My research work explores the intersection of AI, machine learning, and finance, addressing both theoretical advancements and practical applications. These papers reflect my commitment to pushing the boundaries of what AI can achieve, while emphasizing transparency, ethical considerations, and industry impact. Below is a selection of my published work, covering topics from model interpretability to applied AI techniques in finance.
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Nicole Koenigstein
"Dynamic and context-dependent stock price prediction using attention modules and news sentiment"
Digital Finance, Springer Nature, 2023
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Yiting Liu, Joerg Osterrieder, Branka Hadji Misheva, Nicole Koenigstein, Lennart Baals
"Navigating the Environmental, Social, and Governance (ESG) landscape: constructing a robust and reliable scoring engine - insights into Data Source Selection, Indicator Determination, Weighting and Aggregation Techniques, and Validation Processes for Comprehensive ESG Scoring Systems"
Open Research Europe, 2023
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