Divergence of Sentiment and Stock Market Trading

Siganos, Antonios; Vagenas‐Nanos, Evangelos; Verwijmeren, Patrick

doi:10.2139/ssrn.2462140

Cited by 15 publications

(20 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the studies in finance, the most common used sources have been press news, both general and specialised press (García, 2013;Li et al, 2014a;Li et al, 2014b;Mo et al, 2016), and the Internet (Das & Che, 2007;Zhang et al, 2011;Sprenger et al, 2014a;Siganos et al, 2017). Press news have traditionally been an important information source, and these texts are relevant to know the general conditions of the economy, the financial markets, and the industries and companies.…”

Section: Textual Analysis In Finance and Accountingmentioning

confidence: 99%

“…The other most used source of textual sentiment in finance is the Internet, especially social networks (Das & Chen, 2007;Bollen et al, 2011;Zhang et al, 2011;Chen et al, 2014;Sprenger et al, 2014a;Sprenger et al, 2014b;Piñeiro-Chousa et al, 2017;Siganos et al, 2017). The disclosure of information about finance and accounting on the Internet is a potentially useful source for sentiment analysis, since there is a wide and diverse audience who interacts in an active (writing) or passive (reading) way on the Internet and in social networks.…”

Section: Sentiment Analysis: Concept and Use In Finance And Accountingmentioning

confidence: 99%

See 1 more Smart Citation

Textual Analysis and Sentiment Analysis in Accounting

Cabedo¹,

Huguet²

2021

Rev. contab.

View full text Add to dashboard Cite

A pesar del relativamente escaso uso de técnicas de análisis textual y de análisis del sentimiento en finanzas y contabilidad, éstas tienen un gran potencial en contabilidad, tanto por el elevado volumen de documentos utilizados para la comunicación de información financiera como por el crecimiento en el uso de herramientas digitales y medios de comunicación social. En este sentido, estas técnicas de análisis pueden ayudar a los investigadores a analizar pistas ocultas o buscar información adicional a la observada a través de los estados financieros, incrementando la cantidad y calidad de la información tradicionalmente utilizada, y proporcionando una nueva perspectiva de análisis. Por ello, el objetivo de este estudio es realizar una revisión del uso del análisis textual y del análisis del sentimiento en contabilidad. Tras presentar los conceptos de análisis textual y análisis del sentimiento y justificar teóricamente su papel en la investigación en contabilidad, llevamos a cabo una revisión de la literatura previa en el uso de estas técnicas en finanzas y contabilidad y describimos las principales técnicas de análisis del sentimiento, así como el procedimiento a seguir para el uso de esta metodología. Finalmente, sugerimos tres líneas de investigación futura que pueden beneficiarse del uso del análisis textual y del análisis del sentimiento. In spite of the relatively scarce use of textual analysis and sentiment analysis techniques in finance and accounting, they have great potential in accounting, both because of the volume of documents used for the communication of information and due to the growth in the use of digital tools and social media. In that regard, these techniques of analysis may help researchers to analyse hidden clues or look for additional information to that one observed through financial information, increasing the quantity and quality of the information traditionally used, and providing a new perspective of analysis. The aim of this study is to review the use of textual analysis and sentiment analysis in accounting. After presenting the concepts of textual analysis and sentiment analysis and expose their interest in accounting, we perform a review of the previous literature on the use of these techniques in finance and accounting and describe the main techniques of sentiment analysis, as well as the procedure to be followed for the use of this methodology. Finally, we suggest three lines of future research that may benefit from the use of textual and sentiment analysis.

show abstract

Section: Textual Analysis In Finance and Accountingmentioning

confidence: 99%

Section: Sentiment Analysis: Concept and Use In Finance And Accountingmentioning

confidence: 99%

Textual Analysis and Sentiment Analysis in Accounting

Cabedo¹,

Huguet²

2021

Rev. contab.

View full text Add to dashboard Cite

show abstract

“…Siganos, Vagenas‐Nanos, and Verwijmeren () introduced a new measure based on the divergence of sentiment. This Facebook measure is calculated by the distance between people of 20 countries with positive and negative sentiments.…”

Section: Related Workmentioning

confidence: 99%

Big data tools for Islamic financial analysis

Mnif

Jarboui

Hassan

et al. 2020

Intell Sys Acc Fin Mgmt

View full text Add to dashboard Cite

Summary Behavioural science states that emotions, principles and the manner of thinking can affect the behaviour of individuals and even investors in their decision making on financial markets. In this paper, we have tried to measure the investor sentiment by three means of big data. The first is based on a search query of a list of words related to Islamic context. The second is inferred from the engagement degree on social media. The last measure of sentiment is built, based on the Twitter API classified into positive and negative directions by a machine learning algorithm based on the naive Bayes method. Then, we investigate whether these sensations and emotions have an impact on the market sentiment and the price fluctuations by means of a vector autoregression model and Granger causality analysis. In the final step, we apply the agent‐based simulation by means of the sequential Monte Carlo method with the control of our Twitter measure on Islamic index returns. We show, then, that the three social media sentiment measures present a remarkable impact on the contemporaneous and lagged returns of the different Islamic assets studied. We also give an estimation of the parameters of the latent variables relative to the agent model studied.

show abstract

“…A growing body of evidence generated by decision science scholars demonstrates that investors' systematic judgment errors induced by their biological motivates result in stocks' divergence from randomness. According to this view, investors' motivational state may be shaped by many controlling stimuli such as the power of mass psychology, risk tolerance and misguided thinking (Engelberg & Parsons, 2016;Nofsinger, 2016;Siganos, Vagenas-Nanos, & Verwijmeren, 2017). In another word, it is believed that the physiological variables associated with the autonomic nervous system are highly correlated with market events, even for highly experienced traders.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Market Moods and Network Dynamics of Stock Returns: The Bipolar Behavior

Ajirlou

Esmalifalak

et al. 2019

Journal of Behavioral Finance

View full text Add to dashboard Cite

We show that a simple mood-separable preference in a network study of stock returns captures a variety of stylized facts regarding stocks' provisional (ab)normal behavior. These behaviors are articulated in a multi-state complete Euclidean network model that specifies the existence, direction and magnitude of a self-organized dynamics for each individual stock during abnormal market moods. In the empirical setting, we apply suggested model along with two established visual approaches (MDS and AHC) for benchmark purposes. Results reveal different levels of erratic return dynamics for each stock and the entire market in different abnormal market moods. We model and interpret these self-organized dynamics as evidence of stocks' and market's bipolar behavior.

show abstract

Divergence of Sentiment and Stock Market Trading

Cited by 15 publications

References 48 publications

Textual Analysis and Sentiment Analysis in Accounting

Textual Analysis and Sentiment Analysis in Accounting

Big data tools for Islamic financial analysis

Market Moods and Network Dynamics of Stock Returns: The Bipolar Behavior

Contact Info

Product

Resources

About