Issue #3 (Volume 22 2025)

Free Trade Agreements (FTAs) are widely recognized as instruments for enhancing economic integration and attracting Foreign Direct Investment (FDI). This study examines the impact of FTAs, particularly next-generation agreements such as the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) and the European Union-Vietnam Free Trade Agreement (EVFTA), on FDI inflows to Vietnam. Using a Gravity Model and a panel dataset of 48 trading partners covering the period 2007–2023, this study quantifies the extent to which FTAs influence FDI attraction. The empirical results reveal that FTAs significantly contribute to increased FDI inflows. The overall effect of FTA participation is estimated at 5.64% (coefficient = 0.0549, p < 0.05), reinforcing the positive relationship between trade liberalization and investment attraction. However, the impact varies across agreements. The CPTPP has a stronger effect, increasing FDI inflows by approximately 9.47% (coefficient = 0.0905, p < 0.05), while the EVFTA does not exhibit a statistically significant impact. These findings highlight the effectiveness of next-generation FTAs in attracting investment, particularly when agreements include deeper commitments beyond tariff reductions. For Vietnam and other emerging economies, maximizing the benefits of FTAs requires complementary structural reforms, including institutional improvements, regulatory enhancements, and investment-friendly policies to sustain FDI inflows and strengthen global economic integration.

Acknowledgment
The authors gratefully acknowledge the financial support from the Banking Academy of Vietnam.

This study investigates the relationship between Environmental, Social, and Governance (ESG) performance and financial outcomes, as measured by Return on Assets (RoA), among publicly SRI-KEHATI listed firms in Indonesia. Utilizing panel data from 90 firm-year observations over six years, the analysis employs a Random Effects Model (REM) across three progressively expanded specifications. ESG performance is proxied by the Sustainalytics ESG Risk Score, with higher values indicating poorer ESG standing. The estimation reveals a consistently positive and statistically significant relationship between ESG risk and financial performance. In the baseline model, the coefficient for ESG is 0.598 with a p-value of 0.052. This effect strengthens in the second model (coefficient = 0.768, p-value = 0.010) and remains significant in the fully controlled model (coefficient = 0.724, p-value = 0.017). These results imply that firms with weaker ESG profiles may achieve higher profitability, particularly in emerging markets with lenient ESG enforcement. Sustainable Growth Rate (SGR) also strongly and positively influences RoA (coefficient = 0.740, p-value = 0.002), underscoring the role of sectoral reinvestment capacity. The findings raise critical questions regarding the alignment between ESG efforts and financial incentives in transition economies. Policymakers are urged to consider stronger regulatory frameworks to realign ESG compliance with firm-level profitability. This study contributes to the literature by providing context-specific insights into the paradox of ESG and financial success in under-regulated markets.

The foreign exchange market, characterized by high volatility and economic significance, requires accurate predictive models. This study investigates the application of the Temporal Fusion Transformer (TFT), enhanced with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), for forecasting major foreign exchange (forex) currency pairs: USD/EUR, USD/JPY, USD/CNY, USD/AUD, and USD/INR. The proposed framework integrates a wide range of economic indicators, which include interest rate differentials, GDP growth, and trade balances, alongside investor sentiment derived from Twitter and ESG-related news sentiment. By addressing the non-linear, multiscale nature of forex time series, the CEEMDAN-TFT model facilitates improved signal decomposition and interpretability. Empirical results indicate that the model demonstrates competitive performance across all five currency pairs, with the USD/EUR pair exhibiting the highest predictive accuracy. Other pairs, exhibiting good predictive accuracy, include USD/JPY and USD/CNY, underscoring the model’s adaptability to varying economic contexts. Performance is assessed using multiple error metrics, and the model is benchmarked against standard neural network approaches (MLP, RNN, LSTM, CNN). Variable importance analysis highlights the dynamic influence of interest rates, investor sentiment, and ESG factors across different market regimes. This study provides empirical evidence that including ESG and investor sentiment can improve the accuracy of currency forecasting models. This study provides guidance and a framework for informed decision-making for traders, analysts, and policymakers.

The efficient market hypothesis is struggling to explain market behavior during rare, high-impact events. In such uncertain times, familiarity guides the decisions, allowing the brain to rely on subconscious processing for optimal outcomes. Therefore, this research aimed to examine the relationship between elevated familiarity bias and abnormal returns during rare events. Data were collected from all companies listed and active on the Indonesia Stock Exchange from 1997 to 2020. A systematic sampling method was used to establish the sample criteria, which led to a total of 5,615 observations derived from the number of trading days over 23 years across nine industries on the Indonesian Stock Exchange. The data collected were analyzed using the traditional Capital Asset Pricing Model, prospect theory and extending the Fama and French three-factor model with the addition of a psychological factor. The results show that familiarity bias behavior does not uniformly occur across all industries in Indonesia during rare events. The industries negatively impacted by these events include agriculture, consumer goods, trade services and investment, finance, basic industry, chemicals, mining, miscellaneous industries, property, real estate and building construction at values of –0.0847, –0.0946, –0.0721, -0.0405, –0.0717, –0.1258, –0.024, and –0.0805, respectively. A positive impact was only found in the infrastructure, utilities, and transportation industry at 0.0028. In conclusion, stock market behavior also affects the economy from a behavioral finance perspective.

