Stories that (are) Move(d by) Markets: A Causal Exploration of Market Shocks and Semantic Shifts across Different Partisan Groups

• URL: http://arxiv.org/abs/2502.14497v1
• Date: Thu, 20 Feb 2025 12:26:56 GMT
• Title: Stories that (are) Move(d by) Markets: A Causal Exploration of Market Shocks and Semantic Shifts across Different Partisan Groups
• Authors: Felix Drinkall, Stefan Zohren, Michael McMahon, Janet B. Pierrehumbert,
• Abstract summary: 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.
• Score: 15.661920010658626
• License:
• Abstract: Macroeconomic fluctuations and the narratives that shape them form a mutually reinforcing cycle: public discourse can spur behavioural changes leading to economic shifts, which then result in changes in the stories that propagate. We show that shifts in semantic embedding space can be causally linked to financial market shocks -- deviations from the expected market behaviour. Furthermore, we show how partisanship can influence the predictive power of text for market fluctuations and shape reactions to those same shocks. We also provide some evidence that text-based signals are particularly salient during unexpected events such as COVID-19, highlighting the value of language data as an exogenous variable in economic forecasting. Our findings underscore the bidirectional relationship between news outlets and market shocks, offering a novel empirical approach to studying their effect on each other.

Related papers

• 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)
• Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices [20.574163667057476]
  Prior research using tweet data for stock market prediction faces three challenges. ECON has an adept tweets filter that efficiently extracts and decodes the vast array of tweet data. It discerns multi-level relationships among stocks, sectors, and macroeconomic factors through a self-aware mechanism in semantic space.
  arXiv  Detail & Related papers  (2023-12-04T22:27:43Z)
• A Causal Framework for Decomposing Spurious Variations [68.12191782657437]
  We develop tools for decomposing spurious variations in Markovian and Semi-Markovian models. We prove the first results that allow a non-parametric decomposition of spurious effects. The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine.
  arXiv  Detail & Related papers  (2023-06-08T09:40:28Z)
• The Battle of Information Representations: Comparing Sentiment and Semantic Features for Forecasting Market Trends [0.5249805590164902]
  We study whether semantic features in the form of contextual embeddings are more valuable than sentiment attributes for forecasting market trends. We consider the corpus of Twitter posts related to the largest companies by capitalization from NASDAQ and their close prices. Our results show that in the substantially prevailing number of cases, the use of sentiment features leads to higher metrics.
  arXiv  Detail & Related papers  (2023-03-24T18:30:15Z)
• Opinion Market Model: Stemming Far-Right Opinion Spread using Positive Interventions [4.635820333232681]
  We introduce a two-tier online opinion ecosystem model that considers both inter-opinion interactions and the role of positive interventions. We test OMM on two learning tasks, applying to two real-world datasets to predict attention market shares and uncover latent relationships between online items. OMM outperforms the state-of-the-art predictive models on both datasets and captures latent cooperation-competition relations.
  arXiv  Detail & Related papers  (2022-08-13T10:36:04Z)
• Forecasting with Economic News [0.9281671380673304]
  We consider only the text in the article that is semantically dependent on a term of interest. We find that several measures of economic sentiment track closely business cycle fluctuations. We also find that sentiment matters to explains the tails of the probability distribution across several macroeconomic variables.
  arXiv  Detail & Related papers  (2022-03-29T15:46:42Z)
• SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction [59.064925464991056]
  We propose one new prediction model named Social Soft Attention Graph Convolution Network (SSAGCN) SSAGCN aims to simultaneously handle social interactions among pedestrians and scene interactions between pedestrians and environments. Experiments on public available datasets prove the effectiveness of SSAGCN and have achieved state-of-the-art results.
  arXiv  Detail & Related papers  (2021-12-05T01:49:18Z)
• Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective [72.55093886515824]
  We introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables. We devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a causal graph. Experiment results on synthetic and real datasets show that our three proposed components significantly improve the robustness and reusability of the learned motion representations.
  arXiv  Detail & Related papers  (2021-11-29T18:59:09Z)
• REST: Relational Event-driven Stock Trend Forecasting [76.08435590771357]
  We propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks.
  arXiv  Detail & Related papers  (2021-02-15T07:22:09Z)
• Adversarial trading [91.3755431537592]
  We show that adversarial samples can be implemented in a trading environment and have a negative impact on certain market participants. This could have far reaching implications for financial markets either from a trading or a regulatory point of view.
  arXiv  Detail & Related papers  (2020-12-16T16:08:22Z)
• 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)

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.