Short-term
Economic Indicators, Stock Market Indexes and Indian Oil and Gas Stocks Returns
Rama
Krishna Yelamanchili PhD
Associate
Professor of Finance
Department of Finance and Accounting
ICFAI Business School,
IFHE-Hyderabad, India
E-mail: [email protected]
Abstract
In this
paper we examine the causal relationship between short term economic
indicators, stock market indexes and oil and gas stocks returns. We postulate
that economic indicators positively and significantly cause and predict stock
market indexes and oil and gas stock returns in short run. In addition, we
posit that stock market indexes cause and predict oil and gas stock returns in
short run. To test our hypotheses we chose four short-term economic indicators,
two stock market indexes, and 10 oil and gas companies. Our results indicate
that there is no causal relationship between both short-term economic
indicators and stock market indexes, and between short-term economic indicators
and oil and gas stock returns. However, we receive support to one of our
hypotheses that stock market indexes cause oil and gas stock returns. This
causation is contemporaneous only and we observe that stock market indexes lack
short-term predictive power of oil and gas stock returns. We conclude that investors
need to be vigilant in considering coincident indicators as explanatory
variables to predict stock returns. We suggest that stock market indexes are
helpful to predict contemporaneous returns but not future returns of oil and
gas stocks.
1. Introduction
Traditionally,
stock market index is viewed as a leading economic indicator and Index of
Industrial Production (IIP) is viewed as a coincident or contemporaneous
economic indicator. Few studies examine the causal relationship between stock
market index and industrial production. Some argue that industrial production
causes stock market movements, while others argue that stock market movements
cause industrial production. We notice that most of the studies examine the
relationship between economic factors and stock market movements rather than
analyzing the influence of economic factors on individual stocks. We observe
that there is limited research that examines the causal relationship between
index of industrial production, stock market index, and individual stock
returns. Therefore, this paper attempts to see whether stock returns can be
explained with economic indicators and stock market indexes, and also to see
whether stock returns can be predicted with those economic indicators and stock
market indexes. It can be seen that industrial production increases during
economic expansion and decreases during recession and thus change in industrial
production would signal a change in economy. Similarly, stock market indexes
reflect investors� current and future expectations about companies�
performance. A positive movement in stock market represents investors
optimistic outlook, and a negative movement indicates investors� pessimistic
outlook. Our proposition is that high industrial production raise companies
sales and profits, enhances investors optimism and leads to positive movement
in stock market which rightly result in rise in stock returns. We postulate
that stock returns are influenced by stock market movements and also by IIP
which constitute the aggregate of manufacturing, mining, and electricity
production.
This paper assumes significance in the light that India is
the world�s third largest consumer of primary energy and one of the fastest
growing economies in the world. India produces less than one per cent of the
world�s crude oil, however, consumes nearly five per cent of the world�s crude
oil production. India imports 80 per cent of its crude oil. The oil and gas
industry is among the eight core industries in India and plays main role in
inducing decision making for all the other important industries of the economy.
As a growing economy India�s appetite for energy is also growing. In addition,
as Indian households generating higher disposable incomes, there is significant
boost in vehicle sales, transportation, tourism, and consumer goods. It is perceived
that there exists a strong correlation between oil and gas companies� financial
performance and the IIP, which is largely driven by economic activity. It is
also observed that in the recent past there is a spurt in India�s natural gas
consumption and natural gas imports. Natural gas is seen to dominate the
mainstay sectors of fertilizers, power, petrochemicals, steel, manufacturing
industries, consumer goods manufacturers, domestic LPG and households. India�s
economic growth is closely related to energy demand. Therefore the need for oil
and gas is projected to grow more, thereby making the sector quite favorable
for investment.
In this paper, we argue that short-term economic performance
indicators will influence stock market movements and stock returns. We also propose
that short-term economic indicators and stock market indexes predict stock
returns in short run. To examine our hypotheses we chose oil and gas stocks. We
expect that positive economic indicators reflect increased business activity
which in turn creates demand for oil and gas products because majority of industries
directly or indirectly consume oil and gas in their production, operations and
distribution of goods and services. Furthermore, a high economic activity
indicates higher income to workforce, which in turn boosts sales of certain
goods and services like automobile, transportation etc., which in turn affect
the financial performance of oil and gas stocks and their stock returns. In
this paper we investigate the causal relationship between four short-term
economic performance indicators namely, IIP, manufacturing index, mining index,
electricity index, and two broad market indexes namely S & P BSE SENSEX,
and stock market Oil and Gas sectorial index with 10 oil and gas companies of
India during the period 2012-2019. Our results show that there is no causal
relationship between short-term economic indicators and broad stock market
indicators and oil and gas stocks. Similarly, we do not find any predictive power
of short-term economic indicators. On the other hand we receive support for our
hypothesis that stock market indexes cause oil and gas stocks returns. However,
in contrast to our hypothesis stock market indexes fail to predict oil and gas stock
returns in short-run during our study period.
The reminder of the paper is organized as follows. Section 2
presents literature review and theoretical relationship between index of
industrial production, stock markets, and stock returns. In section 3 we
present our data and methodology. Section 4 presents empirical results. Section
5 discusses our empirical findings. Finally, section 6 provides a
conclusion.�
Over the
past few decades, part of the literature has analyzed the relationship between
oil prices and stock market returns, while another section of the literature
has analyzed the connection between oil prices and index of industrial
production. Nevertheless, a small part of the literature has addressed the
relationship among industrial production, the stock market indexes and oil and
gas stocks returns. Although changes in industrial production is considered an
important factor for understanding the movements of stock markets and stock
returns, there is still no definitive consensus regarding the relationship
between IIP, stock market indexes and individual stock returns.
