Asian Economic and Financial Review

Company growth, free cash flow, tax avoidance, and earnings management: Examining the role of the audit committee

2025-09-17T20:39:10-05:00

Earnings management remains a key concern in financial reporting quality, particularly in emerging markets where regulatory enforcement may be weaker. The novelty of this study lies in the integration of several variables, namely firm growth, free cash flow, and tax avoidance, into a single model to examine their impact on earnings management. Furthermore, this study incorporates corporate governance, specifically the role of the audit committee as a moderating variable. This research investigates the influence of company growth, free cash flow, and tax avoidance on earnings management, while also examining the moderating role of the audit committee. The study uses a sample of 274 manufacturing firms listed on the Indonesia Stock Exchange (IDX) in 2023, selected through purposive sampling. Data from financial reports were analyzed using multiple linear regression and moderated regression analysis. The findings indicate that company growth enhances earnings management, whereas free cash flow exerts a negative influence. Furthermore, the audit committee mitigates the positive relationship between tax avoidance and earnings management. This research highlights the importance of having independent and skilled audit committees to enhance supervision and ensure adherence to regulations. Firms should also design performance-based compensation carefully to align managerial incentives with shareholder interests.

Building a composite early warning index for financial market crises using machine learning and macroeconomic-political uncertainty indicators

2025-09-18T20:55:36-05:00

The accurate and timely prediction of financial market crises remains a persistent challenge for economists, policymakers, and investors. Traditional early warning systems (EWS) often rely on low-frequency macroeconomic indicators and static econometric models, limiting their effectiveness in dynamic market environments. This study proposes to fill this gap by developing a novel framework for crisis prediction through constructing a Composite Early Warning Index (CEWI) that integrates daily data from financial markets, macroeconomic fundamentals, and political uncertainty indicators. Principal Component Analysis (PCA) was employed to synthesize these diverse variables into a single latent factor, capturing the underlying systemic risk. Machine learning algorithms, including Logistic Regression, Random Forest, and XGBoost classifiers, were trained on historical data spanning from 2000 to 2025 to predict crisis periods, defined by sharp equity market declines and official recession declarations. The XGBoost model achieved superior performance with an ROC-AUC of 0.953. Feature importance analysis utilizing SHAP values identified market volatility (VIX), gold prices, and oil prices as the most influential predictors. The results demonstrate that combining high-frequency financial and political indicators with advanced machine learning techniques significantly enhances crisis prediction accuracy. The proposed CEWI-based framework offers a powerful tool for early risk detection and has important implications for financial regulation, investment strategy, and economic policy design.

Assessment of domestic trade performance in developing economies: Integrated metrics, factor prioritization and strategic implications

2025-09-18T21:06:36-05:00

Domestic trade constitutes a critical driver of sustainable economic growth in emerging markets. However, assessing its efficiency remains challenging due to fragmented indicators and the absence of a unified methodology. This study aims to develop a comprehensive metric for evaluating the performance of the trade sector and identifying strategic priorities for its development. Using data from the Bureau of National Statistics of the Republic of Kazakhstan for the period 2001–2023, the analysis employs factor and regression analysis methods, complemented by expert evaluations to weight priority factors and enhance the precision of the aggregated indicator aligned with strategic development objectives. The study identifies four key groups of efficiency determinants socio-economic, production, infrastructure, and price-behavioral factors with purchasing power, employment, and the condition of trade infrastructure exerting the most significant influence. Production and infrastructure factors demonstrate a moderate impact, while price gross value-added variables gain importance under inflationary pressures. The proposed methodology offers a reliable diagnostic tool for monitoring sectoral sustainability and supports the development of targeted economic measures to enhance trade efficiency. Although the findings, based on the context of Kazakhstan, may require adaptation for broader application, the methodology remains pertinent for other emerging economies, considering differences in data availability and institutional maturity. Practical implications include providing policymakers and stakeholders with actionable insights to stimulate domestic production, improve logistics and institutional infrastructure, and strengthen strategic planning processes. This study presents a scalable framework for assessing trade efficiency and its impact on supply chain resilience in emerging markets.