Stock Prediction Using Event-Based Sentiment Analysis

Makrehchi, Masoud; Shah, Sameena; Liao, Wenhui

doi:10.1109/wi-iat.2013.48

Cited by 72 publications

(47 citation statements)

References 8 publications

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“…Mittermayer, 2004;Bollen et al, 2011), we too find such relationships; albeit in limited fashion. Our tests show that sentiment metrics can have material bearing on the forecasting of share price direction (resonating with, for example, Makrehchi, et al 2013). However, the predictability of volatility (c.f.…”

Section: Discussionmentioning

confidence: 67%

“…With a novel method, Makrehchi, Shah & Liao (2013), test the predictive ability of sentiments by retrospectively assessing what sentiments plausibly could have been able to predict just before large market movements. Using daily analysis, they show that sentiments have sufficient predictive value to enable supernormal trading profits.…”

Section: Predicting Markets With Social Media Sentiment Metricsmentioning

confidence: 99%

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The hasty wisdom of the mob: How market sentiment predicts stock market behavior

Checkley

Hign

Alles

2017

Expert Systems with Applications

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DisclaimerThe University of Gloucestershire has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material.The University of Gloucestershire makes no representation or warranties of commercial utility, title, or fitness for a particular purpose or any other warranty, express or implied in respect of any material deposited.The University of Gloucestershire makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights.The University of Gloucestershire accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement. PLEASE SCROLL DOWN FOR TEXT.The Hasty Wisdom of the Mob: How Market Sentiment Predicts Stock Market Behavior M. S. Checkleya, D. Añón Higónb, H. Allesc ABSTRACT We explore the ability of sentiment metrics, extracted from micro-blogging sites, to predict stock markets. We also add ess se ti e ts' p edi ti e ti e-horizons. The data o e logge s' feeli gs about five major stocks. Taking independent bullish and bearish sentiment metrics, granular to two minute intervals, we model their ability to forecast stock price direction, volatility, and traded volume. We find evidence of a causal link from sentiments to stock price returns, volatility and volume. The predictive time-horizon is minutes, rather than hours or days. We argue that diverse and high volume sentiment is more predictive of price volatility and traded volume than nearconsensus is predictive of price direction. Causality is ephemeral. In this sense, the crowd is more a hasty mob than a source of wisdom.

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Section: Discussionmentioning

confidence: 67%

Section: Predicting Markets With Social Media Sentiment Metricsmentioning

confidence: 99%

The hasty wisdom of the mob: How market sentiment predicts stock market behavior

Checkley

Hign

Alles

2017

Expert Systems with Applications

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“…Abbasi and Chen, (2007b;2007a) analysed hate and violence in extremist web forums using manually constructed affect lexicons. Financial index and stock prediction based on SA was explored by (Lee et al, 2013;Makrehchi et al, 2013;Milea et al, 2010;Zhang et al, 2011b).…”

Section: Implicit Featuresmentioning

confidence: 99%

Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data

Sharef¹,

Zin²,

Nadali³

2016

Journal of Computer Science

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Abstract:The ability to exploit public sentiment in social media is increasingly considered as an important tool for market understanding, customer segmentation and stock price prediction for strategic marketing planning and manoeuvring. This evolution of technology adoption is energised by the healthy growth in big data framework, which caused applications based on Sentiment Analysis (SA) in big data to become common for businesses. However, scarce works have studied the gaps of SA application in big data. The contribution of this paper is two-fold: (i) this study reviews the state of the art of SA approaches. including sentiment polarity detection, SA features (explicit and implicit), sentiment classification techniques and applications of SA and (ii) this study reviews the suitability of SA approaches for application in the big data frameworks, as well as highlights the gaps and suggests future works that should be explored. SA studies are predicted to be expanded into approaches that utilise scalability, possess high adaptability for source variation, velocity and veracity to maximise value mining for the benefit of the users.

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“…Moreover, Mao et al (2013) investigated the correlation relationship between Twitter volume spike and stock trading, and developed a method to monitor Twitter volume spikes in stock trading. Makrehchi et al (2013) extracted stock movements and textual information from Twitter and built a model with these labeled sentiment texts to predict the future stock movement. Arias et al (2014) developed a public sentiment indicator from Twitter messages and investigated two domains -stock market and movie box office revenue using two forecasting models.…”

Section: The Technology Perspective: Predicting Stock Prices In the Bmentioning

confidence: 99%

Systemic risk management and investment analysis with financial network analytics: research opportunities and challenges

Schwabe

2015

Financial Innovation

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Recent economic crises like the 2008 financial tsunami has demonstrated a critical need for better understanding of the topologies and various economic, social, and technical mechanisms of the increasingly interconnected global financial system. Such a system largely relies on the interconnectedness of various financial entities such as banks, firms, and investors through complex financial relationships such as interbank payment networks, investment relations, or supply chains. A network-based perspective or approach is needed to study various financial networks in order to improve or extend financial theories, as well as develop business applications. Moreover, with the advance of big data related technologies, and the availability of huge amounts of financial and economic network data, advanced computing technologies and data analytics that can comprehend such big data are also needed. We referred this approach as financial network analytics. We suggest that it will enable stakeholders better understand the network dynamics within the interconnected global financial system and help designing financial policies such as managing and monitoring banking systemic risk, as well as developing intelligent business applications like banking advisory systems. In this paper, we review the existing research about financial network analytics and then discuss its main research challenges from the economic, social, and technological perspectives. AbstractRecent economic crises like the 2008 financial tsunami has demonstrated a critical need for better understanding of the topologies and various economic, social, and technical mechanisms of the increasingly interconnected global financial system. Such a system largely relies on the interconnectedness of various financial entities such as banks, firms, and investors through complex financial relationships such as interbank payment networks, investment relations, or supply chains. A network-based perspective or approach is needed to study various financial networks in order to improve or extend financial theories, as well as develop business applications. Moreover, with the advance of big data related technologies, and the availability of huge amounts of financial and economic network data, advanced computing technologies and data analytics that can comprehend such big data are also needed. We referred this approach as financial network analytics. We suggest that it will enable stakeholders better understand the network dynamics within the interconnected global financial system and help designing financial policies such as managing and monitoring banking systemic risk, as well as developing intelligent business applications like banking advisory systems. In this paper, we review the existing research about financial network analytics and then discuss its main research challenges from the economic, social, and technological perspectives.

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Stock Prediction Using Event-Based Sentiment Analysis

Cited by 72 publications

References 8 publications

The hasty wisdom of the mob: How market sentiment predicts stock market behavior

The hasty wisdom of the mob: How market sentiment predicts stock market behavior

Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data

Systemic risk management and investment analysis with financial network analytics: research opportunities and challenges

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