The role of industrial production as a variable in the
determination of stock returns remains an open question, since the results of
number of empirical studies do not definitively determine a significant and
reliable statistical relationship between them (Bilson et al., 2001; Fama, 1981; Gultekin, 1983). However, Chen
et al., (1986) identify industrial production as a vital factor for the
determination of stock returns, while Cutler et al., (1989) find that stock returns correlate significantly and
positively with industrial production growth over the period 1926-1986. Chen (1991) finds that macro-economic variables help forecast the
market premium and future growth of the economy. Stock & Watson (2003) report that leading economic indicators forecast future
performance of stocks returns, however, these forecasts are not consistent. Comincioli (1996) suggests that stock prices do Granger cause economic
activity and stock market does predict the economy. Flannery & Protopapadakis (2002) report
that macro-economic variables significantly increase stock market trading and
have positive influence on stock returns. Sadorsky (1999) find
evidence for influence of oil prices on stock returns. They observed that oil
prices and oil price volatility both play important roles in affecting stock
returns. Besides, Errunze & Hogan (1998) conclude that the volatility of
industrial production has a negative impact on the stock market. Chen et al., (1986) reveals
that industrial production responds positively to shocks in stock returns. Lamont (2001) finds that tracking portfolio returns
track the growth rates of economic variable returns. Ewing & Thompson (2007); Serletis & Shahmoradi (2005) examine the relationship between industrial production and
oil stocks. Balver et
al.,
(1990) show that
IIP is able to predict stock returns and find that the relationship between IIP
and stock returns is stronger with significant coefficient of determination. Young (2006) finds
there to be a statistically significant relationship using the seasonally
adjusted index after a lag, with the explanation that increased industrial
production leads to increased economic activity, thus resulting in higher
earnings for companies. However, Young
(2006) find that for the sub-period from
1988-2000 indicate that industrial production can no longer predict stock
returns which confirms the US economy�s transition from a manufacturing to a
service oriented economy. Chan et al., (1998) reports that the macroeconomic factors
generally make a poor showing. In Indian context Patel
(2012) reports stock market index Granger
cause Index of Industrial Production (IIP). In contrast, Singh (2014) reports that Indian stock market index
does not Granger cause Index of Industrial Production. Joshi (2015) reports that Indian stock market
influence the IIP.
The goal of this paper is to determine whether IIP relate to
oil and gas stock returns and can IIP predict future stock returns. As well as
we aim to assess whether relationship holds between stock market indexes and oil
and gas stocks returns, and can market indexes predict future stock returns.
In this paper we aim to measure causal
relationship between economic indicators, stock market indexes and stock
prices. To achieve this broad objective, we set specific objectives as, to
examine the influence of short-term economic indicators on oil and gas stocks,
to measure the impact of stock market indexes on stock returns, to evaluate the
predictability of short-term economic indicators and stock market indexes of
stock returns, and to understand the contemporaneous relationship among oil and
gas stocks To validate the objectives set in this paper, we postulate certain
hypotheses as H1: Short-term economic indicators have significant
positive effect on stock returns, H2: Stock market indexes have
significant positive effect on stock returns, H3: Short term economic
indicators have short-term predictive ability of oil and gas stock returns. H4
Stock market indexes have short-term predictive ability of oil and gas stock
returns
This
paper uses a monthly data that spans from April 2012 to March 2019. Using OLS
and VAR models, this paper analyzes the relationship among four short-term
economic indicators, two stock market indexes, and 10 oil and gas stocks in
India. The four short-term economic variables considered in this paper are
Index of Industrial Production (IIP), Manufacturing, Mining, Electricity index.
�Index of industrial production (IIP) has
historically been one of the most well-known and well-used short-term economic
performance indicators. The all India�
IIP is a composite indicator that measures the short-term changes in the
volume of production of a basket of industrial products during a given period
with respect to that in a chosen base period. The all India IIP provides a
single representative figure to measure the general level of industrial
activity in the economy on a monthly basis. The IIP is calculated with
aggregation of manufacturing, mining, and electricity. Manufacturing has 77.63%
weightage; Mining represents 14.37 % weightage, and Electricity 7.99%
weightage. Use based classification of IIP is primary goods (34.05%), capital
goods (8.22%), intermediate goods (17.22%), consumer durables (12.84%),
consumer non-durables (15.33%), and infrastructure / construction goods
(12.34%). The two market indexes that are considered as explanatory variables
in this paper are S & P BSE SENSEX index, BSE Oil & Gas index. We
obtain list of oil and gas stocks from BSE Oil & Gas Index. This index has
10 oil and gas stocks listed in Bombay Stock Exchange of India (BSE).The 10 oil
and gas stocks considered in this paper has 90 per cent of market share in
refining, sales and distribution of petroleum products, natural gas, and
lubricants. These companies cater oil and gas needs of all market segments in
the country. Of these 10 stocks seven are public sector units and three are
private companies. Four of the public sector units (Bharat Petroleum
Corporation Limited (BPCL), Hindustan Petroleum Corporation Limited (HPCL),
Indian Oil Corporation (IOC), Oil India Limited (OIL), are in the business of
refinery, sales and distribution; two (Gas Authority of India Limited (GAIL), Oil
and Natural Gas Corporation (ONGC) are in exploration and refinery of oil and
natural gas; and one (Indraprasta Gas Limited, IGL) is in refinery and
distribution of natural gas. One of the private companies (Castrol) is in
exclusive marketing of lubricants and one (PetroNet) is in production and
distribution of natural gas, and the third one (Reliance Industries Limited,
RIL) is a conglomerate. We source monthly data of short-term economic
indicators from the Indian Government�s Ministry of Statistics and Programme
Implementation database, whereas, market indexes data and stock prices data are
taken from BSE official website. This period (2012-2019) is specifically chosen
due to change in base period of short-term economic indicators. In India IIP
and related indexes are available since 1937 and the base year changes
occasionally to reflect current economic and market conditions. Recent change
in base-year happened in 2012. Economic indicators considered in this study are
measured on 100 points index, market indexes are measured on free-float market
capitalization of index components in points and stock prices are measured in
Indian rupee. Initially we run analysis on raw data and later on we calculate
log returns of all the variables considered in this paper.
Table 1 reports descriptive statistics
of short-term economic indicators and market indexes on their raw data. In five
variables Skewness is positive and near to zero and excess Kurtosis values is
less than 3, in Electricity index Skewness is negative and excess Kurtosis is
less than 3. Depending Skewness and Kurtosis the normality condition is not
rejected at 5% significance level for all the series. We then proceed to check
the normality of all series using Jarque-Bera (JB) test. The JB test rejects
the hypothesis of normal distribution. From table 1 we learn that the short-term
economic indicators and stock market index is all time low in the year 2012.
These indicators reflect the events in international and domestic level during
that period. During year 2012 there was sovereign debt crisis in the Euro zone,
political uncertainty in Middle East, rise in crude oil prices, and Japan was
struck with earthquake are the few to mention. At domestic level the Indian GDP
estimation is below 7 per cent, slowdown in policy reforms, domestic demand is
lowest in previous ten years. In addition, there is slowdown in the
manufacturing and mining sectors, increased gap between demand and supply for
electricity which in turn affected the industrial production and exports. All these
factors are pertinently captured by short-term economic indicators. On the
other hand the short-term economic indicators report highest values in the year
2018. During this period India witnessed GDP of 7.3 per cent, manufacturing
sector registered robust growth, revival is seen in investment activity, and witnessed
improvement in global demand for Indian products. However, the IIP is slightly
hit by gold and jewelers industry due to banking fraud investigated in one of
the leading public sector banks. We also notice that along with manufacturing
sector, mining and electricity sectors report record growth during this period.
