Conventional and neural network target‐matching methods dynamics: The information technology mergers and acquisitions market in the USA

Anagnostopoulos, Yiannis; Rizeq, Anas

doi:10.1002/isaf.1492

Cited by 6 publications

(4 citation statements)

References 86 publications

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“…Technology-driven financial innovation aims to use modern scientific and technological achievements to transform or innovate financial products, business models, business processes, etc., and promote financial development to improve quality and efficiency [6][7]. Artificial intelligence extends human intelligence to computer systems, allowing computers to perform biometric recognition, speech recognition, natural language processing, machine learning, etc.…”

Section: Technological and Financial Genesmentioning

confidence: 99%

The Dominant Force of Science and Technology Financial Genes in the Great Power Technology Game

2023

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Technology is the primary productive force and drives the development of the world economy. Traditional science and technology(ST) finance research methods are usually based on experience and qualitative analysis, lack the support of quantitative data, and are unable to provide accurate and reliable prediction and decision support, and the subjectivity of experts and the slowness in processing complex data lead to certain errors in people's judgment and analysis of gene dominance. To this end, this article needs to use advanced technologies such as big data analysis technology and artificial intelligence to model and analyze large-scale complex data. This article aims to analyze the relationship between ST finance and competition among major powers, reveal the genetically dominant role of ST finance in shaping the landscape of ST competition among major powers, and explore its importance in global ST competition. This article conducts an in-depth analysis of the genetic dominance of ST finance and builds a relevant model. This article uses big data technology and analysis methods to calculate and draw the variables of technology finance and innovation, and conducts data analysis and comparison based on the graphics. Investigating the experimental data shows that in the F test, its statistic is 18.76 and the p-value is 0.

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Section: Technological and Financial Genesmentioning

confidence: 99%

The Dominant Force of Science and Technology Financial Genes in the Great Power Technology Game

2023

accaf

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“…PSO finds the best value through particle interaction, but when the search space is very high, its convergence speed becomes very slow near the global optimal value. In this paper, PSO convergence related analysis is divided into four categories: particle motion stability analysis, particle motion trajectory analysis, algorithm local convergence analysis and expected time analysis of the first hit [7][8].…”

Section: Convergence Of Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Dynamic Financial Asset Allocation Strategy Based on Particle Swarm Optimization Algorithm

Yan¹

2023

Proceedings of the 3rd International Conference on Big Data Economy and Information Management, BDEIM 2022, December 2-3, 2022,

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With the continuous improvement and development of the financial market, the allocation of financing assets has become a hot topic. In the financial market, the imbalance of capital supply and demand is very common. Therefore, in order to grasp the financial dynamics in time, it is necessary to conduct relevant analysis on its asset allocation. In order to improve the accuracy of calculation, this paper proposes the application of particle swarm optimization algorithm. This paper mainly uses digital modeling and data comparison to study the dynamic financial asset allocation strategy. The experimental results show that the TSVL-DPM model has the strongest ability to predict asset allocation, and the minimum error is 2.01.

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“…This means that no bankrupting companies were detected by the model developed by Tumpach et al (2020). The comparable predictive methodology was used with NN models in the past to predict mergers and acquisitions, where the error rate achieved, when using a split ratio of 70/30, was 34.8% (Anagnostopoulos & Rizeq, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

Papíková

Papík

2022

Intell Sys Acc Fin Mgmt

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Small and medium-sized enterprises are the pillars of an economy, and their poor performance has a negative impact on living standards of population and country development. This study analyzes real-life data of 89,851 small and medium-sized enterprises, out of which 295 have declared bankruptcy. The analysis is performed via 27 financial ratios. The study framework combines seven classifications and three resampling and seven feature selection methods. Out of all classification methods applied, CatBoost has achieved the best results for all combinations of resampling and feature selection methods. CatBoost surpassed the results of other classification methods for the area under curve parameter, achieving a value of 99.95%. The application of resampling methods on different classification models has not identified a statistically significant level of improvement in any of the resampling methods. This finding has also been observed for feature selection methods. Based on these findings, we assume that individual resampling and feature selection methods do not improve model performance compared with the original imbalanced sample's results.Our results suggest that, even though the data sample may be significantly imbalanced with a minority of bankrupt companies, most classification algorithms can handle this imbalance and achieve interesting results. Moreover, our findings provide broad practical application for all stakeholders who could need to detect bankrupting companies.

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Conventional and neural network target‐matching methods dynamics: The information technology mergers and acquisitions market in the USA

Cited by 6 publications

References 86 publications

The Dominant Force of Science and Technology Financial Genes in the Great Power Technology Game

The Dominant Force of Science and Technology Financial Genes in the Great Power Technology Game

Dynamic Financial Asset Allocation Strategy Based on Particle Swarm Optimization Algorithm

Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

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