Nostradamus: Weathering Worth

• URL: http://arxiv.org/abs/2212.05933v1
• Date: Thu, 8 Dec 2022 08:03:26 GMT
• Title: Nostradamus: Weathering Worth
• Authors: Alapan Chaudhuri, Zeeshan Ahmed, Ashwin Rao, Shivansh Subramanian, Shreyas Pradhan and Abhishek Mittal
• Abstract summary: We analyze associative correlation and causation between environmental elements and stock prices based on the US financial market. We take four natural disasters as a case study to observe their effect on the emotional state of people and their influence on the stock market.
• Score: 1.3506685059252612
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nostradamus, inspired by the French astrologer and reputed seer, is a detailed study exploring relations between environmental factors and changes in the stock market. In this paper, we analyze associative correlation and causation between environmental elements and stock prices based on the US financial market, global climate trends, and daily weather records to demonstrate significant relationships between climate and stock price fluctuation. Our analysis covers short and long-term rises and dips in company stock performances. Lastly, we take four natural disasters as a case study to observe their effect on the emotional state of people and their influence on the stock market.

Related papers

• Stories that (are) Move(d by) Markets: A Causal Exploration of Market Shocks and Semantic Shifts across Different Partisan Groups [15.661920010658626]
  We show that shifts in semantic embedding space can be causally linked to financial market shocks. We show how partisanship can influence the predictive power of text for market fluctuations and shape reactions to those same shocks.
  arXiv  Detail & Related papers  (2025-02-20T12:26:56Z)
• Indexing and Visualization of Climate Change Narratives Using BERT and Causal Extraction [2.7325857919669327]
  We use two natural language processing methods, BERT (Bidirectional Representations from Transformers) and causal extraction, to analyze newspaper articles on climate change. The novelty of the methodology could extract and quantify the causal relationships assumed by the newspaper's writers.
  arXiv  Detail & Related papers  (2024-08-03T11:05:41Z)
• Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates [49.93577170464313]
  We deal with air quality observations in a city-wide network of ground monitoring stations. We propose a conditioning block that embeds past and future covariates into the current observations. We find that conditioning on future weather information has a greater impact than considering past traffic conditions.
  arXiv  Detail & Related papers  (2024-04-08T09:13:16Z)
• Tweet Influence on Market Trends: Analyzing the Impact of Social Media Sentiment on Biotech Stocks [0.0]
  This study investigates the relationship between tweet sentiment across diverse categories: news, company opinions, CEO opinions, competitor opinions, and stock market behavior in the biotechnology sector. We analyzed historical stock market data for ten of the largest and most influential pharmaceutical companies alongside Twitter data related to COVID-19, vaccines, the companies, and their respective CEOs.
  arXiv  Detail & Related papers  (2024-01-26T15:43:27Z)
• Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests [47.285748922842444]
  We train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models.
  arXiv  Detail & Related papers  (2024-01-08T15:29:23Z)
• Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing [0.0]
  This study was to assess the viability of Twitter sentiments as a tool for predicting stock prices of major corporations such as Tesla, Apple. Our findings indicate that positivity, negativity, and subjectivity are the primary determinants of fluctuations in stock prices.
  arXiv  Detail & Related papers  (2023-08-31T21:20:58Z)
• Stock Price Prediction Under Anomalous Circumstances [81.37657557441649]
  This paper aims to capture the movement pattern of stock prices under anomalous circumstances. We train ARIMA and LSTM models at the single-stock level, industry level, and general market level. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98%.
  arXiv  Detail & Related papers  (2021-09-14T18:50:38Z)
• A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
  We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
  arXiv  Detail & Related papers  (2020-12-10T01:15:48Z)
• Analyzing Sustainability Reports Using Natural Language Processing [68.8204255655161]
  In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt to the changing climate context. This is reported via increasingly exhaustive reports, which cover many types of climate risks and exposures under the umbrella of Environmental, Social, and Governance (ESG) We present this tool and the methodology that we used to develop it in the present article.
  arXiv  Detail & Related papers  (2020-11-03T21:22:42Z)
• Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
  We find a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
  arXiv  Detail & Related papers  (2020-10-20T15:31:38Z)
• Impact of natural disasters on consumer behavior: case of the 2017 El Nino phenomenon in Peru [0.0]
  El Nino is an extreme weather event featuring unusual warming of surface waters in the eastern equatorial Pacific Ocean. This phenomenon is characterized by heavy rains and floods that negatively affect the economic activities of the impacted areas. We performed a multi-scale analysis of data associated with bank transactions involving credit and debit cards.
  arXiv  Detail & Related papers  (2020-08-11T17:44:39Z)

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.