Table 1. Descriptive statistics of raw data
|
|
IIP |
Manufacturing |
Mining |
Electricity |
S & P BSE SENSEX |
Oil & Gas Index |
|
�Mean |
115.83 |
117.23 |
99.32 |
131.78 |
26872.70 |
11068.93 |
|
�Median |
115.40 |
116.25 |
98.30 |
133.60 |
26833.84 |
9898.69 |
|
�Maximum |
140.30 |
140.20 |
132.60 |
167.20 |
38672.91 |
16552.40 |
|
�Minimum |
98.30 |
99.00 |
82.50 |
94.10 |
16218.53 |
7587.84 |
|
�Std. Dev. |
10.31 |
10.09 |
11.14 |
19.10 |
6059.13 |
2629.40 |
|
�Skewness |
0.30 |
0.28 |
0.80 |
-0.10 |
0.09 |
0.57 |
|
�Kurtosis |
2.31 |
2.24 |
3.38 |
1.95 |
2.04 |
1.89 |
|
�Jarque-Bera |
2.97 |
3.13 |
9.43 |
4.02 |
3.34 |
8.79 |
|
�Probability |
0.23 |
0.21 |
0.01 |
0.13 |
0.19 |
0.01 |
|
�Observations |
84 |
84 |
84 |
84 |
84 |
84 |
We first analyze our series
by plot the graphs and then draw correlogram to check whether any seasonality
or cycles are present in the series. We then perform Hodrick-Prescott Filter
test to verify the seasonality. We do not find any seasonality in our series. We
then try to identify whether the series are stationary or non-stationary at
their level. To verify this, we use three unit-root tests in level by including
intercept and trend in the test equation (ADF, ADF-GLS, KPSS) and present results
in Table 2. The ADF and ADF-GLS tests for unit root and the null hypothesis is
that the series is non-stationary. For the KPSS test the null hypothesis is
that the data is stationary. Results present in Table 2 indicate that we cannot
reject the null hypothesis of a unit root for all series. All the three tests
give similar results and confirm the presence of unit root or non-stationary of
data. As unit root is present at level we compute log returns of the data, run
descriptive statistics, and the three unit root tests with intercept once
again. Table 3 and 4 reports those results. The JB test results confirm the
normality of stocks series at 5% significance level. We re-verified presence of
unit root with three tests and if two out of the three tests confirm the
presence of a unit root, then it is concluded that the series has a unit root.
The unit root tests show that log return series of market indexes and stocks returns
are stationary, therefore, we use log returns in the model for further
analysis.
Table 2. Results of stationarity tests on raw data
|
|
ADF |
DF - GLS |
KPSS |
||
|
Variable |
t-Statistic |
Prob.* |
t-Statistic |
Prob. |
LM-Stat |
|
IIP |
1.09 |
1.00 |
3.33 |
0.00 |
1.28 |
|
Manufacturing (Mgf) |
0.90 |
1.00 |
3.35 |
0.00 |
1.28 |
|
Mining (Ming) |
0.95 |
1.00 |
-0.31 |
0.76 |
0.84 |
|
Electricity (Elec) |
-1.24 |
0.65 |
-0.28 |
0.78 |
1.09 |
|
S&PSENSEX (BSE) |
-0.40 |
0.90 |
1.05 |
0.30 |
1.06 |
|
Oil&Gas (O&G) |
-0.80 |
0.81 |
-0.02 |
0.99 |
0.93 |
|
BPCL |
-1.84 |
0.36 |
-1.71 |
0.09 |
0.27 |
|
HPCL |
-2.02 |
0.28 |
-1.86 |
0.07 |
0.30 |
|
IOC |
-1.81 |
0.37 |
-1.79 |
0.08 |
0.25 |
|
OIL |
-0.69 |
0.84 |
-0.56 |
0.58 |
1.05 |
|
ONGC |
-1.18 |
0.68 |
-1.15 |
0.25 |
0.90 |
|
Gail |
-3.23 |
0.02 |
-2.76 |
0.01 |
0.22 |
|
Castrol |
-1.79 |
0.38 |
-1.03 |
0.30 |
0.42 |
|
IGL |
-2.25 |
0.19 |
-1.96 |
0.05 |
0.41 |
|
PetroNet |
-1.73 |
0.41 |
-1.32 |
0.19 |
0.73 |
|
Reliance |
-2.79 |
0.06 |
-1.98 |
0.05 |
0.88 |
Table 3. Descriptive statistics of log returns
|
|
IIP |
Mgf |
Ming |
Elec |
BSE |
O & G |
||||||||||
|
�Mean |
0.42 |
0.41 |
0.35 |
0.53 |
0.97 |
0.78 |
||||||||||
|
�Median |
0.22 |
0.17 |
1.02 |
0.88 |
1 |
0.65 |
||||||||||
|
�Max |
12.76 |
11.9 |
17.84 |
15.52 |
9.69 |
12.82 |
||||||||||
|
�Min |
-13.49 |
-13.01 |
-25.66 |
-13.42 |
-7.81 |
-11.96 |
||||||||||
|
�S D |
5.54 |
5.42 |
9.07 |
6.02 |
3.93 |
5.43 |
||||||||||
|
�Skew |
-0.27 |
-0.38 |
-0.6 |
0.03 |
-0.09 |
-0.03 |
||||||||||
|
�Kurt |
3.25 |
3.23 |
3.74 |
3.22 |
2.5 |
2.8 |
||||||||||
|
�J-B |
1.23 |
2.2 |
6.84 |
0.17 |
1 |
0.15 |
||||||||||
|
�P |
0.54 |
0.33 |
0.03 |
0.92 |
0.61 |
0.93 |
||||||||||
|
|
BPCL |
HPCL |
IOC |
OIL |
Gail |
ONG |
Castrol |
IGL |
Petro |
RIL |
||||||
|
�Mean |
1.04 |
1.25 |
1.08 |
-1.09 |
0.06 |
-0.63 |
0.35 |
2.34 |
1.55 |
1.56 |
||||||
|
�Median |
2.11 |
2.48 |
0.47 |
-0.79 |
0.8 |
-0.33 |
-0.05 |
2.45 |
1.93 |
1.12 |
||||||
|
�Max |
19.79 |
24.14 |
31.76 |
21.05 |
20.35 |
23.94 |
20.84 |
21.35 |
19.21 |
19.79 |
||||||
|
�Min |
-30.78 |
-28.47 |
-19.73 |
-48.66 |
-33 |
-41.18 |
-13.61 |
-17.34 |
-14.76 |
-17.01 |
||||||
|
�S D |
10.06 |
10.99 |
8.63 |
9.2 |
9.03 |
9 |
6.71 |
7.44 |
6.58 |
7.11 |
||||||
|
�Skew |
-0.61 |
-0.07 |
0.62 |
-1.84 |
-0.95 |
-0.87 |
0.44 |
0.21 |
0.02 |
0.22 |
||||||
|
�Kurt |
3.86 |
2.92 |
4.18 |
11.5 |
5.77 |
6.73 |
3.71 |
3.08 |
3 |
3.29 |
||||||
|
�J-B |
7.63 |
0.09 |
10.15 |
296.85 |
38.94 |
58.48 |
4.45 |
0.66 |
0.01 |
0.98 |
||||||
|
�P |
0.02 |
0.96 |
0.01 |
0 |
0 |
0 |
0.11 |
0.72 |
0.99 |
0.61 |
||||||
|
�Obr |
83 |
83 |
83 |
83 |
83 |
83 |
83 |
83 |
83 |
83 |
||||||
Table 4. Results of stationarity tests on log returns
|
ADF |
DF - GLS |
KPSS |
|||
|
Variable |
t-Statistic |
Prob.* |
t-Statistic |
Prob. |
LM-Stat |
|
IIP |
-4.03 |
0.00 |
-0.04 |
0.97 |
0.032 |
|
Mgf |
-3.91 |
0.00 |
0.06 |
0.95 |
0.028 |
|
Mining |
-3.98 |
0.00 |
-0.69 |
