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Building on pecking order theory, this study seeks to understand the various financial factors that influence top management's decision regarding the company’s capital structure. The authors attempt to understand and analyse whether the capital structure of mid‐ and small‐cap firms is affected by cash surplus scaled to total assets. Along with other determinants of capital structure such as liquidity, profitability, tangibility, market capitalisation and age, this is considered one of the major factors. Cash surplus is calculated using data from the cash flow statement. It is defined as the difference in cash from operating activities and that from investing activities and is scaled to total assets. To the best of the authors’ knowledge, this is the first study to regress cash surplus scaled to total assets and other determinants over leverage to examine the impact on mid‐ and small‐cap firms. The pecking order theory was found to hold for firms earning cash surplus.

Data were collected from the CMIE Prowess database of all firms listed on the NIFTY Small cap 250 index and NIFTY Midcap 150 index. The data of non-financial firms belonging to the midcap and small-cap sector, listed on the National Stock Exchange of India from 2012 to 2019 were considered. After cleaning the data, an unbalanced panel of 171 companies totalling 1,362 observations for the NIFTY Small-cap 250 index and another panel of 96 companies with 761 observations for the NIFTY Midcap 150 index was created. Panel data regression analysis was used to determine the effect of cash surplus scaled to total assets on the firms' capital structure.

This study demonstrates how small- and midcap firms' behave differently in taking capital structure decisions. Pecking order theory was found to hold for firms earning cash surplus as a proportion of total assets (Surplusta).

The study was conducted through data available on secondary sources and database. The study can be better conducted by conducting a primary survey too. Further study may be conducted with a blend of secondary and questionnaire method. The results can be compared to check the similarity in findings.

Managers can benefit from the findings when making decisions on long- and short-term loans. This study can help managers in terms of the financial variables that have a role to play in the financial leverage of the company. The decision of the managers of midcap or small-cap firms would be different. Factors influencing short- and long-term borrowings are different. Academics can discuss whether there is any difference in the influence of capital structure variables of small- and midcap companies and the reasons for such differences. Judicious decisions on capital structure will create wealth for the shareholders as the right decision about leverage would result in a proper cost of capital. The findings also add to the existing literature on the Pecking order theory.

Academics can discuss whether there is any difference in the influence of capital structure variables of small- and midcap companies and the reasons for such differences.

The study extends the existing literature by demonstrating that the capital structure of mid and small-cap firms is affected by cash surplus scaled to total assets. The pecking order theory was found to hold for firms earning cash surplus. This study can inform the practitioners about the financial variables that have a role to play in the company's financial leverage. As the results and significance of the variables of the midcap or small-cap firms are different, the decisions of the managers of these firms would be separate for the capital structure of their firms. The study also infers that the factors influencing short and long-term borrowings are different. The study determines whether managers' decision-making in such companies is different in terms of raising short- and long-term loans. The study attempts to guide managers in considering the different variables that would influence their capital structure decisions, particularly the decision to include debt in the capital. Financial variables need not be of equal importance for managers belonging to small- and midcap companies.

This study thoroughly examines the relationship between environmental, social and governance (ESG) scores and their subcategories with the investment decisions made by foreign institutional investors (FII). These subcategories include resource use, emission reduction and innovation under the environmental pillar, workforce, human rights, community and product responsibility under the social pillar and management, shareholders and CSR strategy under the governance pillar.

A machine learning technique known as “topic modeling” is used to analyse the current literature on ESG. To investigate the correlation between ESG scores and their subcategories with the investment decisions made by FII and to address concerns regarding multicollinearity and overfitting, a penalty-based regression model is employed.

The findings indicate that FIIs invest in firms with higher emission reduction and innovation scores under the environmental indicator. Additionally, firms with high human rights, community and product responsibility scores under the social indicator category have a positive relationship with FII investors. All subcategories of governance indicators, such as corporate social responsibility (CSR), strategy, shareholders and management scores, also positively impact FII investment. Of the three indicators, i.e. ESG, non-promoter FIIs give maximum weightage to governance indicators.

Since ESG is a contemporary topic, the findings on the relationship between different categories of ESG on FII investment will support managers in their FII investment. Also, the study will help the government frame policy decisions on ESG.

Previous studies have explored the impact of the overall ESG indicators on FII investments, but they have not specifically studied the influence of sub-indicators within these categories on investment decisions. By addressing this gap, the study enhances stakeholder theory by identifying and prioritizing the various subcategories of ESG indicators that impact FII investment decisions.

The paper examines contingent liabilities' effect on the firm's dividend decisions.

Fixed-effects regression and logit model results estimate the influence of contingent liabilities on firms' dividend decisions using a sample of 2,288 firm-year observations of S&P 500 firms from 2012 until 2022. Robustness checks and results from the 2SLS model further support the authors’ findings.

The results show that contingent liabilities negatively affect dividend payment decisions. This analysis further demonstrates that the stated effect of contingent liabilities on dividend decisions is more substantial for firms with financing deficits and those with above-industry-average corporate governance scores.

There needs to be more systematic conceptual reason for measuring uncertainty for firms and its influence on dividend decisions. Future research should use other measures of firm uncertainty to examine the relation of the firm's uncertainty with dividend decisions.

The authors suggest that contingent liabilities create uncertainty for future cash flows, influence a firm's agency costs and provide credible signals on a firm's prospects to the market. The findings support existing literature that measurable firm-specific variables significantly influence a firm's dividend decisions. The results are robust for an alternative explanation.

By investigating the impact of the influence of contingent liabilities on dividends, the authors extend research on dividend decisions and attempt to provide insights into a firm's dividend decisions by incorporating an off-the-balance sheet item (contingent liabilities) as a significant predictor for dividend decisions.