Practical forecasting of risk boundaries for industrial metals and critical minerals via statistical machine learning techniques

Choi, Insu; Kim, Woo Chang

doi:10.1016/j.irfa.2024.103252

Cited by 3 publications

(1 citation statement)

References 52 publications

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“…Select assets with higher returns in the capital market, and then apply the R-Vine Copula model to reduce risk levels and determine the optimal asset to ensure investment safety and profitability. This method can systematically assist investors in making investment decisions, telling portfolio managers which assets to hold and how much to invest in each asset to achieve the goal of obtaining maximum potential return with minimal risk [38].…”

Section: Discussionmentioning

confidence: 99%

Measurement and Control of Risk Contagion in Portfolio Optimization Processes

Qiao,

Qiao

2024

Symmetry

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The success of an investment portfolio is not only related to its yield, but is also influenced by market risks, especially the contagion risks that may exist between assets. Therefore, effective portfolio optimization requires first studying the risk contagion relationship between financial assets. We selected a total of six financial assets from different stock and exchange rate markets as the research objects, and explored the risk contagion relationships of various assets in the investment portfolio through Vine Copula. Research has shown that there is often a structural mutation in one or some assets of an investment portfolio, leading to significant fluctuations in investment returns. The Vine Copula model can effectively measure the risk contagion between assets caused by asset structural mutations.

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Section: Discussionmentioning

confidence: 99%

Measurement and Control of Risk Contagion in Portfolio Optimization Processes

Qiao,

Qiao

2024

Symmetry

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A temporal information transfer network approach considering federal funds rate for an interpretable asset fluctuation prediction framework

Choi,

Kim

2024

International Review of Economics & Finance

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Terrorism group prediction using feature combination and BiGRU with self-attention mechanism

Abdalsalam,

Li,

Dahou

et al. 2024

PeerJ Computer Science

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The world faces the ongoing challenge of terrorism and extremism, which threaten the stability of nations, the security of their citizens, and the integrity of political, economic, and social systems. Given the complexity and multifaceted nature of this phenomenon, combating it requires a collective effort, with tailored methods to address its various aspects. Identifying the terrorist organization responsible for an attack is a critical step in combating terrorism. Historical data plays a pivotal role in this process, providing insights that can inform prevention and response strategies. With advancements in technology and artificial intelligence (AI), particularly in military applications, there is growing interest in utilizing these developments to enhance national and regional security against terrorism. Central to this effort are terrorism databases, which serve as rich resources for data on armed organizations, extremist entities, and terrorist incidents. The Global Terrorism Database (GTD) stands out as one of the most widely used and accessible resources for researchers. Recent progress in machine learning (ML), deep learning (DL), and natural language processing (NLP) offers promising avenues for improving the identification and classification of terrorist organizations. This study introduces a framework designed to classify and predict terrorist groups using bidirectional recurrent units and self-attention mechanisms, referred to as BiGRU-SA. This approach utilizes the comprehensive data in the GTD by integrating textual features extracted by DistilBERT with features that show a high correlation with terrorist organizations. Additionally, the Synthetic Minority Over-sampling Technique with Tomek links (SMOTE-T) was employed to address data imbalance and enhance the robustness of our predictions. The BiGRU-SA model captures temporal dependencies and contextual information within the data. By processing data sequences in both forward and reverse directions, BiGRU-SA offers a comprehensive view of the temporal dynamics, significantly enhancing classification accuracy. To evaluate the effectiveness of our framework, we compared ten models, including six traditional ML models and four DL algorithms. The proposed BiGRU-SA framework demonstrated outstanding performance in classifying 36 terrorist organizations responsible for terrorist attacks, achieving an accuracy of 98.68%, precision of 96.06%, sensitivity of 96.83%, specificity of 99.50%, and a Matthews correlation coefficient of 97.50%. Compared to state-of-the-art methods, the proposed model outperformed others, confirming its effectiveness and accuracy in the classification and prediction of terrorist organizations.

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Practical forecasting of risk boundaries for industrial metals and critical minerals via statistical machine learning techniques

Cited by 3 publications

References 52 publications

Measurement and Control of Risk Contagion in Portfolio Optimization Processes

Measurement and Control of Risk Contagion in Portfolio Optimization Processes

A temporal information transfer network approach considering federal funds rate for an interpretable asset fluctuation prediction framework

Terrorism group prediction using feature combination and BiGRU with self-attention mechanism

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