0.49 |
0.067 |
|
Electricity |
-3.86 |
0.00 |
-0.27 |
0.79 |
0.025 |
|
S & P SENSEX |
-10.09 |
0.00 |
-1.89 |
0.06 |
0.05 |
|
Oil & Gas |
-9.50 |
0.00 |
-7.48 |
0.00 |
0.05 |
|
BPCL |
-10.67 |
0.00 |
-10.10 |
0.00 |
0.269 |
|
HPCL |
-9.38 |
0.00 |
-9.20 |
0.00 |
0.195 |
|
IOC |
-9.67 |
0.00 |
-9.15 |
0.00 |
0.101 |
|
OIL |
-9.06 |
0.00 |
-8.80 |
0.00 |
0.162 |
|
ONGC |
-8.99 |
0.00 |
-8.23 |
0.00 |
0.107 |
|
Gail |
-10.26 |
0.00 |
-10.05 |
0.00 |
0.076 |
|
Castrol |
-9.88 |
0.00 |
-9.75 |
0.00 |
0.239 |
|
IGL |
-11.10 |
0.00 |
-1.74 |
0.09 |
0.086 |
|
PetroNet |
-8.13 |
0.00 |
-5.50 |
0.00 |
0.124 |
|
Reliance |
-10.65 |
0.00 |
-8.29 |
0.00 |
0.396 |
We then perform cross-order
correlation among series and present results in Table 6. We find significant
positive correlation among four short-term economic indicators. We also observe
that economic indicators have negative correlation with oil and gas stocks and
these correlations are low and statistically insignificant. On the other hand we
observe significant positive correlations among 10 oil and gas stocks and these
correlations range between 0.82 and 0.22 and statistically significant at 5%
significance level.
One of our aims in this
paper is to measure the influence of short-term economic indicators on oil and
gas stocks returns. In order to do this, we regress economic indicators on
stocks. Table 7 reports OLS results. From table 7 we learn that there is no
significant causation between short-term economic indicators and oil and gas
stocks. However, we find significant negative causal relationship between four
economic indicators and one of the 10 stocks (GAIL) at 10% significance level.
In one another stock (IOC), Electricity indicator is positively related. Except
these two causations economic indicators fail to cause any other oil and gas stocks.
Our results indicate that there is no causal relationship between short-term
economic indicators and eight oil stocks returns. Our result contradicts that
of Comincioli (1996), who suggests that stock market movements Granger cause
economic activity. To confirm our OLS results we run pair-wise Granger
causality tests and fail to find any causation happening from short-term
economic indicators to oil and gas stock returns or from oil and gas stock
returns to short-term economic indicators. Granger causality test results
confirms the results of cross order correlation and OLS results and indicate
that there is no causation between economic indicators and oil and gas stock
returns in any direction. Results of pair-wise granger causality are present in
Table 5.
We then employ cross-order correlation
between market indexes and stocks. In this paper we use two stock market indexes,
one is broad market index (S & P BSE SENSEX) and the other is sectorial
index (S & P Oil & Gas). Result of cross-order correlations is present
in Table 8. From Table 8, we learn that both the indexes have significant
positive correlations with oil and gas stocks returns. Further, these
correlations are strong and range between 0.28 and 0.73 and statistically
significant at 1% significance level. Results of cross-order correlations among
market indexes and stocks returns are in contrast with our earlier results of
cross-order correlation among economic indicators and stocks returns. Our
results indicate that there is no correlation among economic indicators and
stocks returns. Whereas, we observe significant positive correlation among
market indices and oil and gas stocks returns.
Table 5. Pair-wise granger causality tests between IIP
and oil and gas stocks
|
Pairwise
Granger Causality Tests |
������������������� F-Statistic |
��������������������� Prob. |
|
�IIP does not Granger Cause BPCL |
0.00 |
0.99 |
|
�BPCL does not Granger Cause IIP |
1.89 |
0.17 |
|
�IIP does not Granger Cause HPCL |
0.47 |
0.50 |
|
�HPCL does not Granger Cause IIP |
0.62 |
0.43 |
|
�IIP does not Granger Cause IOC |
0.31 |
0.58 |
|
�IOC does not Granger Cause IIP |
1.26 |
0.27 |
|
�IIP does not Granger Cause OIL |
0.01 |
0.91 |
|
�OIL does not Granger Cause IIP |
0.42 |
0.52 |
|
�IIP does not Granger Cause GAIL |
0.43 |
0.52 |
|
�GAIL does not Granger Cause IIP |
1.68 |
0.20 |
|
�IIP does not Granger Cause ONGC |
0.15 |
0.70 |
|
�ONGC does not Granger Cause IIP |
3.49 |
0.07 |
|
�IIP does not Granger Cause CASTROL |
0.97 |
0.33 |
|
�CASTROL does not Granger Cause IIP |
0.59 |
0.44 |
|
�IIP does not Granger Cause PETRO |
0.16 |
0.69 |
|
�PETRO does not Granger Cause IIP |
0.03 |
0.87 |
|
�IIP does not Granger Cause RIL |
0.01 |
0.94 |
|
�RIL does not Granger Cause IIP |
0.36 |
0.55 |
|
�IIP does not Granger Cause IGL |
0.18 |
0.68 |
|
�IGL does not Granger Cause IIP |
0.63 |
0.43 |
From cross-order
correlations among market index, sectorial index and stocks returns we learn
that these correlations are moderate, thus suggesting that multicollinearity
should not be an issue during the estimation process. In continuation to
correlation analysis, we run bivariate OLS regression between each market
indicator and individual oil and gas stocks returns. Results present in Table 9.
