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Sathyanarayana, S.
- Determinants of Capital Structure:Evidence from Indian Stock Market with Special Reference to Capital Goods, FMCG, Infrastructure and IT sector
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PDF Views:93
Authors
Affiliations
1 MP Birla Institute of Management, Bengaluru, IN
2 School of Commerce, Reva University, Bengaluru, IN
1 MP Birla Institute of Management, Bengaluru, IN
2 School of Commerce, Reva University, Bengaluru, IN
Source
SDMIMD Journal of Management, Vol 8, No 1 (2017), Pagination: 75-83Abstract
The current study aims to examine the relationship between various identified determinants (Profitability, Tangibility, Growth Rate, Business Risk, Size and Non Debt tax shield) and its impact on financial leverage (CS) decisions of Capital goods, FMCG, Infrastructure and IT sector in Indian Stock market. In order to realise the stated objectives of the study the researchers have collected data from the published financial statements of quoted firms in the Indian stock market from the above mentioned sectors for a period of ten years (2006-2015). In the very first step,we tested the data by using multico1tinearity test and then we use linear multiple regression model to investigate the impact of chosen independent variables on CS (leverage) decisions in Indian capital market. Later, residual diagnostic CS, such as Serial correlation test, Heteroskedasticity Test, Normality and CUSUM test have been run to assess the strength of the constructed regression model. The results show that ER (Earnings), TA (Tangibility) and GR (Growth) were the major determinants in case of capital goods sector and ER (Earnings), TA (Tangibility), GR (Growth), Size and NDTS were the major factors for the FMCG sector. GR (Growth), BR (Business Risk) and Size for the Infrastructure sectors and ER (Earnings), BR (Business Risk) and Size were the major factors for the IT sector. The study revealed inconsistency in independent variables influencing the financial leverage component, though there is statistical support for the proposed determinants with respect to earnings and growth rate influencing the financial leverage.Keywords
Leverage, CUSUM Test, Heteroskedasticity, NDTS, Pecking Order Theory.Full Text
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- Volatility in Crude Oil Prices and its Impact on Indian Stock Market Evidence from BSE Sensex#
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PDF Views:87
Authors
Affiliations
1 MP Birla Institute of Management, Bangalore – 560001, Karnataka, IN
2 REVA University, Bangalore – 560064, IN
1 MP Birla Institute of Management, Bangalore – 560001, Karnataka, IN
2 REVA University, Bangalore – 560064, IN
Source
SDMIMD Journal of Management, Vol 9, No 1 (2018), Pagination: 65-76Abstract
The recent fluctuations in the crudes prices have captured the researcher’s attention towards the crucial role that crudes prices play on the economy of any nation. The volatility in crude price has influenced the uncertainty in the price expectation in the countries economy. As majority of the empirical studies support that the crude oil price volatility significantly influences the country’s economy and also the stock returns. Therefore, understanding the movement of stock returns is an important issue from the perspective of a developing economy like India. Therefore, it is necessary to identify the variables that drives the stock prices are very important for the market participants and policy makers. The aim of this paper is to investigate the volatility of crude prices and its impact on Indian stock market. For the purpose of the study the data has been collected from Prowess data base for a period from 2006 to 2015. The collected data has been tested for stationarity by employing ADF test and the length intervals for each variable to run the AIC. Later a linear regression has been run. The volatility of the Sensex has been measured by applying GARCH (1,1) model. The linear regression results show that changes in crudes prices have an impact on Sensex. Apart from that the study concludes that the Crude prices was significant in the volatility of the Sensex and have the competency to transmit shock on Sensex. Therefore, policy makers have to take the movement of the crudes prices while framing the policies that affect the economy at large and stock market in particular. Finally, these results have been compared to the available evidence.Keywords
Crude Oil Prices, GARCH (1, 1), Stationarity, Serial Correlation, Volatility.Full Text
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- Modeling Cryptocurrency (Bitcoin) using Vector Autoregressive (Var) Mode
Abstract Views :280 |
PDF Views:82
Authors
Affiliations
1 M P Birla Institute of Management, Bengaluru – 560001, Karnataka, IN
2 Joint Director, M P Birla Institute of Management, Bengaluru – 560001, Karnataka, IN
1 M P Birla Institute of Management, Bengaluru – 560001, Karnataka, IN
2 Joint Director, M P Birla Institute of Management, Bengaluru – 560001, Karnataka, IN
Source
SDMIMD Journal of Management, Vol 10, No 2 (2019), Pagination: 47-64Abstract
A digital currency in which encryption techniques are used to regulate the generation of units of currency and verify the transfer of funds, operating independently of a central bank. Therefore, Bitcoin is a form of digital currency that was designed by Satoshi Nakamoto (an unknown author of Bitcoin white paper 2008) and since then it has able to generate a considerable attention from investors due to its decentralized characteristics and the technology (block-chain) behind it. Bitcoin is a form of digital peer-to-peer currency system where transactions take place without a central bank. The transactions are verified by the nodes of the network and recorded in the Blockchain. Since the popularization of Bitcoin, this technology has caught attention of several technology companies who started to do research on the applications and opportunities of this technology. In this paper, an attempt has been made to capture the time varying variance of most prominent Cryptocurrency Bitcoin with world’s top traded currencies such as USD, GBP, Euro, Yen and CHF. In order to realise the stated objectives the researchers have collected the data from Prowess and Yahoo finance database from September 2013 till March 2018. In the first phase the collected data has been for normality and stationarity. Bitcoin was modelled for GARCH and EGARCH tests to capture the time varying volatility and leverage effect. Later the Johansen cointegration test has been conducted to find out the existence of cointegration between the top global currencies with Bitcoin. In the last phase the VECM has been run to capture the both long run and short relationship between Bitcoin and top five traded currencies. In the last phase Variance Decomposition has been run to capture the variance explained by the prominent global currencies on Bitcoin. Both USD and GBP share long run relationship with Bitcoin. Finally, the results have been compared with the possible evidence.
Keywords
Cryptocurrency, Blockchain, Stationarity, VAR, Impulse Response Function.Full Text
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