We propose an indicator to measure the degree to which a particular news article is novel, as well as an indicator to measure the degree to which a particular news item attracts attention from investors. The novelty measure is obtained by comparing the extent to which a particular news article is similar to earlier new…
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We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day. We propose a unified latent space model to characterize the "co-movements" between stock prices and …
The profusion of online news articles makes it difficult to find interesting articles, a problem that can be assuaged by using a recommender system to bring the most relevant news stories to readers. However, news recommendation is challenging because the most relevant articles are often new content seen by few users. …
Paper proposes a new trading strategy using corporate event detection from news articles.
problem Predicting stock movements based on corporate events from news articles.
method Bi-level event detection model: low-level for token-level event identification, high-level for article-level event identification.
result The proposed strategy outperforms existing models in stock prediction metrics.
The rising growth of fake news and misleading information through online media outlets demands an automatic method for detecting such news articles. Of the few limited works which differentiate between trusted vs other types of news article (satire, propaganda, hoax), none of them model sentence interactions within a d…
In this work, we ask two questions: 1. Can we predict the type of community interested in a news article using only features from the article content? and 2. How well do these models generalize over time? To answer these questions, we compute well-studied content-based features on over 60K news articles from 4 communit…
Fake news may be intentionally created to promote economic, political and social interests, and can lead to negative impacts on humans beliefs and decisions. Hence, detection of fake news is an emerging problem that has become extremely prevalent during the last few years. Most existing works on this topic focus on man…
A rising topic in computational journalism is how to enhance the diversity in news served to subscribers to foster exploration behavior in news reading. Despite the success of preference learning in personalized news recommendation, their over-exploitation causes filter bubble that isolates readers from opposing viewpo…
Content-based news recommendation systems need to recommend news articles based on the topics and content of articles without using user specific information. Many news articles describe the occurrence of specific events and named entities including people, places or objects. In this paper, we propose a graph traversal…
Predict stock price movements using financial data and news articles with LLMs.
problem Predicting stock price movements using financial data and news articles.
method Combining financial data and news articles, employing pre-trained LLMs, and using retrieval augmentation techniques.
result Predicted stock price movements with a weighted F1-score of 58.5% and 59.1%.
On the one hand, nowadays, fake news articles are easily propagated through various online media platforms and have become a grand threat to the trustworthiness of information. On the other hand, our understanding of the language of fake news is still minimal. Incorporating hierarchical discourse-level structure of fak…
Model predicts ESG ratings from news articles using multivariate timeseries analysis.
problem Lack of accurate and automated methods for ESG ratings prediction.
method Multivariate timeseries analysis combined with deep learning.
result Model outperforms state-of-the-art methods in predicting ESG ratings.
Better stock market predictions can be made by simpler topic models.
problem Improving stock market prediction accuracy using news articles.
method Empirical and theoretical analysis of supervised and plain LDA models, with a focus on Gibbs sampling and random search.
result Simpler topic models (plain LDA) outperform more complex models (sLDA) in out-of-sample performance.
In this article we raise some new questions about positive definite functions on free groups, and explain how these are related to more well-known questions. The article is intended as a survey of known results that also offers some new perspectives and interesting observations; therefore the style is expository.
This paper gives an overview of some basic properties of Leibniz algebras. Some of the results were known earlier, but in the article they are accompanied by new simple proofs. Some of the results are new. The article can be viewed as a digest or a mini-manual for the basic theory of Leibniz algebras
This article is the introductory part of authors PhD thesis. The article presents a new coordinate invariant definition of quasiregular and quasiconformal mappings on Riemannian manifolds that generalizes the definition of quasiregular mappings on . The new definition arises naturally from the inner product struc…
Fake news are nowadays an issue of pressing concern, given their recent rise as a potential threat to high-quality journalism and well-informed public discourse. The Fake News Challenge (FNC-1) was organized in 2017 to encourage the development of machine learning-based classification systems for stance detection (i.e.…
To compare entities of differing types and structural components, the artificial neural network paradigm was used to cross-compare structural components between heterogeneous documents. Trainable weighted structural components were input into machine-learned activation functions of the neurons. The model was used for m…
Hyperfinite knots, or limits of equivalence classes of knots induced by a knot invariant taking values in a metric space, were introduced in a previous article by the author. In this article, we present new examples of hyperfinite knots stemming from sequences of torus knots.
Article explores G2-structures with quadratic conditions, finding new ERP and complete solitons.
problem Investigating closed G2-structures satisfying quadratic conditions.
method Analyzing a second-order PDE system involving parameter λ, producing new examples of ERP and complete solitons.
result First examples of ERP G2-structures, including complete inhomogeneous ERP G2-structure.
SAFE detects fake news by analyzing text and image similarities.
problem Detecting fake news with less focus on text-image similarity.
method SAFE uses neural networks to extract text and visual features, then learns their relationship to predict fake news.
result SAFE effectively recognizes fake news based on text, images, or mismatches.
Study shows publicly available news impacts financial markets.
problem Impact of publicly available news on financial markets.
method Extracted news from Common Crawl, identified relevant companies, used sentiment analysis and information theory.
result Publicly available news has significant impact on financial markets.
Transformer models improve financial sentiment measurement.
problem Capturing nuanced sentiment from financial news articles.
method Transformer-based language models for sentiment classification and aggregation.
result Transformer models outperform traditional dictionary-based methods in sentiment classification.