From the results in Table 9 we find that both market index and sectorial index
cause stocks at 1% significance level. The results of causality test indicate
that coefficients range between 0.55 and 1.21 and r2 value range
between 0.10 and 0.37. These results suggest that broad market index and
sectorial index have explanatory power about the variability in stock returns
for contemporaneous period.
Table 6. Cross-order
correlations among short-term economic indicators and oil and gas stocks
|
Variable |
IIP |
Mgf |
Mining |
Elec |
BPCL |
Castrol |
Gail |
HPCL |
IGL |
IOC |
OIL |
ONGC |
Petro |
RIL |
|
IIP |
1 |
|||||||||||||
|
Mgf |
.99** |
1 |
||||||||||||
|
Mining |
.86** |
.80** |
1 |
|||||||||||
|
Elec |
.62** |
.57** |
.44** |
1 |
||||||||||
|
BPCL |
.10 |
.08 |
.11 |
.18 |
1 |
|||||||||
|
CASTROL |
.10 |
.11 |
.02 |
.05 |
.18 |
1 |
||||||||
|
GAIL |
-.20 |
-.18 |
-.18 |
-.20 |
.36** |
.04 |
1 |
|||||||
|
HPCL |
.11 |
.10 |
.11 |
.15 |
.81** |
.32** |
.31** |
1 |
||||||
|
IGL |
-.02 |
.00 |
-.08 |
-.04 |
.16 |
.42** |
.37** |
.29** |
1 |
|||||
|
IOC |
.11 |
.11 |
.04 |
.22* |
.61** |
.20 |
.24* |
.72** |
.25* |
1 |
||||
|
OIL |
-.13 |
-.12 |
-.16 |
-.03 |
.18 |
.01 |
.39** |
.19 |
.35** |
.22* |
1 |
|||
|
ONGC |
-.04 |
-.05 |
-.04 |
.12 |
.47** |
.17 |
.41** |
.39** |
.25* |
.31** |
.41** |
1 |
||
|
PETRO |
-.03 |
-.03 |
-.06 |
.06 |
.24* |
.28* |
.42** |
.33** |
.49** |
.29** |
.27* |
.35** |
1 |
|
|
RIL |
-.07 |
-.06 |
-.12 |
.02 |
.24* |
.14 |
.26* |
.16 |
.32** |
.25* |
.33** |
.29** |
.23* |
1 |
|
**. Correlation is significant at the
0.01 level (2-tailed).�� *. Correlation
is significant at the 0.05 level (2-tailed). |
||||||||||||||
Table 7.
Contemporaneous Causal Relation among short-term economic indicators and oil
and gas stocks
|
Index |
IIP |
Mining |
Manufacturing |
Electricity |
|||||||||||||||
|
Stock |
Beta |
R2 |
F |
Sig. |
Beta |
R2 |
F |
Sig. |
Beta |
R2 |
F |
Sig. |
Beta |
R2 |
F |
Sig. |
|||
|
BPCL |
0.19 |
0.01 |
0.89 |
0.35 |
0.13 |
0.01 |
1.07 |
0.30 |
0.15 |
0.01 |
0.54 |
0.47 |
0.31 |
0.03 |
2.82 |
0.10 |
|||
|
HPCL |
0.23 |
0.01 |
1.14 |
0.29 |
0.13 |
0.01 |
1.01 |
0.32 |
0.21 |
0.01 |
0.90 |
0.35 |
0.27 |
0.02 |
1.87 |
0.18 |
|||
|
IOC |
0.18 |
0.01 |
1.12 |
0.29 |
0.04 |
0.00 |
0.14 |
0.71 |
0.18 |
0.01 |
1.00 |
0.32 |
0.32 |
0.05 |
4.20 |
0.04 |
|||
|
OIL |
-0.22 |
0.02 |
1.51 |
0.22 |
-0.17 |
0.03 |
2.38 |
0.13 |
-0.22 |
0.02 |
1.33 |
0.25 |
-0.06 |
0.00 |
0.12 |
0.73 |
|||
|
ONGC |
-0.07 |
0.00 |
0.13 |
0.72 |
-0.04 |
0.00 |
0.15 |
0.70 |
-0.10 |
0.00 |
0.28 |
0.60 |
0.19 |
0.02 |
1.38 |
0.24 |
|||
|
Gail |
-0.33 |
0.04 |
3.43 |
0.07 |
-0.18 |
0.03 |
2.77 |
0.10 |
-0.31 |
0.03 |
2.91 |
0.09 |
-0.30 |
0.04 |
3.39 |
0.07 |
|||
|
IGL |
-0.03 |
0.00 |
0.03 |
0.86 |
-0.07 |
0.01 |
0.65 |
0.42 |
0.00 |
0.00 |
0.00 |
0.98 |
-0.05 |
0.00 |
0.14 |
0.71 |
|||
|
Castrol |
0.12 |
0.01 |
0.85 |
0.36 |
0.02 |
0.00 |
0.06 |
0.81 |
0.15 |
0.01 |
1.14 |
0.29 |
0.06 |
0.00 |
0.23 |
0.63 |
|||
|
PetroNet |
-0.04 |
0.00 |
0.11 |
0.74 |
-0.04 |
0.00 |
0.29 |
0.59 |
-0.05 |
0.00 |
0.12 |
0.73 |
0.07 |
0.00 |
0.29 |
0.59 |
|||
|
Reliance |
-0.09 |
0.00 |
0.40 |
0.53 |
-0.09 |
0.01 |
1.18 |
0.28 |
-0.08 |
0.00 |
0.34 |
0.56 |
0.03 |
0.00 |
0.05 |
0.83 |
|||
Table 8. Cross-order
correlation among stock market indexes and oil and gas stocks returns
|
|
BSE
SENSEX |
OilGas |
BPCL |
HPCL |
IOC |
OIL |
Gail |
ONGC |
Castrol |
IGL |
Petro |
RIL |
|
BSESENSEX |
1 |
|||||||||||
|
OilGas |
.72** |
1 |
||||||||||
|
BPCL |
.47** |
.65** |
1 |
|||||||||
|
HPCL |
.43** |
.61** |
.81** |
1 |
||||||||
|
IOC |
.33** |
.63** |
.61** |
.72** |
1 |
|||||||
|
OIL |
.33** |
.43** |
.18 |
.19 |
.22* |
1 |
||||||
|
Gail |
.40** |
.55** |
.36** |
.31** |
.24* |
.39** |
1 |
|||||
|
ONGC |
.51** |
.65** |
.47** |
.39** |
.30** |
.40** |
.41** |
1 |
||||
|
Castrol |
.32** |
.27* |
.18 |
.32** |
.20 |
.01 |
.04 |
.17 |
1 |
|||
|
IGL |
.44** |
.45** |
.16 |
.29** |
.25* |
.34** |
.36** |
.24* |
.42** |
1 |
||
|
Petro |
.35** |
.45** |
.24* |
.33** |
.29** |
.22* |
.42** |
.35** |
.27* |
.49** |
1 |
|
|
RIL |
.61** |
.71** |
.24* |
.16 |
.25* |
.33** |
.26* |
.29** |
.14 |
.31** |
.22* |
1 |
|
**. Correlation is significant at the
0.01 level (2-tailed).�� *. Correlation
is significant at the 0.05 level (2-tailed). |
||||||||||||
Table 9.
