STUDY OF THE IMPACT OF MACRO ECONOMIC VARIABLES AND VOLATILITY OF BANKING SECTORS IN NSE - infokara research

 
INFOKARA RESEARCH                                                                                   ISSN NO: 1021-9056

                STUDY OF THE IMPACT OF MACRO
             ECONOMIC VARIABLES AND VOLATILITY OF
                   BANKING SECTORS IN NSE
                                         Gayathry1, Dr. J.samuelCaeser Pickens 2
                   1
                       Research Scholar, Dr.SNS Rajalakshmi College of Arts and Science, Autonomous,
                                                     Coimbatore – 49.
             2
                 Asst. Professor and Head, Department of Commerce with IT, Dr.SNS Rajalakshmi College
                                    of Arts and Science, Autonomous, Coimbatore - 49.
             ABSTRACT
                        In this present scenario of the world financial market there seems to be stock
             exchange on almost every country expressing the financial health of the respective economy.
             This study investigates the volatility by using GARCH model along with the impact on
             scheduled macro-economic variables with selected banks sectors listed in NSE.Considered
             for the study arethe four macro-economic variables are Interest Rate(IR),Foreign exchange
             rate(USD-INR),Crude Oil Price(CP),Gold Price(GP).The result exhibit the significant impact
             of macroeconomics variables on NSE. Main observational of this study are to find volatility
             and effects of macro-economic variables in indices. Hypothesis testing on price volatility and
             impact of macro-economic variables. Empirical study period was from April 2016 to March
             2018. Hence it is recommended for the investors to continuously have a close watch on the
             frequent changes, based on the return on investing. So effects can be attained by making
             correct investment at gain profit.

             Keywords: Stock Exchange, Banking Sectors, , Macroeconomic Variables. Volatility.

             INTRODUCTION

                        The stock market is one of the most important sources for companies to raise money.
             Indian stock market has developed in term of number of stock exchange and other
             intermediaries. The number of listed stocks, trading volume, market capitalisation, turnover
             of the stock exchange, investor involvement and price indices. Macro economy is the study of
             the economy as a whole, and the variables that control the macro economy. The study of
             government policy meant to control and stabilizes the economy over time, that is to reduce
             fluctuations in the economy. Macro Economics treats the components of the economy as one
             unit, as one aggregate, that is looks for relationships between the various components. These
             variables are indicators or main signposts signalling the current trends in the economy it

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INFOKARA RESEARCH                                                                                  ISSN NO: 1021-9056

             understands the major variables that determine the current behaviour of the macro economy
             so government must understand the forces of economic growth, why and when recession or
             inflation occur, and anticipate these trends as well as what determine the policy which will
             be most suitable for curing whatever its economy. After extensive survey with macro-
             economic variables we arrived at a conclusion that the macro variables mentioned play major
             role in the economy. In this study the researcher has particularly focused on banking sectors
             listed in NSE because total market fluctuations are determined by performance of NSE.
             Markets increase the volatility around announcement period with an anticipation that affect
             the market and diminish the investor expectations.

             REVIEW OF LITERATURE

                    Raman Preet Singh(2011), has done a Study of The Impact of Macro Economic
             Variable & Their Role as an Indicators for the S&P CNX Nifty, the objective of this paper is
             to study the role of these seven macroeconomic variable in relation to Nifty, how these
             variables can be used to predict the movement of the Nifty Index and to which category of
             indicator these macro-economic variables correlate The Granger causality test with the data
             ranging from 2002-2011 has been used to prove that there is no cause and effect relationship.
             It has been found that with the help of this method we can predict the future.

                    N.Sangeetha(2013), to analyse the Impact of Macro economic Factors on Share Price
             Movements of Trading Sectors, the objective of the study is to determine the risk return
             relationship of the stock, study the relationship between the macro economic factors and the
             share price movements and also to analyse the impact of the factors on the performance of the
             companies’ stocks. The analysis is done with 30 companies taken from 6 sectors (5
             companies from each sector). The data are based on secondary data is that used in this study
             and was collected from national stock exchange website and various journals, reports,
             publications and historical documents. It has been founded and interpreted that the
             performance of Banking sector will be good and the Telecommunication sector will be the
             low performing sector. It has been suggested that the investor should have knowledge of
             exchanging the stocks during the change in macroeconomic factor. Banking sector is high
             performing sector in which investors can invest in future.

