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Bounds test shows that there is a co-integrating relationship between the dependent and explanatory variables under both models of the study. Al-Malkawi (Faculty of Business and Law, The British University in Dubai) for his guidance, valuable and timely suggestions and overwhelming support throughout the period of the project.
INTRODUCTION
- Significance & Motivation of Study
- Contribution of Study
- Aim & Research Questions
- Structure of the Dissertation
- INDIAN AUTOMOBILE INDUSTRY
The above discussions indicate that there are quite a few changes happening in the Indian auto industry which have an impact on the company as a whole and consequently on the performance of auto stocks in the equity markets. In the first section, a brief overview of the Indians. This chapter is organized into two subsections. India's automobile industry, which includes both automobile manufacturing as well as automobile components, is one of the major contributors to economic growth in India (Miglani 2019).
Strong domestic demand coupled with supportive government policies have enabled the Indian automobile industry to become one of the world leaders in this field. The table below shows the market share of various types of vehicles in the Indian automobile sector.
Domestic Market Share (2018-19) %
Factors such as increasing disposable income and the presence of a large pool of skilled workers coupled with the fact that India has the second largest road network in the world (4.7 million kilometers) will continue to drive the demand for vehicles in the future . The number of registered motor vehicles per 1,000 population in 2017 stood at 197 (Statista 2019), compared to 838 in the US, indicating a great potential for greater penetration of private vehicle ownership in the coming years. Still, commercial vehicles and three-wheeler categories registered a reduction of (-) 44.44 percent and (-) 12.40 percent respectively during the same period as compared to the previous year.
To highlight this scene, the chart below shows a year-over-year growth rate of domestic sales of the Indian car market. As shown in the figure above, the year-over-year growth rate for all three segments in the Indian auto market has been uneven since 2006.
New Sales - YoY Growth Rate
STOCK MARKETS OF INDIA
A sectoral index of a stock market is a representation of the entire sector and therefore keeps track of the changes in the market over time (Joshi & Giri 2015). The S&P BSE Auto Index is comprised of the constituent companies of the S&P BSE 500 that are categorized as members of the Transportation Equipment sector as defined by the BSE Industry Classification System. The constituents of the index are selected based on market capitalization, trading frequency, industry representation and reputation.
The index is managed by the Index Committee of the Bombay Stock Exchange Limited and was established in 1986. The Nifty Auto Index is tailored to the behavior and performance of the automotive sector, including manufacturers of automobiles and motorcycles, heavy duty vehicles, automotive accessories, tires, etc.
LITERATURE REVIEW
However, the exchange rate is not a better predictor in the short run than in the long run. However, there is a lack of research specifically aimed at finding a possible relationship between these macroeconomic factors and vehicle indices in the Indian stock market. To explore the possible relationship between market share volatility and stock price volatility in the automotive industry.
To identify and evaluate the factors affecting the automotive industry in the EU during the financial crisis. In the long run, the most important macroeconomic indicators of stock market are IIP, WPI and interest rate.
DATA AND METHODOLOGY 4.1 Data Description
- MEASUREMENTS OF VARIABLES AND HYPOTHESIS DEVELOPMENT .1 Dependent Variables
- EMPIRICAL MODEL
- TIME SERIES DATA AND COMPONENTS
- KEY CONCEPTS IN TIME SERIES ANALYSIS
- UNIT ROOT TESTS
- DIAGNOSTIC TESTS
- COINTEGRATION
- METHOD SELECTION FRAMEWORK
- MODEL SPECIFICATION FOR COINTEGRATION WITH ARDL
- ARDL BOUNDS TESTING
As stated earlier, this study attempts to empirically estimate the impact of the above macroeconomic variables on the BSE and NSE auto-indices using time series concepts and methods detailed in the following sections. Some authors claim that monthly data are better at capturing short-term fluctuations of variables (Joshi & Giri 2015). Since this research study uses time series data, a brief overview of important concepts in time series data and time series analysis is provided in the following sections.
This study makes use of discrete time series data for the identified variables on a monthly basis. Two different types of models are used in a time series analysis to incorporate the effect of the above four components. A brief description of some of the important terminologies used in time series analysis is given below.
A crucial assumption underlying most time series analyzes is that it is stationary (Baltagi 2011). According to Gujarati (2003), a time series is considered stationary “if the mean and variance are constant over time and the value of the covariance between two time periods depends on it. The DF test for stationarity is a normal t-test on the coefficient of the delayed dependent variable yt-1 of one of the above models.
