Information Theoretic Causality Detection between Financial and Sentiment Data

Scaramozzino, Roberta; Cerchiello, Paola; Aste, Tomaso

doi:10.3390/e23050621

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…Transfer entropy has been increasingly adopted by scientific community to measure the signal flowing to and from the stock market. The authors of [ 36 ] found a strong causal flow of information between the prices and sentiments of different studied companies, as well as flow in directions from prices to sentiment and sentiment to prices in the top 50 S&P companies for the period of 2018–2020; in the present study, we found a separation of negative and positive sentiments for a selection of 24 companies for a 10-year period that included a full economic cycle, with periods of high stress and high positive growth.…”

Section: Discussionmentioning

confidence: 99%

Twitter Sentiment Analysis and Influence on Stock Performance Using Transfer Entropy and EGARCH Methods

Mendoza-Urdiales

Núñez-Mora

Santillán‐Salgado

et al. 2022

Entropy

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Financial economic research has extensively documented the fact that the impact of the arrival of negative news on stock prices is more intense than that of the arrival of positive news. The authors of the present study followed an innovative approach based on the utilization of two artificial intelligence algorithms to test that asymmetric response effect. Methods: The first algorithm was used to web-scrape the social network Twitter to download the top tweets of the 24 largest market-capitalized publicly traded companies in the world during the last decade. A second algorithm was then used to analyze the contents of the tweets, converting that information into social sentiment indexes and building a time series for each considered company. After comparing the social sentiment indexes’ movements with the daily closing stock price of individual companies using transfer entropy, our estimations confirmed that the intensity of the impact of negative and positive news on the daily stock prices is statistically different, as well as that the intensity with which negative news affects stock prices is greater than that of positive news. The results support the idea of the asymmetric effect that negative sentiment has a greater effect than positive sentiment, and these results were confirmed with the EGARCH model.

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Section: Discussionmentioning

confidence: 99%

Twitter Sentiment Analysis and Influence on Stock Performance Using Transfer Entropy and EGARCH Methods

Mendoza-Urdiales

Núñez-Mora

Santillán‐Salgado

et al. 2022

Entropy

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“…They show that the TDMI analysis is applicable in high dimensional complex systems and have applied it to neural data to show the existence of 𝜃-driving neuron in rat hippocampus that was reported in mouse hippocampus previously. [53] investigate causal relationship between sentiment about a company in social media and it's stock price. They use transfer entropy to detect strength and direction of transfer of information between the sentiment and prices.…”

Section: Data Science and Intelligent Computing Techniquesmentioning

confidence: 99%

Causality in Time-Series: A Short Review

Palshikar,

Apte,

Vaishampayan

et al. 2023

Data Science and Intelligent Computing Techniques

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The study of causal relations has been proved to be significant in acquisition, understanding, and representation of human knowledge across physical and biological sciences, engineering, social sciences, economics. Identifying or inferring causal relations from empirical data is a crucial step in knowledge acquisition. For study of causality, time is an important factor, as the effect will always occur after its cause, and, often, within stipulated amount of time. Thus, time-series data is considered ideal for causal inference. In this chapter we review various notions of causality in time-series data. Granger Causality is the most widely used notion for extraction of causal relations from time-series data, and has been extended to non-linear, conditional and multivariate scenarios. We also review some of the other notions of causality like Dynamic Bayesian network and Mutual Information related causal extraction techniques. We briefly touch upon Sir Austin Bradford Hill’s criteria for causality, which puts forward nine viewpoints for concluding an association relation into causality. Some applications of causality and a list of software tools are also provided. The chapter will provide sufficient information for the readers looking for a quick introduction to the field.

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“…Differently, those costs would be mainly faced by shareholders, bondholders and depositors in case of bail-in or governments and, ultimately, taxpayers in case of bail-out. Second, the failure of a bank can induce failures of other banks or of part of the financial system (Kaminsky et al, 2003 ; Scaramozzino et al, 2021 ). Understanding the determinants of a single bank failure may thus help to understand the determinants of financial systemic risks, were they due to microeconomic idiosyncratic factors or to macroeconomic imbalances.…”

Section: Introductionmentioning

confidence: 99%

Assessing Banks' Distress Using News and Regular Financial Data

Cerchiello

Nicola²,

Rönnqvist³

et al. 2022

Front. Artif. Intell.

Self Cite

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In this paper, we focus our attention on leveraging the information contained in financial news to enhance the performance of a bank distress classifier. The news information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with the issues related to Natural Language interpretation and to the analysis of news media. Among the different models proposed for such purpose, we investigate a deep learning approach. The methodology is based on a distributed representation of textual data obtained from a model (Doc2Vec) that maps the documents and the words contained within a text onto a reduced latent semantic space. Afterwards, a second supervised feed forward fully connected neural network is trained combining news data distributed representations with standard financial figures in input. The goal of the model is to classify the corresponding banks in distressed or tranquil state. The final aim is to comprehend both the improvement of the predictive performance of the classifier and to assess the importance of news data in the classification process. This to understand if news data really bring useful information not contained in standard financial variables.

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Information Theoretic Causality Detection between Financial and Sentiment Data

Cited by 9 publications

References 27 publications

Twitter Sentiment Analysis and Influence on Stock Performance Using Transfer Entropy and EGARCH Methods

Twitter Sentiment Analysis and Influence on Stock Performance Using Transfer Entropy and EGARCH Methods

Causality in Time-Series: A Short Review

Assessing Banks' Distress Using News and Regular Financial Data

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