Quantum analysis tags news for sentiment and entities.
problem Identifying bias in news reporting.
method Continuous data collection, NER and sentiment analysis.
result A corpus of tagged news articles for public use.
StonkBERT predicts stock price movements using company text data.
problem Can language models predict medium-run stock price movements?
method Fine-tuning transformer-based language models (BERT) on company text data (news articles, blogs, annual reports) for stock price performance classification.
result StonkBERT shows substantial improvement in predictive accuracy compared to traditional models, with news articles providing the best results.
Predicting bankruptcy using financial data and news sentiment.
problem Predicting company bankruptcy to mitigate its impact.
method Combining financial data with news sentiment analysis.
result A framework for predicting company bankruptcy.
Financial forecasting using news articles is an emerging field. In this paper, we proposed hybrid intelligent models for stock market prediction using the psycholinguistic variables (LIWC and TAALES) extracted from news articles as predictor variables. For prediction purpose, we employed various intelligent techniques …
Study detects fake news in Brazilian Portuguese using machine learning.
problem Spread of misinformation through fake news in Brazilian Portuguese.
method Machine learning, TF-IDF, Word2Vec, classification algorithms.
result High accuracy and F1-Score in identifying fake news.
Examines medial axis in pseudo-Euclidean spaces.
problem No specific problem stated; focuses on new context.
method Follows Birbrair and Denkowski's approach.
result Feasibility of medial axis in pseudo-Euclidean spaces checked.
Stock price prediction is important for value investments in the stock market. In particular, short-term prediction that exploits financial news articles is promising in recent years. In this paper, we propose a novel deep neural network DP-LSTM for stock price prediction, which incorporates the news articles as hidden…
In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brough…
The study identifies impactful news articles based on liquidity changes, improving asset return prediction.
problem Evaluating the sentiment of financial news articles for institutional investors.
method Liquidity-driven variables are used to identify impactful news articles, focusing on liquidity mode switches.
result The screened dataset leads to superior performance in short-term asset return prediction.
The new property of minimal surfaces is obtained in this article.
Predict stock prices using financial news sentiment analysis.
problem Predicting stock market trends for better investment returns.
method Deep Learning (MLP, LSTM, FinBERT-LSTM) integrating news sentiment.
result FinBERT-LSTM model predicts stock prices more accurately.
Method aligns multilingual news for better stock return prediction.
problem Losing nuances in multilingual news translations.
method Optimal transport for sentence alignment.
result Aligned sentences correlate better with stock returns.
This article analyzes the relationship between co-persistence and hedging which indicates co-persistence ratio is just the long-term hedging ratio. The new method of exhaustive search algorithm for deriving co-persistence ratio is derived in the article. And we also develop a new hedging strategy of combining co-persis…
This article is an attempt to generalize Riemann's bilinear relations on compact Riemann surface of genus at least 2, which may lead to new structures in the theory of hyperbolic Riemann surfaces. No significant result is obtained, the article serves to bring the readers' attention to the observation made by [Bol-1949]…
CHAMELEON tackles news recommendation using deep learning, addressing cold-start issues.
problem Personalizing user experiences in a dynamic news search space.
method Modular reference architecture with different neural building blocks, leveraging user and article context, and modeling temporal decay and concept drift.
result CHAMELEON outperforms traditional and state-of-the-art session-based recommendation algorithms in accuracy, item coverage, novelty, and reduced item cold-start problem.
The article proves a new entropy formula for surfaces with boundaries.
problem Entropy formula for surfaces with boundaries.
method Established a monotonicity formula of Hamilton type entropy.
result Entropy functional and -functional relation studied.
New estimator for causal effects in large datasets.
problem Unobserved confounding in large-scale data.
method Doubly robust estimator combining imputation, IPW, and cross-fitting.
result Error converges to Gaussian distribution at parametric rate.
Colored link Invariantsmath.GT
New colored link invariants using multi-quandles.
problem Developing new invariants for colored links.
method Introducing multi-quandles and topological multi-quandles.
result New colored link invariants created.
Study detects emerging trends in financial news articles about Microsoft.
problem Challenges in identifying trends in long-form financial news articles.
method Topic modeling and term frequency for keyword similarity analysis.
result Demonstrates the influence of the pandemic on Microsoft.
In this article we define new flows on the Hitchin components for PGL(V). Special examples of these flows are associated to simple closed curves on the surface and give generalized twist flows. Other examples, so called eruption flows, are associated to pair of pants in S and capture new phenomena which are not present…
Let denote a diffusion process defined on a closed compact manifold. In an earlier article, the author introduced a new approach to constructing admissible vector fields on the associated space of paths, under the assumption of ellipticity of . In this article, this method is extended to yield similar results fo…
This article addresses persistent tangles. These are tangles whose presence in a knot diagram forces that diagram to be knotted. We provide new methods for constructing persistent tangles. Our techniques rely mainly on the existence of non-trivial colorings for the tangles in question. Our main result in this article i…
This is mainly a survey article on the recent development of the theory of graph-like Legendrian unfoldings and its applications. The notion of big Legendrian submanifolds was introduced by Zakalyukin for describing the wave front propagations. Graph-like Legendrian unfoldings belong to a special class of big Legendria…
We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Usin…
Analyzes news graphs to predict financial market dislocations.
problem Predicting financial market dislocations using news content.
method Extracts entities from news articles, aggregates them into graphs, applies network analysis, and uses sentiment analysis.
result Identifies high entropy in news graphs correlates with financial market dislocations.