8 citations · 19 across the 11 of their papers we have counts for
11 papers
Benchmarking Off-the-Shelf Multimodal AI Models Against Dermatologists on Patient-Captured Skin Images
Rian Dolphin, Laura Knowles
Artificial intelligence (AI) has advanced at a rapid pace in recent years. Initially, breakthroughs in large language models caught widespread attention. However, recent generation…
Grounded Event Extraction from SEC 8-K Filings with a Fine-Grained Taxonomy
Rian Dolphin, Joe Dursun, Jarrett Blankenship +2
Form 8-K filings are the primary channel through which U.S. public companies disclose material events, but the SEC item codes attached to them are coarse: a single item spans routi…
Relational Probing: LM-to-Graph Adaptation for Financial Prediction
Yingjie Niu, Changhong Jin, Rian Dolphin +1
Language models can be used to identify relationships between financial entities in text. However, while structured output mechanisms exist, prompting-based pipelines still incur a…
Taxonomy-Aligned Risk Extraction from 10-K Filings with Autonomous Improvement Using LLMs
Rian Dolphin, Joe Dursun, Jarrett Blankenship +2
We present a methodology for extracting structured risk factors from corporate 10-K filings while maintaining adherence to a predefined hierarchical taxonomy. Our three-stage pipel…
Evaluating Financial Relational Graphs: Interpretation Before Prediction
Yingjie Niu, Lanxin Lu, Rian Dolphin +2
Accurate and robust stock trend forecasting has been a crucial and challenging task, as stock price changes are influenced by multiple factors. Graph neural network-based methods h…
Contrastive Learning of Asset Embeddings from Financial Time Series
Rian Dolphin, Barry Smyth, Ruihai Dong
Representation learning has emerged as a powerful paradigm for extracting valuable latent features from complex, high-dimensional data. In financial domains, learning informative r…
Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach
Rian Dolphin, Joe Dursun, Jonathan Chow +3
Financial news plays a crucial role in decision-making processes across the financial sector, yet the efficient processing of this information into a structured format remains chal…
Industry Classification Using a Novel Financial Time-Series Case Representation
Rian Dolphin, Barry Smyth, Ruihai Dong
The financial domain has proven to be a fertile source of challenging machine learning problems across a variety of tasks including prediction, clustering, and classification. Rese…
A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets
Rian Dolphin, Barry Smyth, Ruihai Dong
Industry classification schemes provide a taxonomy for segmenting companies based on their business activities. They are relied upon in industry and academia as an integral compone…
Stock Embeddings: Learning Distributed Representations for Financial Assets
Rian Dolphin, Barry Smyth, Ruihai Dong
Identifying meaningful relationships between the price movements of financial assets is a challenging but important problem in a variety of financial applications. However with rec…
Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities
Rian Dolphin, Barry Smyth, Yang Xu +1
Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and…