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INFOKARA RESEARCH                                                                                  ISSN NO: 1021-9056

             STATEMENT OF THE PROBLEM

                    Indian stock market overcame many changes has been built based on the policies that
             were made by the government and other upper hands. The decreasing share price have been a
             equivalent problems are overlooked by each investors. Similarly, sector is designate as an
             important factor which makes an changes in the economy and it cross over the gap between
             stock market and investors at tonnes. Volatility specify that the degree of variations in the
             share price which is purposive during the investment process. High volatility results to more
             risk. Although there is change is macroeconomic variable that will affect various sectors. In
             this study it has been analysed to know about the extent macroeconomic variable that has an
             effect on share market and the reason for the unstable fluctuations that appear in various
             banking sectors.

             OBJECTIVE OF THE STUDY

                   To investigate the stock price volatility of selected banks listed in NSE.
                   To find the effect of macroeconomic variables on selected banks listed in NSE.

             HYPOTHESES OF THE STUDY

             H0: There is no significant change on stock price volatility on selected banks in NSE

             H0: There is no significant effect on macroeconomic variable on selected banks in NSE

             RESEARCH METHODOLOGY OF THE STUDY

                    This study uses analytical research because we utilise the facts and information
             already available, and analyse these to make a critical evaluation of the material.

             Data Source

                    The data used in for the study is purely secondary in nature with the monthly closing
             price of the banking indices in NSE and monthly data of macroeconomic variables.

             Period of Study

                    The monthly closing prices have been collected from the official website of National
             Stock Exchange for a period of five year from April 1st 2016 to 30th March 2018.
             Macroeconomic monthly data collected fromworld bank from yahoo finance.

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INFOKARA RESEARCH                                                                                     ISSN NO: 1021-9056

             Tools used

                      Descriptive Analysis
                      Augmented Dickey Fuller Test
                      GARCH Model
                      Multiple Regression

             Methods of Sampling

             Table1: Description of selected Macroeconomic variables

             S.NO                       VARIBALES                        FREQUENCY OF ANNOUNCEMENT
               1        Inflation                                                     Monthly
               2        Gold Price                                                    Monthly
               3        Exchange Rate                                                 Monthly
               4        Crude oil                                                     Monthly

             Table 2: Description of selected banking sectors listed in NSE

             S.No              Bank Name            Last        %       52 week      52 week       Market
                                                    Price     Change      high         Low       Capitalization
                                                                                                   (Rs.Cr)
               1       State Bank of India         271.10      1.01      373.70      247.65       241,946.22
               2       Bank of Baroda               93.45      3.20      156.25       89.75       35,947.67
               3       Punjab National Bank         63.55      3.42      99.90        58.65       29,258.72
               4       HDFC                       2,163.25     0.48     2,502.90    1884.40       591,490.98
               5       Kotak Mahindra Bank         1472.50     0.32     1,555.45    1,002.30      281,185.14
               6       ICICI                       395.75      0.85      443.85      294.80       255,460.39

             SIGNIFICANCE OF THE STUDY

                       The study highlights the measure of stock price volatility with dynamic influence that
             occurs on and around the economic market. It is also important to realize the macroeconomic
             variable which influence stock price, effect is also been analysed to know its impact among
             the share price of the selected banks with macroeconomic variable. This study will also help
             the investor to make a better investment process and render the profit as well.

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INFOKARA RESEARCH                                                                                       ISSN NO: 1021-9056

             Summary of
             statistics           SBI         BOB          PNB         HDFC         KMB          ICICI
             Mean              266.5833     159.2813     137.2167     1524.869     892.6750     267.3167
             Median            264.1750     161.1500     139.4250     1494.525     887.0750     252.5200
             Maximum           320.3500     187.5500     197.1500     2006.350     1108.950     352.9500
             Minimum           189.0000     137.6000      79.0000     1132.350     717.1500     215.4100
             Standard          34.46204     12.69357     29.70983     294.4853     128.7070     37.22972
             Deviation
             Skewness          -0.370544    -0.002725    -0.139924    0.121082     0.122524     0.424696
             Kurtosis          2.655096     2.645621     2.516236     1.400913     1.518589     2.209721
             JarqueBera        0.668170     0.125614     0.312342     2.615722     2.254627     1.346957
             Probability       0.715993     0.939125     0.855413     0.270398     0.323902     0.509932
             Number of             24           24           24          24           24           24
             Observation
             Table 3 : Descriptive Analysis for selected banking sectors listed under NSE

             (Source: Computed from yahoo finance)

                     Table 3. Represents the descriptive statistics on share price of selected banking sector
             for the period of 1st April 2016 to 31sr march 2018 on monthly data. It reveals that the mean
             value of share price is 1524.869 on HDFC bank and its standard deviation is 294.4853 with
             high value it also implies that there is high degree of variability due to less deviation. It
             means the mean value is distribution normally. The distribution of share price of SBI, BOB,
             PNB is negatively skewned (-0.370),(0.002)and (-0.139) that indicates the distribution
             towards the left tailed. Kurtosis is distributed is platykurtic. The p-value of JarqueBera (J-B)
             test statistics of share price (0.71),(0.93),(0.855),(0.270),(0.323), and (0.509) is not
             significant at 5 percent with probability not normally distributed share price. The H0 null
             hypothesis is accepted.

