The purpose of this article is to unearth the dimensions of quality of work life and work–life balance and to find the impact of the quality of work life on work–life balance. Data have been gathered from 89 managers of public and private sector banks in India using a convenience sampling method and analysed using principal component analysis and multiple regression analysis. Both qualities of work life and work–life balance are multidimensional constructs. Results indicate that the productivity dimension of a work–life balance was influenced by all dimensions of quality of work life except grievance redress. Further, the skill deployment dimension was predicted by all three dimensions of quality of work life. However, none of the quality of work life dimensions had any relation with the efficiency dimension of work–life balance. The study will help managers to ensure employee productivity and skill deployment by enhancing the quality of work life. The study has relevance for employee welfare and organizational output. The study has unearthed new dimensions in quality of work life and work–life balance and has established new relationships.
Interactions between the foreign exchange market and the stock market of a country are considered to be an important internal force of the markets in a financially liberalized environment. If causal relationship from a market to the other is not detected, then informational efficiency exists in the other whereas existence of causality implies that hedging of exposure to one market by taking position in the other market will be effective. The temporal relationship between the forex market and the stock market of developing and developed countries has been studied, especially after the East Asian financial crisis of 1997–98, using various methods like cross-correlation, cross-spectrum, and error correction model, but these methods identify only linear relations. A statistically rigorous approach to the detection of interdependence, including non-linear dynamic relationships, between time series is provided by tools defined using the information theoretic concept of entropy. Entropy is the amount of disorder in the system and also is the amount of information needed to predict the next measurement with a certain precision. The mutual information between two random variables X and Y with a joint probability mass function p(x,y) and marginal mass functions p(x) and p(y), is defined as the relative entropy between the joint distribution p(x,y) and the product distribution p(x)*p(y). Mutual information is the reduction in the uncertainty of X due to the knowledge of Y and vice versa. Since mutual information measures the deviation from independence of the variables, it has been proposed as a tool to measure the relationship between financial market segments. However, mutual information is a symmetric measure and does not contain either dynamic information or directional sense. Even time delayed mutual information does not distinguish information actually exchanged from shared information due to a common input signal or history and therefore does not quantify the actual overlap of the information content of two variables. Another information theoretic measure called transfer entropy has been introduced by Thomas Schreiber (2000) to study the relationship between dynamic systems; the concept has also been applied by some authors to study the causal structure between financial time series. In this paper, an attempt has been made to study the interaction between the stock and the forex markets in India by computing transfer entropy between daily data series of the 50 stock index of the National Stock Exchange of India Limited, viz., Nifty and the exchange rate of Indian Rupee vis- à- vis US Dollar, viz., Reserve Bank of India reference rate. The entire period–November 1995 to March 2007–selected for the study, has been divided into three sub-periods for the purpose of analysis, considering the developments that took place during these sub-periods. The results obtained reveal that: there exist only low level interactions between the stock and the forex markets of India at a time scale of a day or less, although theory suggests interactive relationship between the two markets the flow from the stock market to the forex market is more pronounced than the flow in the reverse direction.
Indian economy in the recent past had experienced a volatile situation in its financial markets. Forex markets witnessed continuous weakening of rupee against dollar, followed by rise in crude oil prices, gold prices, inflation rate which made RBI to interfere with its hike in policy rates to curb the inflation. Effect of one market on another market is not a new thing, but the variations in the degree of impact and co-movements between the markets need to be examined. The main objective of this article is to study the causal relationship between oil, gold, forex and stock markets, for a period ranging from January 2005 till July 2015. This study employs the Granger causality test. The results indicate that the existence of only unidirectional relationship among the variables. The Granger causality test reveals that oil prices contribute towards development and forecasting of exchange rate and gold prices, whereas fluctuations in oil prices are granger caused by Sensex.
This study focuses on identifying the factors that lead to energy consumption in select newly industrialized countries of Asia such as China, India, Indonesia, Malaysia, Philippines and Thailand. GDP, Exchange rate, industrialization, urbanization and trade openness are the select factors identified and such data is obtained for a period from 1980 to 2018. To check for stationarity, ADF unit root test and PP unit root test is employed where all variables are found to be stationary at first difference. OLS regression is applied to identify which factor has an impact on energy consumption. Besides, Johansen cointegration test to establish long run relationship and VECM is employed, where all variables were found to be integrated in the long run however VECM indicated that for China and Malaysia energy consumption is able to achieve equilibrium after a shock in the previous period. To determine causal links between variables, Toda Yamamoto Causality test is applied. Results indicate that industrialization, exchange rate, financial development and trade openness causes energy consumption in China. However, in India and Thailand only industrialization causes energy consumption. GDP causes energy consumption in Indonesia and trade openness causes energy consumption in Malaysia.
Market liquidity ensures the marketability of security and is an indispensable feature of stock markets. Previous studies have emphasized the role of stock market liquidity in empirical finance. However, they have inadequately explored its multidimensional nature. This study eliminates the ambiguities related to market liquidity by precisely measuring it by using popular and proven liquidity measures. As such, the present study aims to evaluate market liquidity in terms of depth, breadth, tightness, and immediacy in the Indian equity market and also identifies crucial interdependencies between liquidity dimensions. The study selects 500 stocks constituting the NIFTY 500 index of the National Stock Exchange, India, as of 26th May 2019. The data on trading volume, bid price, ask price, the number of shares outstanding, closing share prices were retrieved for the period from 1st April 2009 to 31st March 2019. The study employs Share Turnover, Amihud Illiquidity Ratio, Relative Quoted Spreads, and Coefficient of Elasticity of Trading for liquidity measurement. The Vector Auto-Regressive (VAR) model is used to establish the simultaneous relationships between liquidity dimensions. The analysis is conducted at the aggregate market level as well as across turnover based stock groups divided based on their rankings in terms of stock specific share turnover. The empirical results evidenced the presence of consistent depth, strong breadth, and immediacy but lower tightness in the Indian equity market. The market depth and tightness appear to be relevant in determining dimensional interdependencies. Also, less frequently traded stocks exhibit higher illiquidity in the wake of lower tightness. The findings of this study will assist the investors to wisely understand the multifaceted nature of market liquidity and base their trading decisions accordingly. Moreover, the regulators of the stock exchange can devise liquidity enhancing policies based on the directional movements among liquidity dimensions.
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