A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Benchmark assesses forecasting models' ability to use textual context.
R package sentometrics analyzes text sentiment for predictions.
The paper proposes a new recommender system combining ratings and textual reviews.
Effective detection of fake news has recently attracted significant attention. Current studies have made significant contributions to predicting fake news with less focus on exploiting the relationship (similarity) between the textual and visual information in news articles. Attaching importance to such similarity help…
A new framework predicts stock movements using news sentiment and relational data.
A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is published on a regular basis and in most occasions is freely distributed. The present Ph.D. Thesis deals with the problem …
The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensional vertex-based vector representations. This paper considers a novel variational formulation of network embeddings, with special focus on …
Textual network embedding leverages rich text information associated with the network to learn low-dimensional vectorial representations of vertices. Rather than using typical natural language processing (NLP) approaches, recent research exploits the relationship of texts on the same edge to graphically embed text. How…
Self-supervised bidirectional transformer models such as BERT have led to dramatic improvements in a wide variety of textual classification tasks. The modern digital world is increasingly multimodal, however, and textual information is often accompanied by other modalities such as images. We introduce a supervised mult…
InfoSEM infers gene regulatory networks without GT labels, improving performance.
ChatGPT snapshots predict future stock returns.
The marvel of markets lies in the fact that dispersed information is instantaneously processed and used to adjust the price of goods, services and assets. Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted…
Proposes Textual Echo Cancellation to improve speech recognition.
Transformer learns CoVaR from financial news, improving systemic risk forecasts.
An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets s…
While ubiquitous, textual sources of information such as company reports, social media posts, etc. are hardly included in prediction algorithms for time series, despite the relevant information they may contain. In this work, openly accessible daily weather reports from France and the United-Kingdom are leveraged to pr…
Enhances GNNs with text features for better fake news detection.
The study finds that supply chain information from LLM embeddings improves stock returns predictions.
New model uses financial filings to predict bankruptcy, even without MDA sections.
MGM improves media profiling by integrating textual and structural features.
Multimodal analysis that uses numerical time series and textual corpora as input data sources is becoming a promising approach, especially in the financial industry. However, the main focus of such analysis has been on achieving high prediction accuracy while little effort has been spent on the important task of unders…
A model integrates CNN and LSTM with LLM for better stock forecasting.
Paper proposes a method to improve graph clustering by integrating node textual metadata with node signals in GGMs.
Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics. In this paper, we propose a novel text regression model based on a conditional generative adversarial network (GAN), with an attempt to associate textual data and social outcomes in a semi-supervis…
IUS framework predicts EUR/USD exchange rate with improved accuracy.
This paper proposes a zero-shot learning approach for audio classification based on the textual information about class labels without any audio samples from target classes. We propose an audio classification system built on the bilinear model, which takes audio feature embeddings and semantic class label embeddings as…
Paper presents ECL dataset for multi-modal bankruptcy prediction.
In the last decade, many diverse advances have occurred in the field of information extraction from data. Information extraction in its simplest form takes place in computing environments, where structured data can be extracted through a series of queries. The continuous expansion of quantities of data have therefore p…
Enhances weather detection by learning from auxiliary information.
Solves the challenge of retrieving item-specific financial information from Form 10-Q filings.
Study uses LLMs to improve financial forecasting by integrating textual and numerical data.
SFBoW provides sentence embeddings with predefined dimensions.
Crimes emerge out of complex interactions of human behaviors and situations. Linkages between crime incidents are highly complex. Detecting crime linkage given a set of incidents is a highly challenging task since we only have limited information, including text descriptions, incident times, and locations. In practice,…
Decision support tools that rely on supervised learning require large amounts of expert annotations. Using past radiological reports obtained from hospital archiving systems has many advantages as training data above manual single-class labels: they are expert annotations available in large quantities, covering a popul…
We introduce a multimodal visual-textual search refinement method for fashion garments. Existing search engines do not enable intuitive, interactive, refinement of retrieved results based on the properties of a particular product. We propose a method to retrieve similar items, based on a query item image and textual re…
An imprecise region is referred to as a geographical area without a clearly-defined boundary in the literature. Previous clustering-based approaches exploit spatial information to find such regions. However, the prior studies suffer from the following two problems: the subjectivity in selecting clustering parameters an…
ChatGPT scores corporate investment plans, predicting future spending and returns.
LMoE uses LLMs to improve stock trading by selecting experts based on textual and price data.
Novel method diagnoses large language models' reasoning abilities.
A new test assesses text similarity between two groups of documents.
Paper proposes active learning for mining conflict dynamics from textual data.
A new differential entropy estimator for neural networks training.
The main approaches for node classification in graphs are information propagation and the association of the class of the node with external information. State of the art methods merge these approaches through Graph Convolutional Networks. We here use the association of topological features of the nodes with their clas…
Examines parallels between human subjects and texts for causal inference.
Paper introduces lexical ratio to measure portfolio diversification.
Anonymization reduces economic signal extraction from financial texts.
ChatGPT enhances GNN for stock movement prediction.
Interpretable text-response modelling for structured outcomes