scholarly journals Language models for financial news recommendation

2000 ◽  
Author(s):  
Victor Lavrenko ◽  
Matt Schmill ◽  
Dawn Lawrie ◽  
Paul Ogilvie ◽  
David Jensen ◽  
...  
Keyword(s):  
Language Models ◽  
Financial News ◽  
News Recommendation

scholarly journals Discovery News: A Generic Framework for Financial News Recommendation

2020 ◽  
Vol 34 (08) ◽  
pp. 13390-13395
Author(s):  
Chong Wang ◽  
Lisa Kim ◽  
Grace Bang ◽  
Himani Singh ◽  
Russell Kociuba ◽  
...  
Keyword(s):  
Recommender System ◽  
Financial Services ◽  
Review Process ◽  
Financial Analysis ◽  
Financial Data ◽  
Financial News ◽  
Generic Framework ◽  
Global Ratings ◽  
News Recommendation ◽  
Manual Review

In the financial services industry, it is crucial for analysts to constantly monitor and stay informed on the latest developments of their portfolio of companies. This ensures that analysts are up-to-date in their analysis and provide highly credible and timely insights. Currently, analysts receive news alerts through manually created news alert subscriptions that are often noisy and difficult to manage. The manual review process is time-consuming and error-prone. We demonstrate Discovery News, a framework for an automated news recommender system for financial analysis at S&P's Global Ratings. This system includes the automated ingestion, relevancy, clustering, and ranking of news. The proposed framework is adaptable to any form of input news data and can seamlessly integrate with other data used for analysis like financial data.

scholarly journals Personalized Financial News Recommendation Algorithm Based on Ontology

2015 ◽  
Vol 55 ◽  
pp. 843-851 ◽  
Author(s):  
Rui Ren ◽  
Lingling Zhang ◽  
Limeng Cui ◽  
Bo Deng ◽  
Yong Shi
Keyword(s):  
Recommendation Algorithm ◽  
Financial News ◽  
News Recommendation

Financial news recommendation based on graph embeddings

2019 ◽  
Vol 125 ◽  
pp. 113115 ◽  
Author(s):  
Jiangtao Ren ◽  
Jiawei Long ◽  
Zhikang Xu
Keyword(s):  
Graph Embeddings ◽  
Financial News ◽  
News Recommendation

scholarly journals News recommendation system using machine learning

2018 ◽  
Vol 7 (2.18) ◽  
pp. 32
Author(s):  
Neha Rani ◽  
Sudhir Sudhir Pathak
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
High Accuracy ◽  
Svm Classifier ◽  
Financial News ◽  
The Neural Network ◽  
Market Forecast ◽  
News Recommendation ◽  
Weighted Values ◽  
The Given

The forecasting of financial news is yet becoming the main issue to divide the new into different classes on the basis of present time series. Moreover, it might be utilized for predicting and analyzing the stock market for the particular industry. Thus, the new content is significantly important to influence market forecast report. In this paper, the financial news from four countries namely America, Australia, India and South Africa along with their stop words are consider. The words along with their weighted values are determined and then the neural network is trained. Here, artificial neural network is used for classifying the appropriate results for the given input data. At last the comparison of ANN with SVM is shown. Experiments show that the ANN classification provides high accuracy to predict the news than the SVM classifier. 

Adolescent Language: Models, Assessment, and Links to Reading

2019 ◽  
Author(s):  
Amanda Goodwin ◽  
Yaacov Petscher ◽  
Jamie Tock
Keyword(s):  
Reading Comprehension ◽  
Bifactor Model ◽  
Language Models ◽  
Multiple Group ◽  
Global Factor ◽  
Eighth Grade Students ◽  
Key Aspects ◽  
Future Work ◽  
The Relationship ◽  
Best Fit

Various models have highlighted the complexity of language. Building on foundational ideas regarding three key aspects of language, our study contributes to the literature by 1) exploring broader conceptions of morphology, vocabulary, and syntax, 2) operationalizing this theoretical model into a gamified, standardized, computer-adaptive assessment of language for fifth to eighth grade students entitled Monster, PI, and 3) uncovering further evidence regarding the relationship between language and standardized reading comprehension via this assessment. Multiple-group item response theory (IRT) across grades show that morphology was best fit by a bifactor model of task specific factors along with a global factor related to each skill. Vocabulary was best fit by a bifactor model that identifies performance overall and on specific words. Syntax, though, was best fit by a unidimensional model. Next, Monster, PI produced reliable scores suggesting language can be assessed efficiently and precisely for students via this model. Lastly, performance on Monster, PI explained more than 50% of variance in standardized reading, suggesting operationalizing language via Monster, PI can provide meaningful understandings of the relationship between language and reading comprehension. Specifically, considering just a subset of a construct, like identification of units of meaning, explained significantly less variance in reading comprehension. This highlights the importance of considering these broader constructs. Implications indicate that future work should consider a model of language where component areas are considered broadly and contributions to reading comprehension are explored via general performance on components as well as skill level performance.

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory
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