This study investigates the impact of perpetual swaps on structural complexity and market efficiency within cryptocurrency markets. Utilizing 15-minute interval price data from 35 cryptocurrencies, we employ multifractal detrended fluctuation analysis (MF-DFA), Multifractal-based measure of the degree of market efficiency (MED), and Market deficiency measure (MDM) to comparatively evaluate market characteristics before and after the introduction of perpetual swaps. Our empirical analysis reveals a substantial decrease in multifractality and structural complexity across most cryptocurrencies post-introduction, particularly pronounced over longer horizons (4–6 months). This reduction indicates enhanced information dissemination and more efficient price formation mechanisms. Notably, Bitcoin (BTC), benefiting from superior liquidity and efficient information flow, exhibited relatively stable multifractal characteristics, although significant volatility driven by fat-tail distributions remained persistent. Statistically significant improvements in market efficiency were consistently demonstrated via paired t-tests, one-sided t-tests, and Wilcoxon non-parametric tests. These improvements were particularly salient during extended observation periods, providing robust empirical evidence that perpetual swaps markedly enhance market efficiency. Consequently, our findings highlight that the introduction of perpetual swaps contributes meaningfully to cryptocurrency market efficiency beyond mere liquidity enhancement, promoting more accurate price discovery and reducing informational asymmetries.

Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.

Pair trading is a statistical arbitrage investment strategy. Traditionally, cointegration has been utilized in the pair exploring step to discover a pair with a similar price movement. Recently, the clustering analysis has attracted many researchers' attention, replacing the cointegration method. This study tests a clustering-driven pair trading investment strategy in the Korean stock market. If a pair detected through clustering has a large spread during the spread exploring period, the pair is included in the portfolio for backtesting. The profitability of the clustering-driven pair trading strategies is investigated based on various profitability measures such as the distribution of returns, cumulative returns, profitability by period, and sensitivity analysis on different parameters. The backtesting results show that the pair trading investment strategy is valid in the Korean stock market. More interestingly, the clustering-driven portfolio investments show higher performance compared to benchmarks. Note that the hierarchical clustering shows the best portfolio performance.