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.

Automated summary

Capital structure is a crucial element in corporate finance that impacts the growth and operations of a company. The choice between debt and equity financing is vital for a company's sustainable growth in a financially constrained environment. Accurate estimation of the cost of capital is of great importance. This study examined the capital structure of the top ten stocks in the MSCI Emerging Index, using various forecasting methods. The findings suggest that artificial neural networks have the potential to replace traditional models in forecasting nonstationary data.