Type of the article: Research Article

Abstract
This study examines the impact of crude oil price shocks on stock market efficiency and volatility spillovers across BRICS countries (Brazil, Russia, India, China, and South Africa) using 6,275 daily observations from April 1999 to March 2024. The results from unit root and Lo-Mackinlay variance ratio tests show that only Russia and India exhibit weak-form efficiency, while Brazil, China, and South Africa display inefficiencies, indicating scope for abnormal returns. Granger causality analysis confirms strong short-term interlinkages, with Brazil emerging as a leading market for Russia, India, and South Africa. Johansen’s cointegration test reveals long-term relationships among BRICS markets and with crude oil prices, suggesting limited diversification opportunities. ARCH-GARCH models and impulse response functions show significant volatility spillovers triggered by oil price shocks, lasting 2-6 trading days. Crude oil volatility affects all markets except South Africa, reflecting varying energy dependencies. These findings underscore the interconnectedness and systemic risk exposure of BRICS financial systems, with critical implications for international investors and policymakers in managing portfolio strategies and stabilizing markets.

Type of the article: Research Article

Abstract
Mega project, defined as infrastructure investments exceeding USD one billion, play a central role in national development but often face significant financial risks. This study explores the dynamic relationship between macroeconomic risk factors and return on investment (ROI) in mega projects using a panel data econometric approach. A balanced dataset from 2000 to 2024 was constructed, covering five economies with significant mega infrastructure investment: Turkey, China, the United States, India, and Germany. The analysis incorporates both internal project risks (cost overruns, completion delays) and external macroeconomic shocks (exchange rate volatility, inflation, interest rates). Representative infrastructure projects in transport, energy, and urban sectors were selected based on official national data and international financial databases. Pedroni cointegration tests confirmed the existence of long-term equilibrium relationships, justifying the application of a panel Generalized Method of Moments (GMM) model to address endogeneity, heterogeneity, and dynamic effects. The results indicate that a 1% increase in budget allocation leads to a 0.21% rise in ROI (p < 0.001), while cost overruns are associated with a 0.84% ROI increase per 1% overspend (p = 0.003), suggesting potential value in strategic overspending. In contrast, a 1% increase in exchange rate volatility reduces ROI by 0.69% (p < 0.001). No significant effects were found for inflation or completion delays. These findings reflect aggregated financial behavior rather than individual project cases, offering generalizable insights into mega project finance. The study contributes to the literature by constructing a risk-adjusted, multi-country econometric model and offers policy guidance for enhancing financial resilience in large-scale infrastructure investments.

Type of the article: Research Article

Abstract
This study examines the relationship between Environmental, Social, and Governance (ESG) performance and equity financing among non-financial firms listed on the Johannesburg Stock Exchange (JSE) in South Africa. Using the data from Refinitiv Eikon, Bloomberg, and company sustainability reports, the research analyzes ESG and financial performance across multiple sectors, including manufacturing, retail, and mining, with a sample of 420 firm-year observations covering 60 firms over the period from 2015 to 2023. The results from System Generalized Method of Moments (GMM) model reveal that the Debt-to-Equity Ratio has a significant positive relationship with equity financing, highlighting the persistence of capital structure in financing decisions. Environmental Score demonstrates a significant positive effect on equity financing, indicating that better environmental performance attracts more investment, though this result was not significant in the Fixed Effects Model. Social Score consistently shows a positive impact across both models, reinforcing the importance of social performance in attracting equity capital. Governance practices also exhibit a significant influence on equity financing, emphasizing the role of effective governance in improving access to equity financing when considering dynamic factors. These findings suggest that ESG performance is a critical factor in equity financing decisions, and underscore the need for financial regulators, investment institutions, and industry bodies to raise awareness about the importance of ESG considerations. The study contributes to the growing literature on sustainable finance, illustrating the strategic importance of ESG factors in shaping investor preferences and enhancing market stability.

Type of the article: Book Review

Abstract
Book “Antitrading” by Oleksii Plastun is a bold, lively, well-illustrated but still critical and science-based examination of the trading industry and financial market, challenging widespread myths about making “easy money”, technical analysis almighty, and the promises of financial heaven given by trading “gurus”. Mixing personal trading experience and academic background, Plastun describes cognitive biases, psychological traps, and misleading narratives that lead traders to financial losses in the face of severe laws of market power, probability, and financial market theories, concepts, and hypotheses.

Type of the article: Research Article

Abstract
In emerging markets such as Vietnam, where firms face rapid growth and rising operational complexity, understanding how inventory efficiency interacts with business dynamics is vital. This study aims to examine the relationship between inventory management and firm performance, with a specific focus on how this relationship is moderated by business growth and management costs. Using a balanced panel dataset of 364 manufacturing firms listed on Vietnam’s stock exchanges from 2012 to 2023, this study employs panel data regression methods, including Fixed Effects Model, Random Effects Model, Feasible Generalized Least Squares, and, notably, the System Generalized Method of Moments to address issues of endogeneity and unobserved firm-level heterogeneity. Firm performance is measured by return on assets, and inventory management is proxied by average inventory days. The results show that average inventory days are negatively associated with firm performance, indicating that longer inventory cycles reduce profitability. The interaction term between inventory days and business growth is also negative, suggesting that growth exacerbates the adverse effects of inefficient inventory practices. In contrast, the interaction between inventory days and management costs is positive, implying that effective cost control can mitigate inventory inefficiency. These findings highlight the need for Vietnamese manufacturing firms to align inventory practices with growth strategies and cost management to sustain profitability in dynamic markets.

Acknowledgment
This research was conducted as part of the doctoral dissertation project under Decision No. 5379/QĐ-ĐHDT dated December 31, 2022, issued by Duy Tan University.