Computational Intelligence and Financial Markets: A Survey and Future Directions

Cavalcante, Rodolfo C.; Brasileiro, Rodrigo C.; Souza, Victor L. F.; Nóbrega, Jarley Palmeira; Oliveira, Adriano L. I.

doi:10.1016/j.eswa.2016.02.006

Cited by 479 publications

(244 citation statements)

References 88 publications

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“…In the application of computational intelligence to the financial market, mining textual content (financial news, financial reports , and even information in micro-blogs) is considered a relevant source of information for predicting future market behaviour [10]. To do so, researches have proposed the extraction of relevant features from the textual content.…”

Section: Related Workmentioning

confidence: 99%

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Twitter permeability to financial events: an experiment towards a model for sensing irregularities

Vilas

Redondo

Crockett

et al. 2018

Multimed Tools Appl

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There is a general consensus of the good sensing and novelty characteristics of Twitter as an information media for the complex financial market. This paper investigates the permeability of Twittersphere, the total universe of Twitter users and their habits, towards relevant events in the financial market. Analysis shows that a general purpose social media is permeable to financial-specific events and establishes Twitter as a relevant feeder for taking decisions regarding the financial market and event fraudulent activities in that market. However, the provenance of contributions, their different levels of credibility and quality and even the purpose or intention behind them should to be considered and carefully contemplated if Twitter is used as a single source for decision taking. With the overall aim of this research, to deploy an architecture for real-time monitoring of irregularities in the financial market, this paper conducts a series of experiments on the level of permeability and the permeable features of Twitter in the event of one of these irregularities. To be precise, Twitter data is collected concerning an event comprising of a specific financial action on the 27th January 2017: the announcement about the merge of two companies Tesco PLC and Booker Group PLC, listed in the main market of the London Stock Exchange

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Section: Related Workmentioning

confidence: 99%

“…Cavalcante et al [10] introduced a comprehensive review on the usage of computational intelligence in the financial market. The authors also suggest a systematic proposal for the building of intelligent trading systems.…”

Section: Related Workmentioning

confidence: 99%

Twitter permeability to financial events: an experiment towards a model for sensing irregularities

Vilas

Redondo

Crockett

et al. 2018

Multimed Tools Appl

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“…The potential and capabilities of these analytics have been successfully demonstrated in many domains such as politics [21], environmental communication [24], nancial market analysis [5], health care [33] and marketing [32]. Chung and Zeng [6], for instance, use network and sentiment analysis on Twitter to investigate the discussion on the U.S. immigration and border security.…”

Section: Information Extraction For Web Intelligence and Big Data Appmentioning

confidence: 99%

Mitigating linked data quality issues in knowledge-intense information extraction methods

Weichselbraun

Kuntschik

2017

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics

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Advances in research areas such as named entity linking and sentiment analysis have triggered the emergence of knowledge-intensive information extraction methods that combine classical information extraction with background knowledge from the Web. Despite data quality concerns, linked data sources such as DBpedia, GeoNames and Wikidata which encode facts in a standardized structured format are particularly attractive for such applications.This paper addresses the problem of data quality by introducing a framework that elaborates on linked data quality issues relevant to di erent stages of the background knowledge acquisition process, their impact on information extraction performance and applicable mitigation strategies. Applying this framework to named entity linking and data enrichment demonstrates the potential of the introduced mitigation strategies to lessen the impact of di erent kinds of data quality problems. An industrial use case that aims at the automatic generation of image metadata from image descriptions illustrates the successful deployment of knowledge-intensive information extraction in real-world applications and constraints introduced by data quality concerns.

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“…In existing literature, computational intelligence techniques have been investigated in the field of wave energy [20], financial market [21], and power quality disturbance [22,23]. However, the published review articles about condition monitoring and fault diagnosis have a limited scope, by focusing either on fault feature extraction and classification [24], or on rotating machinery prediction techniques [5,25].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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Computational Intelligence and Financial Markets: A Survey and Future Directions

Cited by 479 publications

References 88 publications

Twitter permeability to financial events: an experiment towards a model for sensing irregularities

Twitter permeability to financial events: an experiment towards a model for sensing irregularities

Mitigating linked data quality issues in knowledge-intense information extraction methods

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

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