Interconnectedness dynamic spillover among US, Russian, and Ukrainian equity indices during the COVID-19 pandemic and the Russian-Ukrainian war.

We examine the dynamics and spillover effects of interconnectedness among equity indices during the COVID-19 pandemic and the Russian-Ukrainian war. Using daily data from June 2010 to May 2023, we consider five major equity indices, namely, the Dow Jones Industrial Average (DJIA), National Association of Securities Dealers Automated Quotations (Nasdaq), Standard and Poor's 500 (S&P 500), PFTS (Ukraine), and Moscow Exchange (MOEX; Russia). The results demonstrate a 45 % level of the total connectedness index to explain the interconnectedness among these indices. The findings indicate that the DJIA, Nasdaq, Small Cap 100, and MOEX are net receivers of spillovers, whereas the S&P 500 and PFTS are significant net transmitters. Moreover, analysis of different periods, including before and during COVID-19 and before the Russian-U war, provides insights into the changing patterns of interconnectedness and spillover effects during these events. This study contributes to a better understanding of the interdependency and transmission mechanisms among equity markets and highlights the importance of considering external factors and geopolitical events in formulating risk management and investment strategies.

AI-Based Prediction of Capital Structure: Performance Comparison of ANN SVM and LR Models.

Capital structure is an integral part of the corporate finance that sources the funds to finance growth and operations. Managers always have to maintain value of the firm to be higher than the cost of capital in order to maximize the shareholders wealth. Empirical studies have used sources of finance like debt and equity as variables of capital structure. A choice between debt and equity finance analyzes the firm's ability to perform under the financially constrained environment to attain the sustainable growth. Therefore, it gives rise to a dire need to estimate the cost of capital precisely. We examined the capital structure of top ten market capitalization of the stock markets included in MSCI Emerging index with the use of artificial neural networks, support vector regression, and linear regression in forecasting methods. The capital structure is measured as the proportion of total debt over total equity (Tang et al., 1991). Other financial ratios such as profitability, liquidity, solvent, and turnover ratios were considered as drivers of the capital structure. Applying logistic and hyperbolic tangent activation functions, it was concluded that ANN has a great potential of replacing other traditional forecasting models with the nonstationary data. This research contributes with a new dimension for estimation through different activation functions. There is a possibility of ANN dominance as compared to the other models applied for predictability in financial markets.