blood glucose level), electronic health records, diagnostic records, medication usage records, etc., are involved in the health and disease screening task [3], and data such as air quality sensor data, water quality monitoring data, meteorological data, and satellite remote sensing data are acquired through multiple sensors in an environmental monitoring [4] classification task. How to fully extract valid and interpretable information from these high-dimensional data has become a hot topic widely studied by scholars nowadays.…”

Section: Introductionmentioning

confidence: 99%

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High-Dimensional Ensemble Learning Classification: An Ensemble Learning Classification Algorithm Based on High-Dimensional Feature Space Reconstruction

Zhao,

2024

Applied Sciences

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When performing classification tasks on high-dimensional data, traditional machine learning algorithms often fail to filter out valid information in the features adequately, leading to low levels of classification accuracy. Therefore, this paper explores the high-dimensional data from both the data feature dimension and the model ensemble dimension. We propose a high-dimensional ensemble learning classification algorithm focusing on feature space reconstruction and classifier ensemble, called the HDELC algorithm. First, the algorithm considers feature space reconstruction and then generates a feature space reconstruction matrix. It effectively achieves feature selection and reconstruction for high-dimensional data. An optimal feature space is generated for the subsequent ensemble of the classifier, which enhances the representativeness of the feature space. Second, we recursively determine the number of classifiers and the number of feature subspaces in the ensemble model. Different classifiers in the ensemble system are assigned mutually exclusive non-intersecting feature subspaces for model training. The experimental results show that the HDELC algorithm has advantages compared with most high-dimensional datasets due to its more efficient feature space ensemble capability and relatively reliable ensemble operation performance. The HDELC algorithm makes it possible to solve the classification problem for high-dimensional data effectively and has vital research and application value.

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Section: Ablation Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-Dimensional Ensemble Learning Classification: An Ensemble Learning Classification Algorithm Based on High-Dimensional Feature Space Reconstruction

Zhao,

2024

Applied Sciences

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From traditional innovation to green innovation: How an occurrence of natural disasters influences sustainable development?

Ma,

Feng,

Chang

2023

Sustainable Development

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With the high incidence of extreme events, it is important to examine how the occurrence of natural disasters influences long‐term sustainable development. We give evidence from the changing process of innovation. Using data from OECD countries from 1985 to 2018, we conclude that environmental threats from the occurrence of natural disasters drive countries to pay more attention to sustainable development and promote the change of innovation type from traditional to green innovation. Moreover, for different types of natural disasters, volcanic activities, landslides, and floods show a positive influence, while epidemics dampen it. Based on the sub‐sample analysis, this impact is more pronounced in countries with high levels of economic development, oil exports and right‐wing parties in power. Furthermore, renewable energy consumption acts as a positive moderator, while the moderating effects of government efficiency, corruption and globalization are inhibiting. Our paper provides new insights into natural disaster economics and sustainable development.

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Corporate Governance and Internal Control Mechanisms: Developing a Strategic Framework

Al Astal,

Ateeq,

Milhem

et al. 2024

Studies in Systems, Decision and Control

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Analyst monitoring and information asymmetry reduction: U.S. evidence on environmental investment

Cited by 12 publications

References 51 publications

High-Dimensional Ensemble Learning Classification: An Ensemble Learning Classification Algorithm Based on High-Dimensional Feature Space Reconstruction

High-Dimensional Ensemble Learning Classification: An Ensemble Learning Classification Algorithm Based on High-Dimensional Feature Space Reconstruction

From traditional innovation to green innovation: How an occurrence of natural disasters influences sustainable development?

Corporate Governance and Internal Control Mechanisms: Developing a Strategic Framework

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