The optimal delay lengths of the variables can be obtained by AIC or BIC. These models are generally used for forecasting and also for predicting the multiplier effects of the explanatory variables in the model. Second, given the values of regressors, the expected mean value of the error term ut.
First and foremost, the stationarity of the variables should be checked with unit root tests. On the other hand, we can use the Johansen test if all variables are non-stationary and of the same order.
RESULTS
- DESCRIPTIVE STATISTICS
- GRAPHICAL ANALYSIS .1 Log Transformations
- PHILIPS-PERRON (PP) TEST
- MULTI-COLLINEARITY TEST
- ARDL COINTEGRATION TEST
- MODEL -1 (DEPENDENT VARIABLE: LBSEAUTO) .1 Lag Length Selection
- SUMMARY OF RESULTS
The variance and standard deviation values of these variables indicate that there is not much variance in the sample. As discussed in the previous sections, an ARDL model can capture both the long-term and short-term dynamics of the co-integrated variables. The optimal lag orders must be selected appropriately to ensure that there is no automatic correlation in the error terms (Joshi & Giri 2015).
Thus, we can say that the exchange rate (LER) is a strong and significant indicator of the movement of the BSE auto index in the long run. 0.4715619 indicates that 47.16% of the disequilibrium of the previous month's shock is corrected back to the long-run equilibrium of the current month via the explanatory variables. Furthermore, BSE auto index movement in the short term is strongly predicted by the first lag of LCRUDE and is significant at 5% level.
These arguments may possibly justify the other relationships identified in the long run of this model. The null hypothesis of this test is that there is no structural break in the data. Moreover, the first lag of CRUDE (-0.687) appears to be a significant predictor of Nifty auto index in the short term.
The sequence of diagnostic tests was repeated in the same way for the residuals of the second model with the automatic Nifty index (NiftyAuto) as the dependent variable. There may be two reasons for the presence of auto correlations in model-2 (Gujarati 2003). The ARDL cointegration test in model-1 showed a statistically significant and negative relationship between the exchange rate and the BSE auto index in the long run.
A similar relationship exists between the exchange rate and the Nifty auto index also in the long run as shown by the same test in model-2. Thus we can say that, 47.16% and 123.18% of shock disequilibrium of last month have been corrected back to long run balance in current month through explanatory variables of BSE auto index and Nifty auto index respectively.
CONCLUSION 6.1 Introduction
- Implications and Recommendations
- Future Research
- Durbin Watson Test
- Breusch-Godfrey LM Test H 0 : no serial correlation
- Breusch-Pagan / Cook-Weisberg test for heteroskedasticity H 0 : Constant variance
- ARCH LM Test
- Ramsey RESET Test
- CUSUM Test for stability
- Breusch-Godfrey LM test H 0 : no serial correlation
- Breusch-Pagan / Cook-Weisberg Test H 0 : Constant variance
- ARCH LM Test
- CUSUM Test for stability
Exchange rate appears to be a strong and statistically significant predictor for both BSE auto index and Nifty auto index in the long run. Further, crude oil price, IIP and repo rates have also been found to have a statistically significant relationship with Nifty auto index in the long run. In the short run, the first lag of crude oil is found to have a significant positive association with the BSE auto index and a significant negative association with the Nifty auto index.
India (RBI) uses repo rate as monetary policy to increase or decrease liquidity in the market. But the negative association between IIP and the Nifty auto index indicates that while there is economic growth, the current Indian auto industry is not attractive to investors or speculators in the long run. Since some of the macroeconomic factors examined in this study have been found to be statistically significant predictors of auto indices, investors should study the movement of these determinants to understand the potential impact on the auto index before identifying stocks in this sector to park their money.
As already discussed in the previous chapter, the second model faces the challenges associated with autocorrelation. The findings of the analysis of the second model are thus questionable, as the model is not efficient. A Study of the Factors Affecting Stock Price - A Comparative Study of Stocks in India's Automotive and Information Technology Industry, International Journal of Current Research and Academic Review vol.
Numerical Distribution Functions for Unit Root and Cointegration Tests, Journal of Applied Econometrics, vol.11, pp. International Journal of Emerging Markets, vol. https://www.nseindia.com/products/content/equities/indices/sectoral_indices.htm. Durbin Watson's (DW) test statistic indicates whether there is first-order autocorrelation in the residuals.