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INFOKARA RESEARCH                                                                                           ISSN NO: 1021-9056

             Table 4 : The Augmented Dickey Fuller Test of Selected Banking Sectors

             Banking       Closing price of Banking Sectors
             Sector        Level                      1st difference                 2nd difference
                           t-statistic   probability t-statistic      probability t-statistic      probability
             SBI           -2.843        0.067        -4.484          0.002          -6.434        0.000
             BOB           -2.563        0.114        -5.364          0.003          -5.713        0.000
             PNB           -2.994        0.054        -2.646          0.101          -7.961        0.000
             HDFC          -0.819        0.794        -4.103          0.004          -8.142        0.000
             KMB           -1.026        0.725        -5.246          0.004          -8.735        0.000
             ICICI         -1.691        0.422        -4.919          0.008          -6.437        0.000
             (Source: Computed from NSE)

                      Table 4 reveals the unit toot test applied to determine the order of integration among
             the time series data of select banking sector in NSE. The Augmented Dickey fuller test is
             used at level , first difference and in second difference under the assumption intercept.
             According to the results banking sectors like SBI, BOB,PNB, HDFC,KMB and ICICI are not
             stationary on level and first difference but it has to become stationary when it was on second
             difference. The degree of integration of series I(1)

             Table 5: The Augmented Dickey Fuller Test of Selected Macroeconomic Variables

             Banking                                 Price of Macroeconomic Variables
             Sector                      Level                      1st difference              2nd difference
                               t-statistic   Probability   t-statistic     Probability    t-statistic      probability
             INFLATION         1.546         0.998         -4.482          0.024          -7.196           0.000
             GOLD PRICE        -1.581        0.347         -5.545          0.002          -9.216           0.000
             CPI               -1.306        0.608         -4.049          0.005          -5.385           0.000
             EXCHANGE          -1.896        0.327         -2.656          0.097          -4.926           0.000
             RATE
             CRUDE OIL         -4.412        0.558         -4.520          0.029          -4.153           0.000
             (source: computed from world Bank)

                      Table 5 reveals the unit toot test applied to determine the order of integration among
             the time series data of select banking sector in NSE. The Augmented Dickey fuller test is

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INFOKARA RESEARCH                                                                                      ISSN NO: 1021-9056

             used at level , first difference and in second difference under the assumption intercept.
             According to the results banking sectors like INFLATION, GOLD PRICE, EXCHANGE
             PRICE CPI and crude oil are not stationary on level and first difference but it has to become
             stationary when it was on second difference. The degree of integration of series I(1)

             TGARCH Model For SBI Bank:

             Table 6 : Results of TGARCH estimates of market volatility over the period form 2016-
             2018 for State Bank of India

             Variable                Coefficient St. Error            z-Statistic        Probability
             Constant                  5.616659        0.021226           264.6122            0.0000
                                                  Variable Equation
             Constant                  0.003077        0.005065           0.607494            0.5435
             RESID(-1)^2               0.285184        0.657733           0.433587            0.6646
             GARCH(-1)                 0.316115        0.945494           0.334338            0.7381
             R-squared                -0.089173           Akaike info criterion             -1.332318
             Adjusted R-squared       -0.089173            Schwarz criterion                -1.135976
             Durbin-Waston stat        0.315732          Hannan-Quinn Criter                -1.280228
             Source: Compiled and calculated from NSE

                    Results of TGARCH model stated in table 6 speak about the asymmetric response of
             volatility to positive and negative stocks. The volatility modelling captured by Threshold
             GARCH model had good news with a impact of ARCH, Here AIC and SIC has a value of -
             1.33 and -1.135 which could be a better model to exhibit the volatility. Where p value is less
             than the significant value and implies a fluctuation along the volatility. The TGARCH model
             best captures the volatility in the progress with its impact. Thereby Durbin Watson stat value
             is 0.31 which implies that the model is free from autocorrelation

             Table 7 : Results of TGARCH estimates of market volatility over the period form 2016-
             2018 for Bank of Baroda

             Variable                Coefficient St. Error            z-Statistic        Probability
             Constant                  5.058485        0.016055           315.0795            0.0000
                                                  Variable Equation
             Constant                  0.005538        0.005748           0.963376            0.3354
             RESID(-1)^2               0.560131        0.638847           0.876784            0.3806