Contemporaneous causal relationship among stock market indexes and oil and gas
stocks returns
|
Explanatory
variable |
BSE
SENSEX |
Oil
& Gas |
||||||
|
Stock |
Beta |
R2 |
F |
Sig. |
Beta |
R2 |
F |
Sig. |
|
BPCL |
1.21 |
0.22 |
23.51 |
0.00 |
1.20 |
0.42 |
59.18 |
0.00 |
|
HPCL |
1.21 |
0.19 |
18.72 |
0.00 |
1.24 |
0.37 |
48.11 |
0.00 |
|
IOC |
0.73 |
0.11 |
9.97 |
0.00 |
1.00 |
0.40 |
53.21 |
0.00 |
|
OIL |
0.79 |
0.11 |
10.29 |
0.00 |
0.74 |
0.19 |
18.94 |
0.00 |
|
ONGC |
1.18 |
0.27 |
29.47 |
0.00 |
1.09 |
0.43 |
62.04 |
0.00 |
|
Gail |
0.92 |
0.16 |
15.56 |
0.00 |
0.93 |
0.31 |
36.33 |
0.00 |
|
IGL |
0.84 |
0.20 |
20.13 |
0.00 |
0.62 |
0.21 |
21.13 |
0.00 |
|
Castrol |
0.55 |
0.10 |
9.38 |
0.00 |
0.34 |
0.08 |
6.61 |
0.01 |
|
PetroNet |
0.60 |
0.13 |
11.98 |
0.00 |
0.55 |
0.21 |
21.33 |
0.00 |
|
Reliance |
1.11 |
0.37 |
48.49 |
0.00 |
0.93 |
0.51 |
83.18 |
0.00 |
As we find causal relationship between market
indexes and oil and gas stocks returns, we are curious to know whether these
two market indexes can help predict short-term returns of oil and gas stocks.
To know this we relate current returns of stocks with one-lag of market
indexes. By doing this our objective is to assess the short-term predictability
of market indexes of stock returns. Results are present in Table 10. To our
surprise we do not find any predictability of S & P BSE SENSEX and S & P
Oil & Gas indexes of stock returns. This result indicates that current
month market movement has no predictive power of next month stock returns. We
make line estimation of S & P BSE SENSEX on all the 10 stocks and present
in Figure 1. From Figure 1 it is clearly evident that market index fails to predict
next month stock returns and we also observe that observations scatter far away
from estimation line.
Table 10. Predictive
Regression of stock market indexes of oil and gas stocks
|
Explanatory
Variable |
BSE
SENSEX |
Oil
& Gas |
||||||
|
Stock |
Beta |
R2 |
F |
Sig. |
Beta |
R2 |
F |
Sig. |
|
BPCL |
0.00 |
0.00 |
0.00 |
1.00 |
-0.20 |
0.01 |
0.90 |
0.35 |
|
HPCL |
0.18 |
0.00 |
0.31 |
0.58 |
0.05 |
0.00 |
0.04 |
0.84 |
|
IOC |
0.24 |
0.01 |
0.92 |
0.34 |
0.04 |
0.00 |
0.05 |
0.82 |
|
OIL |
0.21 |
0.01 |
0.63 |
0.43 |
0.25 |
0.02 |
1.73 |
0.19 |
|
ONGC |
0.10 |
0.00 |
0.14 |
0.70 |
0.04 |
0.00 |
0.05 |
0.83 |
|
Gail |
-0.04 |
0.00 |
0.02 |
0.89 |
-0.02 |
0.00 |
0.02 |
0.90 |
|
IGL |
-0.35 |
0.03 |
2.81 |
0.10 |
-0.04 |
0.00 |
0.06 |
0.80 |
|
Castrol |
0.11 |
0.00 |
0.30 |
0.59 |
0.07 |
0.00 |
0.27 |
0.61 |
|
PetroNet |
0.32 |
0.04 |
2.92 |
0.09 |
0.35 |
0.08 |
7.24 |
0.01 |
|
Reliance |
-0.18 |
0.01 |
0.80 |
0.37 |
-0.26 |
0.04 |
3.09 |
0.08 |
Then we proceed for VAR model analysis. The
Vector Autoregressive (VAR) model is usually employed for the purpose of
forecasting systems of interconnected time series and studying the dynamic
effects on the system of series arising from arbitrary disturbances. The VAR
approach considers each variable to be endogenous in the model as a function of
all endogenous variables lagged values, as a result of which the need for
structural modeling is bypassed. The auto regressive term happens because of
the appearance of the dependent variables lagged values on the right side of
the model and because of the fact that a vector of two or more variables is
included in the model, the vector term takes place (Fayyad & Dally, 2011).
One of the main strengths of the VAR model is that it enables us to compute the
dynamic relationships between the investigated variables.
