Detecting a Risk Signal in Stock Investment Through Opinion Mining and Graph-Based Semi-Supervised Learning

Yoon, Boram; Jeong, Yujin; Kim, Sunhye

doi:10.1109/access.2020.3021182

Cited by 16 publications

(5 citation statements)

References 51 publications

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“…Opinion mining on social networks is already an established field aged more than one decade [5]. It has been developed particularly in relation with financial transaction [6], marketing strategies [7], and security [8]. Opinions may be expressed as and detected by sentiments [9,10], emotions [11], collocations, associations, and other relationships between linguistic items and diffuse sentiments that can be inferred from texts ("sentiment orientation in a structured form from a set of unstructured data" [10]).…”

Section: Research Contextmentioning

confidence: 99%

Twitter's Mirroring of the 2022 Energy Crisis: What It Teaches Decision-Makers - A Preliminary Study

TEODORESCU,

PIRNAU

2023

ROMJIST

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The paper pertains to the field of opinion mining on social networks in view of decision-making. It was designed to determine the effect of the energy crises on the socio-ethical and ethical worries and concerns related to the role of Information and Communication Technology (ICT) in the crisis, as reflected on Twitter. Knowing public opinions during crises is a key component of decision-making. Other purposes include the determination of the perception of the correlations between these categories of concerns and the determination of specificities of distributions of such perceptions, as represented by words. A mixture of tools from lexical, topical, and semantical analysis were applied for generating the bag of words and for analyzing a set of tweets collected during the initial stage of an energy crisis, with special emphasis to coherence indices. The results confirm that energy crises may be associated with such concerns, as expected, but the association probability is low for the 2022 energy crisis, indicating at least a partial success of the mitigation decisions of the respective authorities. Some surprising relationships also occurred from the study. The paper is dedicated to Acad. Florin Gheorghe Filip, at the 75th anniversary.

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Section: Research Contextmentioning

confidence: 99%

Twitter's Mirroring of the 2022 Energy Crisis: What It Teaches Decision-Makers - A Preliminary Study

TEODORESCU,

PIRNAU

2023

ROMJIST

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“…Support Vector Machine (SVM) [36] Decision Tree (DT) [37] Logistic Regression (LR) [38] Naive Bayes (NB) [39] K Nearest Neighbor (KNN) [40] Random Forest (RF) [41] Adaptive Boosting (ADA BOOST) [42] Extreme Gradient Boosting (XG BOOST) [43] Artificial Neural Network (ANN) [44] https://doi.org/10.1371/journal.pone.0286362.t003…”

Section: Models Referencementioning

confidence: 99%

A performance comparison of machine learning models for stock market prediction with novel investment strategy

Khan,

Shah,

Ali

et al. 2023

PLoS ONE

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Stock market forecasting is one of the most challenging problems in today’s financial markets. According to the efficient market hypothesis, it is almost impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to predict the direction of the stock market. First, these models are trained and validated using the traditional methodology on a historic data captured over a 1-day time frame. Then, the models are trained using the proposed methodology. Following the traditional methodology, Logistic Regression achieved the highest accuracy of 85.51% followed by XG Boost and Random Forest. With the proposed strategy, the Random Forest model achieved the highest accuracy of 91.27% followed by XG Boost, ADA Boost and ANN. In the later part of the paper, it is shown that only classification report is not sufficient to validate the performance of ML model for stock market prediction. A simulation model of the financial market is used in order to evaluate the risk, maximum draw down and returns associate with each ML model. The overall results demonstrated that the proposed strategy not only improves the stock market returns but also reduces the risks associated with each ML model.

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“…In formula (5), l i represents the optimal solution obtained in the i th iteration; x i represents the optimal solution obtained during iteration; t indicates the time required. According to the abnormal data results, formulate the corresponding university financial budget management model, as shown in Figure 4.…”

Section: E Realization Of Evaluation Of College Financial Internal Co...mentioning

confidence: 99%

“…For the nancial internal control management in college, innovation and reform of the evaluation system of nancial internal control can help to realize the e ective utilization of funds and make the nancial management more e cient and accurate, and further improving the ne management of nancial work [3,4]. Under the new situation, the college nancial management is facing great nancial risks, so it is urgent to strengthen the nancial management [5]. However, there is no full understanding of nancial risks in the current college nancial management, and the corresponding measures have not been taken to deal with the external economic environment and potential nancial management crisis [6].…”

Section: Introductionmentioning

confidence: 99%

Evaluation Method of Financial Internal Control in Colleges and Universities Based on Artificial Intelligence Technology

Guo¹

2022

Mobile Information Systems

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The internal financial control of colleges and universities is the main tool to restrain the behavior of financial managers. With the improvement of the economic operation efficiency of colleges and universities, the application effect of internal control management system is poor. Therefore, the evaluation method of the university financial internal control based on artificial intelligence technology is proposed. First put forward the artificial intelligence technology in the application of financial internal control in colleges and universities, and then construct the financial internal organizational structure, on this basis, to meet the enormous workload processing requirements, the creation of financial internal control evaluation in colleges and universities, the chain at the same time use of financial system of general function, the university financial internal control evaluation data output to the financial system. The data acquisition program is used to obtain the relevant data in the evaluation report so as to realize the evaluation of internal financial control in colleges and universities under the artificial intelligence technology. The experimental results show that the evaluation acceleration is faster with the increase of the evaluation time of the key elements. The evaluation effect is the best; the closest to the ideal value of 0.01, and the error detection rate curve is relatively stable, with good effect.

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Detecting a Risk Signal in Stock Investment Through Opinion Mining and Graph-Based Semi-Supervised Learning

Cited by 16 publications

References 51 publications

Twitter's Mirroring of the 2022 Energy Crisis: What It Teaches Decision-Makers - A Preliminary Study

Twitter's Mirroring of the 2022 Energy Crisis: What It Teaches Decision-Makers - A Preliminary Study

A performance comparison of machine learning models for stock market prediction with novel investment strategy

Evaluation Method of Financial Internal Control in Colleges and Universities Based on Artificial Intelligence Technology

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