Crunching Numbers, Making Decisions: Artificial Intelligence and Statistics for Financial Distress Forecasting in Algeria and Saudi Arabia

Amine Sabek; Youcef Saihi

doi:10.23925/cafi.62.60718

Autores

Amine Sabek University of Tamanrasset https://orcid.org/0000-0002-6970-4183
Youcef Saihi University of Tamanghasset https://orcid.org/0000-0002-9583-8118

DOI:

https://doi.org/10.23925/cafi.62.60718

Palavras-chave:

dificuldades financeiras, previsão, rede neural artificial, regressão logística

Resumo

A previsão de dificuldades financeiras tem sido uma preocupação significativa tanto para pesquisadores quanto para profissionais há muito tempo. Este tema tem despertado um interesse substancial devido aos benefícios potenciais do uso de modelos preditivos para antecipar problemas financeiros e ajudar as empresas a evitar riscos financeiros que poderiam levar à falência e à liquidação. O objetivo principal desta pesquisa é prever dificuldades financeiras, comparando a eficácia da Rede Neural Artificial (RNA) com a Regressão Logística (RL). Esta avaliação baseia-se em dados de 12 empresas argelinas e 12 empresas sauditas durante o período de 2015 a 2019. As conclusões do estudo indicam que o modelo RL superou o modelo de Rede Neural Ampla (RNA) na previsão com precisão de dificuldades financeiras, alcançando uma precisão de classificação ideal para empresas argelinas e sauditas. Consequentemente, o modelo RL surge como a escolha preferida para prever dificuldades financeiras em ambos os países.

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