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Recently, machine learning (ML) methods gained popularity in finance and accounting research as alternatives to econometric analysis. Their success in high-dimensional settings is promising as a cure for the shortcomings of econometric analysis. The purpose of this study is to prove further the relationship between intellectual capital (IC) efficiency and firm performance using ML methods.

This study used the double selection, partialing-out and cross-fit partialing-out LASSO estimators to analyze the IC efficiency’s linear and nonlinear effects on firm performance using a sample of 2,581 North American firms from 1999 to 2021. The value-added intellectual capital (VAIC) and its components are used as indicators of IC efficiency. Firm performance is measured by return on equity, return on assets and market-to-book ratio.

The findings revealed significant connections between IC measures and firm performance. First, the VAIC, as an aggregate measure, significantly impacts both firm profitability and value. When the VAIC is decomposed into its breakdowns, it is revealed that structural capital efficiency substantially affects firm value, and capital employed efficiency has the same function for firm profitability. In contrast to the prevalent belief in the area, human capital efficiency’s impact is found to be less important than the others. Nonlinearities are also detected in the relationships.

As ML tools are most recently introduced to the IC literature, only a few studies have used them to expand the current knowledge. However, none of these studies investigated the role of IC as a determinant of firm performance. The present study fills this gap in the literature by investigating the effect of IC efficiency on firm performance using supervised ML methods. It also provides a novel approach by comparing the estimation results of three LASSO estimators. To the best of the author’s knowledge, this is the first study that has used LASSO in IC research.

Firms prefer to have more than one bank relationship to secure the flow of funds for their operations, particularly in bank-based economies. On the other hand, banks lean toward expanding their customer base with firms already in the credit market. The purpose of this study is to investigate the effect of the number of bank relationships as a firm-specific determinant of capital structure and to discuss its impact on the banking sector.

A two-step system generalized method of the moments estimation method is used in this study. The sample comprises 213 Turkish non-financial, publicly listed firms with a positive shareholder’s value for the 2012–2017 period.

The findings show that the number of bank relationships increases the leverage of sample firms while the concentration in the banking sector decreases it. These rather intriguing findings are attributed to an under-the-counter credit policy that causes a high-risk shift and a curse of mainstream banks. Once the mainstream banks allocated credit to the firm, its credibility is consumed by the following banks, which is implied by the significantly negative relationship between bank concentration and firm leverage. This problem is defined as the mainstream bank curse in the study.

The previous literature focuses on the effects of the number of bank relationships on firm profitability, cost of debt and shareholder wealth. However, its impact on the capital structure has not yet been systematically investigated. To the authors’ knowledge, this is the first study to critically analyze the effect of the number of bank relationships on the capital structure. The findings will be of immense benefit to the banking sector and the regulatory bodies.