Using News Articles and Financial Data to predict the likelihood of bankruptcy

• URL: http://arxiv.org/abs/2003.13414v1
• Date: Sun, 22 Mar 2020 17:29:41 GMT
• Title: Using News Articles and Financial Data to predict the likelihood of bankruptcy
• Authors: Michael Filletti and Aaron Grech
• Abstract summary: Millions of companies have filed for bankruptcy over the past decade. High interest rates, heavy debts and government regulations are to blame. The effect of a company going bankrupt can be devastating, hurting workers and shareholders.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the past decade, millions of companies have filed for bankruptcy. This has been caused by a plethora of reasons, namely, high interest rates, heavy debts and government regulations. The effect of a company going bankrupt can be devastating, hurting not only workers and shareholders, but also clients, suppliers and any related external companies. One of the aims of this paper is to provide a framework for company bankruptcy to be predicted by making use of financial figures, provided by our external dataset, in conjunction with the sentiment of news articles about certain sectors. News articles are used to attempt to quantify the sentiment on a company and its sector from an external perspective, rather than simply using internal figures. This work builds on previous studies carried out by multiple researchers, to bring us closer to lessening the impact of such events.

Related papers

• Google's Hidden Empire [0.0]
  We show that Google has amassed an empire of more than 6,000 companies.<n>The power of Google over the digital markets infrastructure and dynamics is likely greater than previously documented.<n>Our lessons from the past failures can inform the current approach towards one of the biggest ever big tech M&A deals.
  arXiv  Detail & Related papers  (2025-11-04T19:29:52Z)
• Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry [86.79268605140251]
  We study whether there are insurmountable barriers to entry in emerging markets for large language models. We show that the required number of data points can be significantly smaller than the incumbent company's dataset size. Our results demonstrate how multi-objective considerations can fundamentally reduce barriers to entry.
  arXiv  Detail & Related papers  (2024-09-05T17:45:01Z)
• FAIL: Analyzing Software Failures from the News Using LLMs [2.7325338323814328]
  We propose the Failure Analysis Investigation with LLMs (FAIL) system to fill this gap. FAIL collects, analyzes, and summarizes software failures as reported in the news. FAIL identified and analyzed 2457 distinct failures reported across 4,184 articles.
  arXiv  Detail & Related papers  (2024-06-12T13:51:51Z)
• On the Societal Impact of Open Foundation Models [93.67389739906561]
  We focus on open foundation models, defined here as those with broadly available model weights. We identify five distinctive properties of open foundation models that lead to both their benefits and risks.
  arXiv  Detail & Related papers  (2024-02-27T16:49:53Z)
• Characterizing Fake News Targeting Corporations [5.762925096147384]
  We investigate corporate misinformation across a diverse array of industries within the S&P 500 companies. Our study reveals that corporate misinformation encompasses topics such as products, politics, and societal issues. We observe that a company is not targeted by fake news all the time, but there are particular times when a critical mass of fake news emerges.
  arXiv  Detail & Related papers  (2024-01-04T10:47:07Z)
• CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn from Financial Reports [29.967008650845774]
  We introduce CR-COPEC called Causal Rationale of Corporate Performance Changes from financial reports. This is a comprehensive large-scale domain-adaptation causal sentence dataset to detect financial performance changes of corporate. CR-COPEC contributes to two major achievements. First, it detects causal rationale from 10-K annual reports of the U.S. companies, which contain experts' causal analysis following accounting standards in a formal manner. Second, it carefully considers different characteristics which affect the financial performance of companies in twelve industries.
  arXiv  Detail & Related papers  (2023-10-24T18:00:40Z)
• Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines [63.52264764099532]
  We use a large dataset of 1.8 million news headlines from major U.S. media outlets spanning from 2014 to 2022. We quantify the fine-grained thematic discrepancy related to four prominent topics - domestic politics, economic issues, social issues, and foreign affairs. Our findings indicate that on domestic politics and social issues, the discrepancy can be attributed to a certain degree of media bias.
  arXiv  Detail & Related papers  (2023-03-28T03:31:37Z)
• Misinfo Belief Frames: A Case Study on Covid & Climate News [49.979419711713795]
  We propose a formalism for understanding how readers perceive the reliability of news and the impact of misinformation. We introduce the Misinfo Belief Frames (MBF) corpus, a dataset of 66k inferences over 23.5k headlines. Our results using large-scale language modeling to predict misinformation frames show that machine-generated inferences can influence readers' trust in news headlines.
  arXiv  Detail & Related papers  (2021-04-18T09:50:11Z)
• Young Adult Unemployment Through the Lens of Social Media: Italy as a case study [108.33144653708091]
  We employ survey data together with social media data to analyse personality, moral values, but also cultural elements of the young unemployed population in Italy. Our findings show that there are small but significant differences in personality and moral values, with the unemployed males to be less agreeable. Unemployed have a more collectivist point of view, valuing more in-group loyalty, authority, and purity foundations.
  arXiv  Detail & Related papers  (2020-10-09T10:56:04Z)
• Impact of News on the Commodity Market: Dataset and Results [0.0]
  We propose a framework that extracts information such as past movements and expected directionality in prices. We apply this framework to the commodity "Gold" and train the machine learning models using a dataset of 11,412 human-annotated news headlines. We experiment to validate the causal effect of news flow on gold prices and observe that the information produced from our framework significantly impacts the future gold price.
  arXiv  Detail & Related papers  (2020-09-09T10:38:48Z)
• Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications [110.54266632357673]
  We present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals.
  arXiv  Detail & Related papers  (2020-05-09T01:32:03Z)
• Firms Default Prediction with Machine Learning [3.8415806547786735]
  An earlier sign that a company has financial difficulties and may eventually bankrupt is going in emphdefault, loosely speaking. Our study uses, for the first time according to our knowledge, a very large database of granular credit data from the Italian Central Credit Register of Bank of Italy. We find that ensemble techniques and random forest provide the best results, corroborating the findings of Barboza et al.
  arXiv  Detail & Related papers  (2020-02-17T10:09:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.