Search Results - subject:"Logistic regression"

Is artificial intelligence really more accurate in predicting bankruptcy?

Letkovský, Stanislav; Jeňcová, Sylvia; … - In: International Journal of Financial Studies : open … 12 (2024) 1, pp. 1-19

. Predictions are generated using the classical logistic regression (LR) method as well as AI techniques, artificial neural networks …

Persistent link: https://www.econbiz.de/10014502270

Comparison of going concern models with and without corporate governance

Hammond, Paul; Opoku, Mustapha Osman; Kwakwa, Paul Adjei; … - In: Cogent business & management 10 (2023) 2, pp. 1-27

This study examines the effectiveness of going-concern prediction models by comparing models that incorporate corporate governance variables with models based solely on financial ratios. The aim was to compare the predictive power of going-concern models that combine corporate governance...

Persistent link: https://www.econbiz.de/10014503317

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

Pornpawee Supsermpol; Huynh, Van-Nam; Suttipong … - In: Journal of open innovation : technology, market, and … 9 (2023) 3, pp. 1-16

statistical method (logistic regression) in predicting financial performance. The findings also reveal that certain determinants …

Persistent link: https://www.econbiz.de/10014504880

Food values and purchase decisions in emerging markets : empirical evidence from Kenya

Femi-Oladunni, Opeyemi; Ruiz-Palomino, Pablo; … - In: Cogent business & management 10 (2023) 3, pp. 1-23

Research on consumer food value preferences has typically focused on consumers in the developed economy, with knowledge regarding food value preferences in emerging food markets being a grey spot. This study aims to fill this gap in the literature. Accordingly, the possible existence of consumer...

Persistent link: https://www.econbiz.de/10014505216

A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions

Afriyie, Jonathan Kwaku; Tawiah, Kassim; Pels, … - In: Decision analytics journal 6 (2023), pp. 1-12

. We study the performance of three different machine learning models: logistic regression, random forest, and decision …

Persistent link: https://www.econbiz.de/10014505835

An integrated machine learning and DEMATEL approach for feature preference and purchase intention modelling

Bhattacharjee, Debraj; Ramesh, Kandela; Jayaram, E. Srinivas - In: Decision analytics journal 6 (2023), pp. 1-13

learning techniques for classification, namely, C4.5, CART, and logistic regression. The result indicates the superiority of …

Persistent link: https://www.econbiz.de/10014505936

An estimation of the default probabilities of Spanish non-financial corporations and their application to evaluate public policies

Blanco, Roberto; Fernández, Elena; García-Posada, Miguel - 2023

Persistent link: https://www.econbiz.de/10014526677

Does social media affect performance in e-commerce business? : The role of customer management

Fitriani, Novi; Setiawan, Doddy; Aryani, Y. Anni; … - In: Journal of open innovation : technology, market, and … 9 (2023) 4, pp. 1-11

dependent variable in this research is dichotomous, this research uses binary logistic regression analysis. The research results …

Persistent link: https://www.econbiz.de/10014513712

A hybrid forecasting model with logistic regression and neural networks for improving key performance indicators in supply chains

Pietukhov, Rostyslav; Ahtamad, Mujthaba; Faraji-Niri, Mona - In: Supply chain analytics 4 (2023), pp. 1-9

Logistic regression and Neural Network techniques. The proposed methodology is evaluated through a comprehensive empirical …

Persistent link: https://www.econbiz.de/10014516544

A predictive analytics model for forecasting outcomes in the National Football League games using decision tree and logistic regression

Gifford, Matt; Bayrak, Tuncay - In: Decision analytics journal 8 (2023), pp. 1-10

target (dependent) variable. Decision tree and binary logistic regression models are constructed to describe the …

Persistent link: https://www.econbiz.de/10014517330