Prediction of the MSCI EURO index based on fuzzy grammar fragments extracted from European Central Bank statements

Milea, Viorel; Sharef, Nurfadhlina Mohd; Almeida, Rui Jorge; Kaymak, Uzay; Frăsincar, Flavius

doi:10.1109/socpar.2010.5686083

Cited by 18 publications

(4 citation statements)

References 14 publications

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“…Regarding the research using the documents published by financial institutions and companies, Milea et al ( 2010 ) predicted the Morgan Stanley Capital International (MSCI) euro index (upwards, downwards, or constant) based on fuzzy grammar fragments extracted from a report published by the European Central Bank. Brown and Tucker ( 2011 ) showed that the market price reaction weakens despite the increase in disclosed information, and the usefulness of management's discussion and analysis (MD&A) has declined.…”

Section: Related Studymentioning

confidence: 99%

Forecasting Stock Price Trends by Analyzing Economic Reports With Analyst Profiles

Suzuki¹,

Sakaji²,

Izumi³

et al. 2022

Front. Artif. Intell.

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This article proposes a methodology to forecast the movements of analysts' estimated net income and stock prices using analyst profiles. Our methodology is based on applying natural language processing and neural networks in the context of analyst reports. First, we apply the proposed method to extract opinion sentences from the analyst report while classifying the remaining parts as non-opinion sentences. Then, we employ the proposed method to forecast the movements of analysts' estimated net income and stock price by inputting the opinion and non-opinion sentences into separate neural networks. In addition to analyst reports, we input analyst profiles to the networks. As analyst profiles, we used the name of an analyst, the securities company to which the analyst belongs, the sector which the analyst covers, and the analyst ranking. Consequently, we obtain an indication that the analyst profile effectively improves the model forecasts. However, classifying analyst reports into opinion and non-opinion sentences is insignificant for the forecasts.

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Section: Related Studymentioning

confidence: 99%

Forecasting Stock Price Trends by Analyzing Economic Reports With Analyst Profiles

Suzuki¹,

Sakaji²,

Izumi³

et al. 2022

Front. Artif. Intell.

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“…Ito et al proposed a text-visualizing neural network model to visualize online financial textual data and a model called gradient interpretable neural network (GINN) to clarify why a neural network model make the result in financial text mining [18,19]. Milea et al predicted the MSCI EURO index (3 classes: up, down, and stay) based on the fuzzy grammar of European Central Bank (ECB) statement text [20]. Xing et al proposed a method for building a semantic vine structure among companies on the US stock markets using text mining and extended this method by implementing the similarity between all pairs of vector representations of descriptive stock company profiles into an asset allocation task to reveal the dependence structure of stocks and optimize financial portfolios [21].…”

Section: Related Workmentioning

confidence: 99%

Related Stocks Selection with Data Collaboration Using Text Mining

et al. 2019

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We propose an extended scheme for selecting related stocks for themed mutual funds. This scheme was designed to support fund managers who are building themed mutual funds. In our preliminary experiments, building a themed mutual fund was found to be quite difficult. Our scheme is a type of natural language processing method and based on words extracted according to their similarity to a theme using word2vec and our unique similarity based on co-occurrence in company information. We used data including investor relations and official websites as company information data. We also conducted several other experiments, including hyperparameter tuning, in our scheme. The scheme achieved a 172% higher F1 score and 21% higher accuracy than a standard method. Our research also showed the possibility that official websites are not necessary for our scheme, contrary to our preliminary experiments for assessing data collaboration.

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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

Self Cite

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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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Prediction of the MSCI EURO index based on fuzzy grammar fragments extracted from European Central Bank statements

Cited by 18 publications

References 14 publications

Forecasting Stock Price Trends by Analyzing Economic Reports With Analyst Profiles

Forecasting Stock Price Trends by Analyzing Economic Reports With Analyst Profiles

Related Stocks Selection with Data Collaboration Using Text Mining

Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data

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