This study analyzes the response of economic fundamentals to a misalignment shock of the real effective exchange rate in Korea. The estimation results of the equilibrium exchange rate determination model and time series model show that there is no significant difference in the direction of the deviation from equilibrium and that the won is significantly undervalued during the period before 1988, or during the currency and global financial crises. The cumulative impulse response analysis of the VAR model over the full period shows that an upward shock to the deviation from the equilibrium exchange rate reduces the GDP gap and inflation rate, while the effect on the call rate is not statistically significant. Furthermore, an upward misalignment shock initially worsens the goods and services balance, but the deficit in the goods and services balance shrinks significantly over time. In rolling regressions analysis, the entire sample is divided into two periods to estimate the impulse response function from the first period, and then the same procedure is repeated by moving the sample forward one by one. The cumulative impulse response results show that, as is the case for the full period, a positive exchange rate misalignment shock initially reduces the GDP gap, inflation, and worsens the goods and services balance, but the impact of this upward shock on these variables becomes increasingly weaker in the more recent sample. It also shows that the negative impact of upward shocks on the current account is smoothed out more recently during periods of undervaluation than during periods of overvaluation.

JEL Classification: C5, F3, F4

The rapid development of China’s digital economy has enabled China to lead the world in financial technology (FinTech). In this context, it is imperative to study the impact of FinTech at the macro level on the sources of R&D financing for micro-enterprises. Using the data of A-share listed companies on the main boards of China’s Shanghai and Shenzhen cities and the municipal-level FinTech development index from 2011 to 2020, this paper conducts an empirical test by applying the system generalized method of moments estimation (system GMM). Fintech facilitates firms’ external financing of R&D. There is significant heterogeneity across different types of firms, with fintech facilitating R&D financing more strongly for young and non-state firms. This study not only complements the literature on the impact of fintech on R&D financing but also has essential practical guidance significance, which can provide valuable guidance and assistance to different types of enterprises in their R&D financing decision-making process.

JEL Classification: D21, G32, M41

With the escalation of global trade, the Chinese commodity futures market has ascended to a pivotal role within the international shipping landscape. The Shanghai Containerized Freight Index (SCFI), a leading indicator of the shipping industry’s health, is particularly sensitive to the vicissitudes of the Chinese commodity futures sector. Nevertheless, a significant research gap exists regarding the application of Chinese commodity futures prices as predictive tools for the SCFI. To address this gap, the present study employs a comprehensive dataset spanning daily observations from March 24, 2017, to May 27, 2022, encompassing a total of 29,308 data points. We have crafted an innovative deep learning model that synergistically combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures. The outcomes show that the CNN-LSTM model does a great job of finding the nonlinear dynamics in the SCFI dataset and accurately capturing its long-term temporal dependencies. The model can handle changes in random sample selection, data frequency, and structural shifts within the dataset. It achieved an impressive R² of 96.6% and did better than the LSTM and CNN models that were used alone. This research underscores the predictive prowess of the Chinese futures market in influencing the Shipping Cost Index, deepening our understanding of the intricate relationship between the shipping industry and the financial sphere. Furthermore, it broadens the scope of machine learning applications in maritime transportation management, paving the way for SCFI forecasting research. The study’s findings offer potent decision-support tools and risk management solutions for logistics enterprises, shipping corporations, and governmental entities.

JEL Classification: G12, L15, O40