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INFOKARA RESEARCH                                                                                      ISSN NO: 1021-9056

             GARCH(-1)                -0.454454        0.718963          -0.632096           0.5273
             R-squared                -0.013483           Akaike info criterion             -2.036913
             Adjusted R-squared       -0.013483            Schwarz criterion                -1.840571
             Durbin-Waston stat       1.023986           Hannan-Quinn Criter                -1.984824
                    Results of TGARCH model stated in table 7 speak about the asymmetric response of
             volatility to positive and negative stocks. The volatility modelling captured by Threshold
             GARCH model had good news with a impact of ARCH, Here AIC and SIC has a minimum
             value of -2.03 and -1.84 which could be a better model to exhibit the volatility. Where p
             value is less than the significant value and implies a fluctuation along the volatility.Thereby
             Durbin Watson stat value is 1.02 which implies that the model is free from autocorrelation

             Table 8 : Results of TGARCH estimates of market volatility over the period form 2016-
             2018 for Punjab National Bank

             Variable                Coefficient St. Error            z-Statistic        Probability
             Constant                 4.961422         0.013903          356.8498            0.0000
                                                  Variable Equation
             Constant                 0.015929         0.009289          1.714761            0.0864
             RESID(-1)^2              1.082528         0.735751          1.471325            0.1412
             GARCH(-1)                -0.308716        0.165226          -1.868442           0.0617
             R-squared                -0.080641           Akaike info criterion             -0.442666
             Adjusted R-squared       -0.080641            Schwarz criterion                -0.246323
             Durbin-Waston stat       0.571232           Hannan-Quinn Criter                -0.390576

                    Results of TGARCH model stated in table 8 speak about the asymmetric response of
             volatility to positive and negative stocks. The volatility modelling captured by Threshold
             GARCH model had good news with a impact of ARCH, Here AIC and SIC has a minimum
             value of -0.44 and -0.246 which could be a better model to exhibit the volatility. Where p
             value is less than the significant value and implies a fluctuation along the volatility.Thereby
             Durbin Watson stat value is 0.57 which implies that the model is free from autocorrelation

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INFOKARA RESEARCH                                                                                      ISSN NO: 1021-9056

             Table 9 : Results of TGARCH estimates of market volatility over the period form 2016-
             2018 for HDFC

             Variable                Coefficient St. Error            z-Statistic        Probability
             Constant                 7.273272         0.036000          202.0326            0.0000
                                                  Variable Equation
             Constant                 0.001590         0.002073          0.767211            0.4430
             RESID(-1)^2              2.005539         2.168999          0.924638            0.3552
             GARCH(-1)                -0.507343        1.016350          -0.499182           0.6177
             R-squared                -0.040599           Akaike info criterion             -0.645779
             Adjusted R-squared       -0.040599            Schwarz criterion                -0.449437
             Durbin-Waston stat       0.048809           Hannan-Quinn Criter                -0.593690

                    Results of TGARCH model stated in table 9 speak about the asymmetric response of
             volatility to positive and negative stocks. The volatility modelling captured by Threshold
             GARCH model had good news with a impact of ARCH, Here AIC and SIC has a minimum
             value of -0.645 and -0.449 which could be a better model to exhibit the volatility. Where p
             value is less than the significant value and implies a fluctuation along the volatility.Thereby
             Durbin Watson stat value is 0.04 which implies that the model is free from autocorrelation

             Table 10 : Results of TGARCH estimates of market volatility over the period form
             2016-2018 for KMB

             Variable                Coefficient St. Error            z-Statistic        Probability
             Constant                 6.679849         0.023021          290.1584            0.0000
                                                  Variable Equation
             Constant                 0.002607         0.002375          1.097745            0.2723
             RESID(-1)^2              1.337693         1.466475          0.912183            0.3617
             GARCH(-1)                -0.241721        0.388962          -0.621450           0.5343
             R-squared                -0.544199           Akaike info criterion             -1.201449
             Adjusted R-squared       -0.544199            Schwarz criterion                -1.005106
             Durbin-Waston stat       0.076848           Hannan-Quinn Criter                -1.149359

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INFOKARA RESEARCH                                                                                       ISSN NO: 1021-9056

                    Results of TGARCH model stated in table10 speak about the asymmetric response of
             volatility to positive and negative stocks. The volatility modelling captured by Threshold
             GARCH model had good news with a impact of ARCH, Here AIC and SIC has a minimum
             value of -1.201 and -1.005 which could be a better model to exhibit the volatility. Where p
             value is less than the significant value and implies a fluctuation along the volatility.Thereby
             Durbin Watson stat value is 0.07 which implies that the model is free from autocorrelation

