One of the most perplexing issues faced by finance managers is to know about the effect of capital structure on the profitability of firm. Many studies have been carried out to examine the effect of capital structure on the profitability of firms, but most of them belong to other parts of the world, and only few studies have been conducted in India. Thus, the present study has been undertaken to evaluate the effect of capital structure on the profitability of Nifty 50 companies listed on National Stock Exchange of India from 2008-2017. The data has been analyzed by using descriptive statistics, correlation and multiple panel data regression models. Four different regression models have been used to study the relationship between capital structure and profitability. In these models, we study the individual effect of total debt and total equity ratios on profitability, that is, ROA and ROE. All four models have been tested with pooled OLS, fixed effects, and random effects. We conclude that there is significant positive impact of capital structure on firm's profitability.
One of the most debated issue in the field of corporate finance is the relationship between dividend policy and market price of share. There is good amount of literature for and against this issue. The present study has been undertaken to evaluate the effect of dividend policy on market prices of shares of Nifty 50 companies listed on the National Stock Exchange (NSE) for 2008–2017. The data have been analysed by employing multiple panel data regression models namely pooled regression, fixed effect model and random effect model. The Hausman test has been used to suggest the most appropriate regression model. The result of the Hausman test indicates that random effect model is more relevant in describing the relationship among the given variables. The results of the random effect regression model support the relevant approaches of dividend policy. Thus, we conclude that there is significant effect of dividend policy on the stock price of firms.
Purpose The purpose of this paper is to investigate the dynamic relationship among Gold, Crude oil, Indian Rupee-US Dollar and Stock market-Sensex (gold, oil, dollar and stock market (GODS)) in the pre-crisis, the crisis and the post-crisis periods in the Indian context. Design/methodology/approach The authors use Johansen’s cointegration technique, Vector Error Correction Model (VECM), Vector Auto Regression, VEC Granger Causality/Block Exogeneity Wald Test, and Granger Causality and Toda Yamamoto modified Granger causality to study long-run relationship and causality. Findings Johansen’s cointegration test results indicate that there is a long-run equilibrium relationship among the variables in the pre-crisis and the crisis periods but not in post-crisis period. VECM results report that none of four models of the variables show long-run causality in the pre-crisis period. During the crisis period, both crude oil and Sensex models show long-run causality. However, in some cases, results indicate short-run causality. The authors find one-way causality from USD and Sensex to crude oil, and from gold and Sensex to USD. Thus, the authors conclude that the relationship among GODS is dynamic across global financial crisis. Practical implications The research findings of this study are vital to the large group of stakeholders and participants of gold, crude oil, US dollar and stock market in emerging economies like India. The results are useful to importers, exporters, government, policy makers, corporate houses, retail investors, portfolio managers, commodity traders, treasury and fund managers, other commercial traders, etc. Originality/value This study is one of its kinds as it investigates the relationship among GODS in India in different sub-periods like before, during and after the global financial crisis of 2008. None of the studies compare phase-wise relationship among GODS in the Indian context. The study contributes to the economic theory and the body of knowledge. It highlights the need to revisit the economic theory to explain the interplay mechanism among GODS.
Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.
Over the globe, the various financial markets are becoming integrated and the linkages among variables Gold prices, Crude Oil prices, US Dollar rate and Stock market (GODS) invite a special attention of various financial analysts and investors. For an import-dependent country like India, the interplay among these variables is vital. Thus in this study, we investigate the cointegration and causality relationship among gold, crude oil, us dollar and stock market (Sensex) across the global financial crisis of 2008. We use Johansen's cointegration technique, Vector Error Correction Model (VECM), Vector Auto Regression (VAR), VEC Granger Causality/Block Exogeneity Wald Test and Granger Causality, and Variance Decomposition to study cointegration and strength & direction of causality for three sub-periods. Johansen's cointegration test results indicate that there is long-run equilibrium relationship among the variables in the pre-crisis and the crisis periods but not in post-crisis period. VECM results report that none of four models of the variables show long-run causality in the pre-crisis period at 5% level of significance. During the crisis period, both crude oil and Sensex models show long run causality. However, in some cases short-run causality is indicated in results. Granger causality test results show that there is one-way causality from USD and Sensex to crude oil, and from gold and Sensex to USD. Thus, we conclude that the relationship among GODS is dynamic and has been affected by global financial crisis of 2008.
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