Fuad Rakhman, Ainun Na'im and Shahrokh Saudagaran
This study investigates whether horizon problems affect the allocation of capital budgets and their implementation in a government setting.
Abstract
Purpose
This study investigates whether horizon problems affect the allocation of capital budgets and their implementation in a government setting.
Design/methodology/approach
We use data from 2005 to 2020 for local governments in Indonesia, which apply a limit of two five-year terms for mayors. We use regression analyses for panel data with total observations of 4,541 local government years from 448 unique local governments. We also use graphical analyses and t-tests to provide robustness to our results.
Findings
Mayors allocated lower capital expenditures in the second term than in the first. Capital budget allocation is lower for local governments whose mayors are older than 60. Our additional analysis shows that incumbents seeking re-election allocate more capital expenditure than those not seeking re-election.
Research limitations/implications
This study contributes to the literature on the behavioral effect of term limits on local government's allocation and implementation of capital budgets. Limiting elected government officials to a certain number of terms will prevent the monopoly of power. However, it may negatively affect budget allocation on capital programs in their last term. Our findings should interest public policymakers in discerning the costs and benefits of term limits for elected offices.
Originality/value
Most studies on horizon problems have focused on the corporate setting. This study provides evidence of the effects of horizon problems in the government setting, especially in Asia.
Details
Keywords
Stiven Agusta, Fuad Rakhman, Jogiyanto Hartono Mustakini and Singgih Wijayana
The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for…
Abstract
Purpose
The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.
Design/methodology/approach
The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.
Findings
Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.
Research limitations/implications
The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.
Practical implications
Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.
Originality/value
Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.