The money market and the capital market of the Indian financial markets have a symbiotic relationship in the development of the Indian economy. The nature and the characteristics of the markets differ to a large extent as the money market ensures liquidity in the system through the monetary policy by the regulators; capital markets propel and act as the engine driver for the economy in the long term. Therefore, the final throughput of the economy is the aggregation of the output of both the markets. Does that imply that the development of both markets is parallel in nature or is any one superior to the other or are they competitors? To understand the influence of one over the other the research was undertaken through a correlation matrix and time series model. A predictive model was further constructed for predicting the volume of money market instrument on the basis of fourteen days historical.
The assessment and performance of national economy involves the aggregation of major economic indicators. The Consumer Price Index is perhaps the most used indicator among the numerous statistical indicators that are available to the planners, administrators, policy makers and the general public at large to analyse the status of the national economy at any point of time. Additionally, the six core financial indicators of an Indian economy namely, foreign exchanges reserves, supply of money, nominal exchange rates, stock market index, country’s industrial production, and CPI rate have a significant relationship in the development of an economy. The aggregation of these indicators could imply that the change in one indicator may affect the other indicator. The relationship of the indicators was explored and it was concluded that there is a correlation between CPI rate and five specified key financial indicators (considering sub-components). Additionally, there is a multicollinearity present in the specified key financial indicators. Furthermore, by using a comprehensive data science tool a predictive deep learning model was built to predict the CPI rate.
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