             Table 11 : Results of TGARCH estimates of market volatility over the period form
             2016-2018 for Punjab National Bank

             Variable                 Coefficient St. Error           z-Statistic        Probability
             Constant                  5.464606        0.016004          341.4630              0.0000
                                                  Variable Equation
             Constant                  0.002305        0.003763          0.612460              0.5402
             RESID(-1)^2               1.308511        0.764658          1.711237              0.0870
             GARCH(-1)                -0.271121        0.426202          -0.636132             0.5247
             R-squared                -0.728935           Akaike info criterion             -1.165582
             Adjusted R-squared       -0.728935            Schwarz criterion                -0.969240
             Durbin-Waston stat        0.145022          Hannan-Quinn Criter                -1.113492

                    Results of TGARCH model stated in table11 speak about the asymmetric response of
             volatility to positive and negative stocks. The volatility modelling captured by Threshold
             GARCH model had good news with a impact of ARCH, Here AIC and SIC has a minimum
             value of -1.165 and -0.969 which could be a better model to exhibit the volatility. Where p
             value is less than the significant value and implies a fluctuation along the volatility.Thereby
             Durbin Watson stat value is 0.14 which implies that the model is free from autocorrelation

             Table 12 : Multiple Regression Analysis of Banking Sector

             Banking     R        R           Adj     R Std.Error DurninWaston F                    Sig
             Sectors              Square      Square
             SBI         .656a    .430        .404       26.573       .800              16.590      .001b
             BOB         .434a    .189        -.037      12.907       1.502             .836        .541b
             PNB         .544a    .296        .101       28.155       1.103             10516       .234b
             HDFC        .992a    .985        .981       41.098       1.561             232.491     .000b

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             KMB         .990a      .980        .975        20.372        2.445          180.063    .000b
             ICICI       .934a      .872        .836        15.110        1.478          24.443     .000b

                     Findings from the regression analysis results for the selected banks and
             macroeconomic variables as depicted in Table12. Indicates that, the R Square of SBI, BOB,
             PNB, HDFC, KMB and ICICI is .430, .189, .296, .985, .980, and .875. The R Square which
             is also measure of the overall fitness of the model indicated that the model is capable of
             explaining about 43, 18, 29, 98, 98 and 87 percent of the variability the share price of banks.
             This results is complimented by the adjusted R-Square of about 40%. -30%, 10%, 98%, 97%
             and 83% which is essence is the proportion of total variance that is explained by the model.

                     The F statistics which is a proof of the validity of the estimated as reflected in
             Table.12 indicated that, the hypothesized relationship between banking sector and
             macroeconomic variables are validated. F is about 16.59, .83, 105.16, 180.06, .24.443 and a
             p-value that is equal to 0.05 (p-value =.05), that invariably suggests clearly that
             simultaneously the explanatory variables are significantly associated with the dependent
             variable. Share price of banking sectors are strongly determined. Further Durbin-Watson
             statistics value is 2.44 which means that the error term is independent and is free of
             autocorrelation.

             SUGGESTION
                    The strength of an investors is the should consider all relevant sources of information
                     when making an investment decision it is strengthen of investors.
                    When the investors are at risk they would not be happy to invest in a highly
                     fluctuating stock. Whenever those with a thirst ofriskiness would happily invest in a
                     highly volatile market.
                    From the study the selected banking sectors are different in their operations even the
                     share volatility is similar for all the selected banks.
                    The banks      are suggested      to improve the performance and prevailing market
                     condition to reduce market volatility of share price that are becoming high volatility
                     heads to high risk.
                    The Macroeconomic variables along with share price fluctuations indicatestrongly, to
                     the investor to attain clarity while investing and also create a stable economy by
                     increasing the flow of cash among the banking sectors.

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             CONCLUSION

                    The study performed necessary analyses to answer the research questions of whether
             some of the identified macroeconomic factors can influence the banking share price and
             volatility of the share price decides the degree of risk and return one investor can earn form
             stock market, so information regarding to the share price volatility helps the investors to
             make the best investment decisions. The study is such an attempt, to examine the volatility
             and effect of macroeconomic variables of selected banking sectors listed in NSE for
             providing valuable information to investors to succeed.

             REFERENCE
                   Indian Journal of Finance
                   International journal of economics and finance
                   International journal of business and management
                   International journal of Research and applied natural and social science.
             WEBSITES
                   www.nse.com
                   www.yahoofinance.com
                   www.worlddatabank.com
                   Google Scholar

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