To further investigate the causal
relationship among market indexes and stocks we estimate a VAR model. As we are
interested in short-term causal relationships we use lag equal to 1 in our VAR
model. Table 11 displays the results of an unrestricted VAR model. Result of LM
test shows evidence of no autocorrelation problem in the model. Results
indicate that stocks have a negative and statistically significant influence of
S & P BSE SENSEX and S & P Oil & Gas indexes at 10% significance
level. This result indicates that following month of increase or decrease in
stock returns the markets decrease or increase. However, we learn that the
coefficients are very weak. To confirm these results we run Granger causality
tests and find that no causality happens between study variables. As there is
no causality in any direction between the variables, the estimation results
indicate no predictive power of market indicators of stock prices. Pair-wise
granger causality test results are present in Table 12.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Figure 1. Line estimations of stock market indexes of oil
and gas stocks
Table 11. VAR Results
between stock market indexes and oil and gas stocks
|
|
SENSEX |
OIL_GAS |
BPCL |
|
|
SENSEX |
OIL_GAS |
HPCL |
|
SENSEX(-1) |
0.07 |
0.09 |
0.40 |
SENSEX(-1) |
0.07 |
0.08 |
0.26 |
|
|
Sig. |
-0.16 |
-0.22 |
-0.41 |
Sig. |
-0.16 |
-0.22 |
-0.46 |
|
|
OIL_GAS(-1) |
-0.21 |
0.04 |
-0.16 |
OIL_GAS(-1) |
-0.18 |
0.03 |
0.07 |
|
|
Sig. |
-0.13 |
-0.18 |
-0.34 |
Sig. |
-0.13 |
-0.18 |
-0.37 |
|
|
BPCL(-1) |
0.02 |
-0.14 |
-0.20 |
HPCL(-1) |
-0.01 |
-0.12 |
-0.13 |
|
|
Sig. |
-0.06* |
-0.08* |
-0.15 |
Sig. |
-0.05* |
-0.07* |
-0.14 |
|
|
SENSEX |
OIL_GAS |
IOC |
SENSEX |
OIL_GAS |
OIL |
|||
|
SENSEX(-1) |
0.08 |
0.04 |
0.32 |
SENSEX(-1) |
0.07 |
0.08 |
-0.07 |
|
|
Sig. |
-0.16 |
-0.23 |
-0.37 |
Sig. |
-0.16 |
-0.22 |
-0.38 |
|
|
OIL_GAS(-1) |
-0.20 |
0.00 |
0.01 |
OIL_GAS(-1) |
-0.17 |
-0.17 |
0.36 |
|
|
Sig. |
-0.14 |
-0.20 |
-0.32 |
Sig. |
-0.12 |
-0.17 |
-0.29 |
|
|
IOC(-1) |
0.01 |
-0.09 |
-0.14 |
OIL(-1) |
-0.03 |
0.07 |
-0.10 |
|
|
Sig. |
-0.07* |
-0.09* |
-0.15 |
Sig. |
-0.05* |
-0.07* |
-0.12 |
|
|
SENSEX |
OIL_GAS |
ONGC |
SENSEX |
OIL_GAS |
Gail |
|||
|
SENSEX(-1) |
0.07 |
0.08 |
0.13 |
SENSEX(-1) |
0.07 |
0.09 |
-0.02 |
|
|
Sig. |
-0.16 |
-0.23 |
-0.38 |
Sig. |
-0.16 |
-0.22 |
-0.37 |
|
|
OIL_GAS(-1) |
-0.18 |
-0.15 |
0.02 |
OIL_GAS(-1) |
-0.25 |
-0.12 |
0.17 |
|
|
Sig. |
-0.13 |
-0.19 |
-0.31 |
Sig. |
-0.12 |
-0.18 |
-0.30 |
|
|
ONGC(-1) |
-0.01 |
0.03 |
-0.04 |
GAIL(-1) |
0.06 |
0.00 |
-0.19 |
|
|
Sig. |
-0.06* |
-0.09* |
-0.15 |
Sig. |
-0.06* |
-0.08* |
-0.14 |
|
|
SENSEX |
OIL_GAS |
IGL |
SENSEX |
OIL_GAS |
Castrol |
|||
|
SENSEX(-1) |
0.06 |
0.07 |
-0.54 |
SENSEX(-1) |
0.08 |
0.12 |
0.13 |
|
|
Sig. |
-0.16 |
-0.23 |
-0.30 |
Sig. |
-0.16 |
-0.23 |
-0.28 |
|
|
OIL_GAS(-1) |
-0.20 |
-0.13 |
0.36 |
OIL_GAS(-1) |
-0.18 |
-0.11 |
0.05 |
|
|
Sig. |
-0.12 |
-0.17 |
-0.22 |
Sig. |
-0.11 |
-0.16 |
-0.20 |
|
|
IGL(-1) |
0.03 |
0.03 |
-0.18 |
CASTROL(-1) |
-0.03 |
-0.07 |
-0.14 |
|
|
Sig. |
-0.07* |
-0.09* |
-0.12 |
Sig. |
-0.07* |
-0.10* |
-0.12 |
|
|
SENSEX |
OIL_GAS |
Petronet |
SENSEX |
OIL_GAS |
Reliance |
|||
|
SENSEX(-1) |
0.06 |
0.08 |
-0.06 |
SENSEX(-1) |
0.10 |
0.06 |
0.18 |
|
|
Sig. |
-0.16 |
-0.22 |
-0.26 |
Sig. |
-0.16 |
-0.23 |
-0.29 |
|
|
OIL_GAS(-1) |
-0.22 |
-0.19 |
0.40 |
OIL_GAS(-1) |
-0.14 |
-0.18 |
-0.26 |
|
|
Sig. |
-0.12 |
-0.17 |
-0.20 |
Sig. |
-0.13 |
-0.19 |
-0.24 |
|
|
PETRO(-1) |
0.07 |
0.14 |
-0.04 |
RIL(-1) |
-0.07 |
0.08 |
-0.10 |
|
|
Sig. |
-0.07* |
-0.10* |
-0.12 |
Sig. |
-0.09* |
-0.12 |
-0.16 |
Table 12. Pair-wise
Granger causality test results between stock market indexes and oil and gas
stocks.
|
Panel
A: Pairwise Granger Causality Tests (SENSEX vs. Stock returns) |
F-Statistic |
Prob.
