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.