| dc.description.abstract | Abstract
It is believed that the state of the macroeconomy has an impact on the stock market returns, as is evident from the statements of academicians and politicians from time to time. This study is based on a paper by Nai-Fu Chen titled "Financial Investment Opportunities and the Macroeconomy", which was published the Journal of Finance in the year 1991. Upon analysis, it was found that there are several loopholes in the methodology that was adopted by Nai-Fu Chen. The aim of our study is to analyze the impact of the state variable on the stock market returns in the Indian context by using econometric methods. More specifically, this study analyses the impact of Yearly production growth rate (YPL), T-bill rates (TB), Term spread (UTS) and Dividend yield (DP) on the real rate of return realized on the Nifty index.
Several methods were tried out in order to arrive at the optimal relationship involving stock market returns and the different macroeconomic state variables. From them, it was concluded that YPL has a strong positive correlation with the market returns during the current and the next quarter, but a negative correlation for quarters further down the line. TB is negatively correlated with the market returns as expected. From the univariate regressions, it was concluded that DP has a strong positive correlation with the market returns one and two quarters hence.
Other methods were also used in order to find an optimal relationship between the market returns and the state variables, like the use of log linear models, multiple regression and data partitioning. It was evident from the results that log linear models are not appropriate for this case. We have also come to the conclusion that DP should be a part of any model explaining the real returns realized on the market. Hence, any of these combinations would be efficient: YPL and DP, TB and DP, UTS and DP. It was also concluded from the Ramsey's RESET test that higher order terms of the independent variables should not be used in the model. Though the method of data partitioning yielded very good results in some cases, care should be exercised in interpreting the results as the sample size is not very large in those cases. | en_US |
