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Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution

Autor: Zhao, Zijie, Welsch, Roy E.

Leveraging Deep Reinforcement Learning (DRL) in automated stock trading has shown promising results, yet its application faces significant challenges, including the curse of dimensionality, inertia in trading actions, and insufficient portfolio diver

Externí odkaz: http://arxiv.org/abs/2410.14927

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Aligning LLMs with Human Instructions and Stock Market Feedback in Financial Sentiment Analysis

Autor: Zhao, Zijie, Welsch, Roy E.

Financial sentiment analysis is crucial for trading and investment decision-making. This study introduces an adaptive retrieval augmented framework for Large Language Models (LLMs) that aligns with human instructions through Instruction Tuning and in

Externí odkaz: http://arxiv.org/abs/2410.14926

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Estimating Heterogeneous Treatment Effects on Survival Outcomes Using Counterfactual Censoring Unbiased Transformations

Autor: Xu, Shenbo, Cobzaru, Raluca, Finkelstein, Stan N., Welsch, Roy E., Ng, Kenney, Shahn, Zach

Methods for estimating heterogeneous treatment effects (HTE) from observational data have largely focused on continuous or binary outcomes, with less attention paid to survival outcomes and almost none to settings with competing risks. In this work,

Externí odkaz: http://arxiv.org/abs/2401.11263

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A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI

Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site, and currently made with biopsy and histology. Here we develop a novel deep learning approach for accurate non-invasive digital histology with whole-brain MR

Externí odkaz: http://arxiv.org/abs/2110.03588

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