Tree-based Machine Learning and Deep Learning in Predicting Investor Intention to Public Private Partnership

Abstract

Public private partnership (PPP) is the government initiate in accelerating public infrastructure development growth. However, the scheme exposes private sector to various risks including political risk which in turn affect financial performance and reporting of participating firms. Given that one of the issues facing the government is the lack of participation from the private sector in such arrangements. Thus, the main objective of this study is to observe the machine learning prediction models on private investor intention in participating the PPP program. Tree-based machine learning and deep learning are two different types of promising algorithms, which proven to be useful in widely domain of prediction problems but never been tested on the concerned problem of this study. Based on real data of investors for Indonesian listed firms, this paper presents the ability of the selected machine learning algorithms by means of different assessments point of view. First assessment is on the algorithms’ performances in producing accurate prediction. Second assessment is to identify the variance of PPP attributes in each of the prediction model with the machine learning algorithms. The performance results show that all the prediction models with the machine learning algorithms and the PPP attributes were well-fitted at R squared above 80%. The findings contribute a significant knowledge to various fields of scholars to implement a more in-depth analysis on the machine learning methods and investors’ prediction.