Predicting financial performance for listed companies in Thailand during the transition period: A class-based approach using logistic regression and random forest algorithm

Supsermpol, Pornpawee; Huynh, Van Nam; Thajchayapong, Suttipong; Chiadamrong, Navee

doi:10.1016/j.joitmc.2023.100130

Cited by 23 publications

(9 citation statements)

References 42 publications

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“…Relative importance refers to the degree of importance of one variable relative to the others in the process of model fitting. According to the method of Supsermpol et al 49 , the relative importance of the variable can be assessed by measuring the decrease of the variable after its introduction. If the relative importance of a variable is high, it has a stronger influence in predicting the digital transformation of media companies.…”

Section: Methodsmentioning

confidence: 99%

Research on predicting the driving forces of digital transformation in Chinese media companies based on machine learning

Wang,

Li,

Zhao

et al. 2024

Sci Rep

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Chinese media companies are facing opportunities and challenges brought about by digital transformation. Media economics takes the evaluation of the business results of media companies as the main research topic. However, overcoming the internal differences in the industry and comprehensively predicting the digital transformation of Chinese media companies from multiple dimensions has become an important issue to be understood. Based on the “TOE-I” theoretical framework, this study innovatively uses machine learning methods to predict the digital transformation of Chinese media companies and to analyze specific modes of the main driving factors affecting the digital transformation, using data from China’s A-share-listed media companies from 2010 to 2020. The study found that environmental drivers can most effectively and accurately predict the digital transformation of Chinese media companies. Therefore, under sustained and stable economic and financial policies, guiding inter-industry competition and providing balanced digital infrastructure conditions are keys to bridging internal barriers in the media industry and promoting digital transformation. In the process of transformation from traditional content to digital production, media companies should focus on policy changes, economic benefits, the decision-making role of core managers, and the training and preservation of digital technology talent.

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Section: Methodsmentioning

confidence: 99%

Research on predicting the driving forces of digital transformation in Chinese media companies based on machine learning

Wang,

Li,

Zhao

et al. 2024

Sci Rep

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“…Financial statements are the final part of the accounting process that plays an important role in measuring and evaluating a company's performance. Companies in Indonesia, especially those that go public, must make financial statements periodically (Mujari 2019;Supsermpol et al 2023;.…”

Section: Introductionmentioning

confidence: 99%

Current ratio, return on assets and company size to capital structure in manufacturing companies in the food and beverage subsector

Oktaviani,

Nurhayati,

Safitri

et al. 2024

ijafibs

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This study investigates the interaction between the current ratio, return on assets, company size and capital structure in Indonesia's food and beverage sector. The goal is to gain insight into how these factors interact and affect the overall value of a company in a particular region and industry. This analysis uses quantitative methods based on financial data and relevant metrics for a sample of 15 food and beverage sector companies operating in Indonesia. This study examines the relationship between the current ratio, return on assets, and company size to capital structure through statistical analysis and linear regression modelling techniques with SPSS version 26. The results showed that the current ratio did not significantly affect the capital structure. Return on assets does not have a significant effect on the capital structure. Then, the size of the company has a significant influence on the capital structure. The current ratio, return on set, and company size affect the capital structure simultaneously. The findings contribute to the existing literature on company valuations by providing empirical evidence specific to the food and beverage sector. The research has implications for policymakers, investors, and managers, as it offers valuable insights into the factors driving shaping capital structures in the industry, helping stakeholders make decisions and strategies to improve performance and competitiveness. Provide benefits in the context of financial management

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“…3, June 2024: 3177-3186 3178 the meticulous calibration of its hyperparameters that wield the power to intricately fine-tune the model's performance. Enter Bayesian optimization, a sophisticated technique that adroitly navigates the labyrinthine hyperparameter landscape, honing the algorithm's precision by minimizing the mean squared error (MSE), a pivotal metric in predictive assessment [6].…”

Section: Introductionmentioning

confidence: 99%

Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset

Rhouas,

El Attaoui,

El Hami

2024

IJECE

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In the ever-evolving landscape of global commerce, marked by the convergence of digital transformation and borderless markets, this research addresses the intricate challenges of currency exchange and risk management. Leveraging Bayesian optimization, the study fine-tunes the random forest algorithm using the extensive Klarna E-commerce dataset. Through systematic analysis, the research uncovers insights into managing currency prediction amid dynamic global markets. Emphasizing the role of Bayesian optimization parameters, the study reveals nuanced trade-offs in model performance. Notably, the optimal simulation, conducted with 14 iterations, 1 job, and a random state set to 684, exhibits a standout performance, showcasing a negative mean squared error (MSE) of approximately -0.9891 and an accuracy rate of 74.63%. The primary objective is to assess the impact of Bayesian optimization in enhancing the random forest algorithm's predictive capabilities, particularly in currency prediction within international e-commerce. These findings offer refined strategies for businesses navigating the intricate landscape of global finance, empowering decision-making through a comprehensive understanding of data, algorithms, and challenges in international commerce.

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Predicting financial performance for listed companies in Thailand during the transition period: A class-based approach using logistic regression and random forest algorithm

Cited by 23 publications

References 42 publications

Research on predicting the driving forces of digital transformation in Chinese media companies based on machine learning

Research on predicting the driving forces of digital transformation in Chinese media companies based on machine learning

Current ratio, return on assets and company size to capital structure in manufacturing companies in the food and beverage subsector

Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset

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