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Pregled bibliografske jedinice broj: 861092

Predicting company growth using logistic regression and neural networks


Zekić-Sušac, Marijana; Šarlija, Nataša; Has, Adela; Bilandžić, Ana;
Predicting company growth using logistic regression and neural networks // Croatian Operational Research Review, 7 (2016), 2; 229-248 (međunarodna recenzija, članak, znanstveni)


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Naslov
Predicting company growth using logistic regression and neural networks

Autori
Zekić-Sušac, Marijana ; Šarlija, Nataša ; Has, Adela ; Bilandžić, Ana ;

Izvornik
Croatian Operational Research Review (1848-0225) 7 (2016), 2; 229-248

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
company growth, factor analysis, logistic regression, neural networks, prediction model

Sažetak
The paper aims to establish an efficient model for predicting company growth by leveraging the strengths of logistic regression and neural networks. A real dataset of Croatian companies was used which described the relevant industry sector, financial ratios, income, and assets in the input space, with a dependent binomial variable indicating whether a company had high- growth if it had annualized growth in assets by more than 20% a year over a three-year period. Due to a large number of input variables, factor analysis was performed in the pre- processing stage in order to extract the most important input components. Building an efficient model with a high classification rate and explanatory ability required application of two data mining methods: logistic regression as a parametric and neural networks as a non- parametric method. The methods were tested on the models with and without variable reduction. The classification accuracy of the models was compared using statistical tests and ROC curves. The results showed that neural networks produce a significantly higher classification accuracy in the model when incorporating all available variables. The paper further discusses the advantages and disadvantages of both approaches, i.e. logistic regression and neural networks in modelling company growth. The suggested model is potentially of benefit to investors and economic policy makers as it provides support for recognizing companies with growth potential, especially during times of economic downturn.

Izvorni jezik
Engleski

Znanstvena područja
Ekonomija



POVEZANOST RADA


Projekti:
3933

Ustanove:
Ekonomski fakultet, Osijek

Poveznice na cjeloviti tekst rada:

Hrčak Hrčak

Citiraj ovu publikaciju:

Zekić-Sušac, Marijana; Šarlija, Nataša; Has, Adela; Bilandžić, Ana;
Predicting company growth using logistic regression and neural networks // Croatian Operational Research Review, 7 (2016), 2; 229-248 (međunarodna recenzija, članak, znanstveni)
Zekić-Sušac, M., Šarlija, N., Has, A., Bilandžić, A. & (2016) Predicting company growth using logistic regression and neural networks. Croatian Operational Research Review, 7 (2), 229-248.
@article{article, year = {2016}, pages = {229-248}, keywords = {company growth, factor analysis, logistic regression, neural networks, prediction model}, journal = {Croatian Operational Research Review}, volume = {7}, number = {2}, issn = {1848-0225}, title = {Predicting company growth using logistic regression and neural networks}, keyword = {company growth, factor analysis, logistic regression, neural networks, prediction model} }
@article{article, year = {2016}, pages = {229-248}, keywords = {company growth, factor analysis, logistic regression, neural networks, prediction model}, journal = {Croatian Operational Research Review}, volume = {7}, number = {2}, issn = {1848-0225}, title = {Predicting company growth using logistic regression and neural networks}, keyword = {company growth, factor analysis, logistic regression, neural networks, prediction model} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Emerging Sources Citation Index (ESCI)
  • EconLit





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