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Self-awareness is a psychological state in which the individual becomes the focal point of their attention. This inward focus transforms the self into an object of contemplation and assessment, influencing how individuals perceive their actions and their alignment with personal and societal standards.Triggers and Contexts for Self-AwarenessSelf-awareness can be activated by external stimuli that make individuals visually or audibly aware of themselves, such as mirrors, cameras, or recordings.
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Related Experiment Video

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Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
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Risk-aware multi-armed bandit problem with application to portfolio selection.

Xiaoguang Huo1, Feng Fu2,3

  • 1Department of Mathematics, Cornell University, Ithaca, NY 14850, USA.

Royal Society Open Science
|January 2, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel portfolio construction algorithm using a risk-aware multi-armed bandit approach. It balances financial market exploration and exploitation for optimal risk-return strategies.

Keywords:
conditional value-at-riskgraph theorymulti-armed banditonline learningportfolio selectionrisk-awareness

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Area of Science:

  • Quantitative Finance
  • Machine Learning
  • Financial Market Analysis

Background:

  • Sequential portfolio selection is gaining traction in finance and machine learning.
  • The exploration-exploitation dilemma is central to sequential decision-making under uncertainty.

Purpose of the Study:

  • To develop a risk-aware algorithm for sequential portfolio selection.
  • To balance risk and return in financial portfolios.

Main Methods:

  • Adapting the stochastic multi-armed bandit framework.
  • Incorporating risk awareness into asset selection.
  • Filtering assets using financial market topology.
  • Minimizing a coherent risk measure.

Main Results:

  • A novel algorithm for portfolio construction is introduced.
  • The algorithm effectively balances risk and return.
  • Demonstrates a practical application of multi-armed bandits in finance.

Conclusions:

  • Risk-aware multi-armed bandit policies offer a robust framework for sequential portfolio selection.
  • Market topology filtering enhances portfolio performance.
  • The proposed method provides a balance between risk and return.