|
||
|
�BPCL does not Granger Cause SENSEX |
0.30 |
0.58 |
||
|
�SENSEX does not Granger Cause BPCL |
0.75 |
0.39 |
||
|
�HPCL does not Granger Cause SENSEX |
0.76 |
0.38 |
||
|
�SENSEX does not Granger Cause HPCL |
0.80 |
0.37 |
||
|
�IOC does not Granger Cause SENSEX |
0.68 |
0.41 |
||
|
�SENSEX does not Granger Cause IOC |
1.60 |
0.21 |
||
|
�OIL does not Granger Cause SENSEX |
1.02 |
0.32 |
||
|
�SENSEX does not Granger Cause OIL |
0.78 |
0.38 |
||
|
�GAIL does not Granger Cause SENSEX |
0.07 |
0.79 |
||
|
�SENSEX does not Granger Cause GAIL |
0.15 |
0.70 |
||
|
�ONGC does not Granger Cause SENSEX |
0.80 |
0.37 |
||
|
�SENSEX does not Granger Cause ONGC |
0.23 |
0.63 |
||
|
�IGL does not Granger Cause SENSEX |
0.01 |
0.93 |
||
|
�SENSEX does not Granger Cause IGL |
0.98 |
0.33 |
||
|
�CASTROL does not Granger Cause SENSEX |
0.29 |
0.59 |
||
|
�SENSEX does not Granger Cause CASTROL |
0.77 |
0.38 |
||
|
�PETRO does not Granger Cause SENSEX |
0.14 |
0.71 |
||
|
�SENSEX does not Granger Cause PETRO |
2.25 |
0.14 |
||
|
�RIL does not Granger Cause SENSEX |
2.28 |
0.14 |
||
|
�SENSEX does not Granger Cause RIL |
0.00 |
0.94 |
||
|
Panel
B: Pairwise Granger Causality Tests (Oil & Gas Index Vs. Stock Returns) |
F-Statistic |
Prob.
|
||
|
�BPCL does not Granger Cause OIL_GAS |
3.14 |
0.08 |
||
|
�OIL_GAS does not Granger Cause BPCL |
0.02 |
0.88 |
||
|
�HPCL does not Granger Cause OIL_GAS |
2.90 |
0.09 |
||
|
�OIL_GAS does not Granger Cause HPCL |
0.52 |
0.47 |
||
|
�IOC does not Granger Cause OIL_GAS |
1.05 |
0.31 |
||
|
�OIL_GAS does not Granger Cause IOC |
0.81 |
0.37 |
||
|
�OIL does not Granger Cause OIL_GAS |
0.91 |
0.34 |
||
|
�OIL_GAS does not Granger Cause OIL |
2.31 |
0.13 |
||
|
�GAIL does not Granger Cause OIL_GAS |
0.00 |
0.99 |
||
|
�OIL_GAS does not Granger Cause GAIL |
0.45 |
0.50 |
||
|
�ONGC does not Granger Cause OIL_GAS |
0.14 |
0.71 |
||
|
�OIL_GAS does not Granger Cause ONGC |
0.11 |
0.74 |
||
|
�IGL does not Granger Cause OIL_GAS |
0.19 |
0.66 |
||
|
�OIL_GAS does not Granger Cause IGL |
0.38 |
0.54 |
||
|
�CASTROL does not Granger Cause OIL_GAS |
0.45 |
0.51 |
||
|
�OIL_GAS does not Granger Cause CASTROL |
0.62 |
0.43 |
||
|
�PETRO does not Granger Cause OIL_GAS |
1.93 |
0.17 |
||
|
�OIL_GAS does not Granger Cause PETRO |
6.52 |
0.01 |
||
|
�RIL does not Granger Cause OIL_GAS |
0.55 |
0.46 |
||
|
�OIL_GAS does not Granger Cause RIL |
0.79 |
0.38 |
||
5. Discussion
In this paper we observe
that stock market movements cause stock returns in contemporaneous periods. Our
results indicate that there is positive correlation between stock market movements
and oil and gas stock returns, when stock markets increase stock prices of oil
and stock prices also increase and vice-versa. However, we fail to find any predictive
ability of stock market indexes in short term of oil and gas stock returns. Our
predictive regression analysis reveals insignificant coefficient of
determination and not able explain the variability of market indexes of stock
returns. Our VAR models and Granger cause models also do not provide any
significant evidence for predictability of stock market indexes of stock
returns. On the other side, we do not find any correlation or causation between
short-term economic indicators and stock market indexes. Our results are in
contrast to results reported by Cutler et
al., (1989); Chen et al., (1986) who find
significant positive correlation between industrial production and stock market
returns. In the Indian context our results are similar to those of Singh (2014) who
reports no causation between IIP and stock market indexes. From our results we
interpret that Indian stock markets and returns of oil and gas stocks are
independent from industrial production. Even though the Indian manufacturing,
mining, and electricity sectors are closely knitted with oil and gas industry,
their impact on oil and gas stock returns is minimal. This is because financial
performance and profitability of oil and gas companies are regulated by
Government of India�s policy decisions. Seventy percent of the oil and gas
stocks studied in this paper are owned and controlled by government and are
still under government policy regulations. In addition, as these companies
import crude oil, global level factors like crude oil prices, exchange rate
fluctuations, demand and supply of crude oil may have significant impact. As a
coincident economic indicator industrial production rightly reflect Indian
economic situation but not able to influence the stock market indexes which are
leading economic indicators.
6. Conclusion
The aim of this paper is four fold: i) to
examine the influence of short-term economic indicators on stock prices ii) to
investigate the impact of market indexes on oil and gas stocks; iii) to predict
stock returns using economic and market indicators; iv) to study the relationship
among stock prices of Indian oil and gas companies over the period 2012-2019. We
consider four short-term economic indicators, two stock market indexes and 10
Indian oil and gas companies. We find no causal relation between short-term
economic indicators and stock market indexes. Similarly, we do not find any
causation between short-term economic indicators and oil and gas stocks. These
results indicate that stock markets and oil and gas stocks are independent from
industrial production. On the other side we find significant positive
correlation and causation between stock market indexes and stock returns.
However, in contrast to this we fail to find any and predictive power of stock
market indexes about stock returns in short run. Leading economic indicators
have contemporaneous relationship with stock returns, but are not able to
predict the stock returns in short run. We conclude that industrial production
will not help predict neither stock market movements nor stock returns. Our
empirical results suggest that stock market indexes help to understand
contemporaneous stock returns but not future returns. In this paper we measure
the impact of domestic economic indicators and ignored the global factors.
Future research can consider the influence of crude oil prices, and exchange
rate fluctuations on oil and gas stocks returns. Furthermore, future research
can link monthly economic indicator values with quarterly financial results of
oil and gas companies.�
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