An Artificial Neural Network Growth Analysis in Construction Businesses

Abstract

This research paper explores the firms' growths analysis through Artificial Neural Networks, explicitly using the Multilayer Perceptron (MLP) Analysis in a panel of construction businesses operating in the country. The construction businesses data used are classified into Organizational characteristics (5 patterns) and Financial indicators (18 patterns). They refer to Liquidity (5), Operational Efficiency (4), Leverage (4), and Growth (5) patterns. Thus, 85 construction business data from 2020-2021 have been collected, but only 31 businesses are considered valid for Multilayer Perceptron analysis training purposes. The first research step before building the multilayer perceptron neural network is the implementation of the Receiver Operating Characteristics (ROC curve) Analysis at a 95% confidence level, considering as a dependent variable the firms' age [in start-up (0); growth (1) and those in the maturity phase (2)]. Then, based on ROC analysis results, a multilayer perceptron network with 10 input layers patterns, 10 customers' patterns factors, and one covariate is implemented. The number of hidden layers is 1, and the number of units in hidden layers is 20. The activation function used is Hyperbolic tangent. Thus, the empirical findings of the research provide construction businesses and line ministries with valuable insights on boosting their growth.