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This study investigates whether horizon problems affect the allocation of capital budgets and their implementation in a government setting.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The purpose of this paper is to investigate factors affecting budget implementation among local governments in Indonesia, where rules are relatively strict and risks of facing corruption charges are high.

This study employs regression analyses using a sample of 1,151 local government-years.

This study finds that the level of budget implementation is affected by the leadership factors (i.e. mayors’ term, tenure and age) and the proportion of capital expenditures. The level of budget implementation is relatively lower when the mayor is in the second term, is in the early years of the five-year tenure and is over 60 years old. Higher proportion of capital expenditures also reduces the level of budget implementation.

This study contributes to the literature by presenting empirical evidence as to what factors explain the variations in the level of budget implementation among local governments especially under strict rules and a risky environment.

The purpose of this paper is to explore the association between a chief finance officer's (CFO's) financial expertise and corporate social responsibility (CSR).

The paper's sample consists of firms from the 2005 S&P 500 Index. Data on CSR come from Kinder, Lydenberg, and Domini (KLD), Inc. Data on CFO financial expertise were had collected. Consistent with prior research, experience (tenure), education (masters of business administration degree), and professional experience (certified public accountant designation) are used to measure the CFO's financial expertise.

Using a sample of S&P 500 firms from 2005, it is found that CFO experience (measured by tenure) is positively related to CSR at a significant level. In addition, the results indicate that CSR activities are not related to the CFO's education (measured as a Master's of Business Administration degree) or accounting expertise (measured as certified public accountant designation). The findings suggest that CFOs with more experience engage in more CSR activities than CFOs with less experience.

This study is valuable for several reasons: First, the study contributes to both the CSR literature and the CFO financial expertise literature by delivering new evidence on the link between CFO financial expertise and corporate social responsibility. Second, the study provides useful information to boards of directors, corporations and investors on certain CFO characteristics associated with effective CSR. Third, the study provides empirical evidence to support the suggested shift in the CFO's role from accountant to co‐driver of a firm's long‐term strategy.