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Classifying High-Growth Manufacturing Firms on the Swedish Stock Market:A Comparative Study Between the Logistic Regression, Support Vector Machine and Artificial Neural Network

Fridström, William LU (2023) NEKP01 20231
Department of Economics
Abstract
This is a comparative study between two modern machine learning algorithms, the Support Vector Machine and Artificial neural network, and one traditional econometric model, the Logistic regression. The main objective is to compare their performance by classifying high-growth companies. The study uses panel data from 156 manufacturing firms on the Swedish stock market between 2018 and 2021. The results show that in line with previous studies, the modern machine learning algorithms, Support Vector Machines and Artificial neural networks perform better in classification accuracy and misclassification rate compared to the logistic regression when classifying which manufacturing firms were high-growth during the four years studied. This study... (More)
This is a comparative study between two modern machine learning algorithms, the Support Vector Machine and Artificial neural network, and one traditional econometric model, the Logistic regression. The main objective is to compare their performance by classifying high-growth companies. The study uses panel data from 156 manufacturing firms on the Swedish stock market between 2018 and 2021. The results show that in line with previous studies, the modern machine learning algorithms, Support Vector Machines and Artificial neural networks perform better in classification accuracy and misclassification rate compared to the logistic regression when classifying which manufacturing firms were high-growth during the four years studied. This study recommends adopting the Support Vector Machine algorithms for high-growth classification modelling. (Less)
Please use this url to cite or link to this publication:
author
Fridström, William LU
supervisor
organization
course
NEKP01 20231
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Machine Learning, Econometrics, Firm Growth, Binary Classification, Prediction, Model comparision.
language
English
id
9119857
date added to LUP
2023-06-19 10:03:47
date last changed
2023-06-19 10:03:47
@misc{9119857,
  abstract     = {{This is a comparative study between two modern machine learning algorithms, the Support Vector Machine and Artificial neural network, and one traditional econometric model, the Logistic regression. The main objective is to compare their performance by classifying high-growth companies. The study uses panel data from 156 manufacturing firms on the Swedish stock market between 2018 and 2021. The results show that in line with previous studies, the modern machine learning algorithms, Support Vector Machines and Artificial neural networks perform better in classification accuracy and misclassification rate compared to the logistic regression when classifying which manufacturing firms were high-growth during the four years studied. This study recommends adopting the Support Vector Machine algorithms for high-growth classification modelling.}},
  author       = {{Fridström, William}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Classifying High-Growth Manufacturing Firms on the Swedish Stock Market:A Comparative Study Between the Logistic Regression, Support Vector Machine and Artificial Neural Network}},
  year         = {{2023}},
}