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30 findingsResults for “Risk Management”
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Finding 63121 source
Authors propose novel adaptive compliance and automated risk analytics definitions merging emerging tech with business strategies, validated through data analysis that underscores efficiency gains, highlights confidentiality challenges, urges research significantly.
Matched: management, risk
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Finding 30651 source
Research introduces novel optimization models awarding rewards for returns surpassing targets while imposing $$penalties$$ for underperformance, bridging gaps between traditional models and downside risk-focused approaches in modern financial risk management.
Matched: management, risk
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Finding 50901 source
AI revolutionizes finance by improving portfolio recommendations, risk management, and automated trading. Future research should address AI's limitations and enhance regulatory oversight. Potential applications include more accurate investment advice, better risk mitigation, and efficient automated trading systems.
Matched: management, risk
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Finding 63481 source
Data-driven analysis reveals that under tight monetary policies, risk management actions amplify off-balance-sheet activity and systemic risk, while strong macroprudential supervision mitigates these effects, highlighting the imperative for regulatory oversight.
Matched: management, risk
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Finding 29971 source
The RiPO framework offers a robust solution for managing portfolio risks while pursuing high returns. Potential applications include real-time portfolio management and adaptive trading strategies in volatile financial markets. Future research could enhance the framework's flexibility for various market conditions.
Matched: management, risk
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Finding 73001 source
The study concludes that the K-means algorithm is highly effective for financial risk prediction, with potential applications in credit and systemic risk management. Future research may focus on improving algorithm accuracy and integrating emerging technologies like blockchain for enhanced financial risk management.
Matched: management, risk
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Finding 70931 source
This paper uniquely advances Quantitative Risk Management by developing and empirically testing novel solutions for negative and multi-level risk allocations under Basel 2.5 and FRTB. Its originality lies in combining practical allocation methods with efficient Monte Carlo computation for Shapley allocation, offering significant insights for regulatory compliance and risk capital allocation.
Matched: management, risk
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Finding 27561 source
Integrating classical econometric $GARCH$ models with deep reinforcement learning, the paper introduces a novel framework for improved $VaR$ estimation in volatile markets. Addressing class imbalance in predictions, it uniquely bridges econometrics and AI, enhancing risk forecasting and capital allocation. This approach offers compelling insights and advancements in financial risk management.
Matched: management, risk
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Finding 30641 source
Researchers reveal innovative robust portfolio management techniques by introducing a reward-penalty mechanism that balances portfolio loss and downside risk, applying explicit closed-form $$CVaR$$ formulas under uncertain multivariate distributions with impact.
Matched: management, risk
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Finding 49551 source
Researchers develop breakthrough methods for constructing improved confidence intervals for expectiles, integral to financial risk management, using higher-order asymptotic results with $$Edgeworth$$ expansions for standardized and studentized kernel-based estimators, notably.
Matched: management, risk
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Finding 59441 source
This paper uniquely addresses the emerging risk landscape from generative AI (GenAI) and large language models (LLMs) in finance. It innovatively adapts model risk management frameworks (e.g., SR 11-7, SS 1/23), introduces governance and testing strategies, and highlights novel challenges, making it a timely, compelling, and original contribution.
Matched: management, risk
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Finding 83491 source
The research suggests AI and ML can significantly enhance financial market operations but require careful implementation. Potential applications include improved trading strategies and risk management. Future research should focus on ethical considerations and regulatory frameworks to ensure responsible AI and ML use.
Matched: management, risk
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Finding 73161 source
The paper compiles recent research on generative AI agents in finance, emphasizing risk management improvements via quantitative illustrations. Its structured synthesis provides practical value and fresh insights by aggregating diverse studies. As a literature review lacking groundbreaking methods, its timeliness and presentation make it a compelling resource for financial AI.
Matched: management, risk