Impact of Demonetisation on various sectors of Indian stock Market

A Study on the impact of
demonetization on selected
sectors of Indian Stock Market
Submitted by:-
Deepanshi
ADGITM, IP University

• Demonetization is the process of removing
certain form of currency from circulation.
• Notes of a particular denomination cease to be
legal tender. In other words, the notes lose
their value as a currency.
• Demonetization has been done three times in
the history of India, first in 1946 ,1978 and
then in 2016.

The demonetisation move was taken-
• to tackle black money and corruption;
• to curb fake currency and terror funding;
• to make India a cash-free economy.

Objectives of the Study
• To study the stock price movements of various
sectors, pre and post demonetization, of Indian
Stock Market.
• To examine the impact of Various sectoral
indices under Nifty 50 to Demonetization in
India.
• To find the impact on selected sectors post
Demonetization using Granger Causality Test.

Chatterjee and Banerji (2016), discussed the general impact of
demonetization on Indian economy and specific impact on various
sectors. As per them the demonetization of 500 and 1000 notes will
have significant and immediate impact on Indian economy.
Demonetization resulted into increase in bank’s deposit level due to
more number of deposits with banks. Further financial savings are
expected to increase as a result of shift from unproductive physical
asset based savings to interest bearing financial assets. This in turn is
expected to increase the banks liquidity position, which can be
leveraged by them for lending purposes. As the demonetization is
expected to result in low preference for informal funding sources, the
real estate sector is expected to have an adverse impact in terms of
demand. Luxury property rates are expected to fall as result of fewer
purchasers with substantial liquidity. The demonetization measures are
also expected to affect the cash transactions in Automobile Industry,
predominantly in auto ancillary and two wheelers industry.

Bathija and Eluri (2017) analysed the impact of
demonetization on stock market. The main reason
behind this move was to curb black money and stop
terrorism funding. They have found out that the share
prices fell around 6 % on 9th November as a combined
effect of demonetization and US Presidency election. In
November, foreign investors sold around $3 billion
value stocks. According to the authors, “this impact is
for short term, BSE Sensex would rise to 26560 by the
end of the month of December”. They have also
predicted that it would reach 28500 by mid of 2017 and
29600 by the end of 2017 and NSE nifty would rise to
8775 points.

Nathan (2017) explained that demonetization and US
presidential election affected Indian stock market. The
day after these changes the BSE opened with a loss of
1300 points and recovered slowly after a week. This
move created opportunity to buy value stocks which
give higher returns in future. It means that the impact is
temporary or for short period and stock market would
recover slowly.

Kumar (2017) examined the effect of demonetization
on stocks in terms of EPS. The author has compared the
EPS of companies before and after demonetization and
estimated the EPS for next quarter i.e. January to March
to analyse the long term impact of demonetization and
has given an opinion that there is no negative impact of
demonetisation in the long term.

RESEARCH DESIGN
• Analytical Research Design
• My Research is primarily based on Secondary Sources.
• As the present study tries to investigate the relationship
between the sectoral indices and Nifty , it considers the
following model:
Y = a +β1 X1+β2X2+ β3X3+ β4X4+β5X5
SOURCES OF SECONDARY DATA
• Journals
• The data of closing prices of various indices under study
has been collected from the website of National Stock
Exchange.

TOOLS&TECHNIQUES OF DATA
• Descriptive Statistics
• Granger Causality Test
• Line Graph
TIME FRAME
• Period of Data- 30 days before (26th September 2016 to
7th November 2016) and 30 days after (9th November
2016 to 21st December 2016)
LIMITATIONS
• The sectors of one only of the indices i.e. NSE were
studied.
• Selected sectors were taken to study the impact.

DATE NIFTY(PRE) P BANK (PRE)BANK(PRE) FMCG(PRE) AUTO(PRE) IT(PRE)
26-Sep-16 8723.05 10931.55 19591.75 21977.1 10048.65 10330
27-Sep-16 8706.4 10894.9 19518.7 21913.15 9992.5 10397
28-Sep-16 8745.15 10954.5 19653.55 21917.1 10115.95 10360.45
29-Sep-16 8591.25 10696.95 19183.65 21661.2 9893.85 10257.8
30-Sep-16 8611.15 10742.05 19285.7 21542.05 10029.3 10292.3
03-Oct-16 8738.1 10912.35 19589.05 21729.45 10275.5 10309.85
04-Oct-16 8769.15 10939.3 19672.7 21709.85 10289.7 10394.3
05-Oct-16 8743.95 10832.05 19536.85 21736.8 10344 10333.35
06-Oct-16 8709.55 10762.3 19395.05 21659.5 10269.85 10242.45
07-Oct-16 8697.6 10758.05 19400.1 21638.65 10344.3 10184.25
10-Oct-16 8708.8 10755.55 19378.55 21672.55 10335.8 10270.95
13-Oct-16 8573.35 10542.85 18954.25 21454.9 10203 10243.75
14-Oct-16 8583.4 10582.95 19020.15 21423 10223.45 10228.65
17-Oct-16 8520.4 10657.95 19070.4 21415.5 10008.4 10158.75
18-Oct-16 8677.9 10904.65 19495.05 21749.1 10150.05 10377.15
19-Oct-16 8659.1 10854.2 19412.1 21449.65 10092.55 10411.1
20-Oct-16 8699.4 11002.9 19658.7 21424.55 10088.7 10372.95
21-Oct-16 8693.05 11028.4 19710.9 21546.8 10101.6 10477.1
24-Oct-16 8708.95 11082.35 19807.9 21569.95 10188.2 10327.65
25-Oct-16 8691.3 11134.45 19834.9 21387.55 10143.7 10261.4
26-Oct-16 8615.25 10917.1 19483.6 21338.4 10056.55 10157.35
27-Oct-16 8615.25 10939.7 19514.6 21450 9912.85 10052.85
28-Oct-16 8638 10947.95 19555.95 21547.95 10056.7 10048.4
30-Oct-16 8625.7 10937.85 19523.55 21481.95 10048.5 10082.7
01-Nov-16 8626.25 10874.8 19458.6 21370.4 10105.55 9990.6
02-Nov-16 8514 10788.1 19227.9 21195.9 9973.8 9871.25
03-Nov-16 8484.95 10773.9 19178.7 21261.75 9948.25 9809.75
04-Nov-16 8433.75 10721.2 19058.1 21658.95 9870.75 9924.05
07-Nov-16 8497.05 10853.1 19356 21916.5 9892.6 9970
08-Nov-16 8543.55 10949.45 19500.8 21902.85 10079.7 10008.4

DATE NIFTY(POST) P BANK(POST)BANK(POST) FMCG(POST AUTO(POST) IT(POST)
09-Nov-16 8432 10911.85 19518.25 21414.35 9819.8 9683.25
10-Nov-16 8525.75 11210.45 20200.25 21561.1 9758.15 9654.75
11-Nov-16 8296.3 10936.85 19738.8 20825.9 9308.3 9434.6
15-Nov-16 8108.45 10574.95 19289.75 20307.95 8828.6 9487
16-Nov-16 8111.6 10464.8 19108.1 20126.1 8918.65 9641
17-Nov-16 8079.95 10461.15 19087.85 20154 8915.6 9496.2
18-Nov-16 8074.1 10400.95 18959.05 19959.9 8983.85 9476.45
21-Nov-16 7929.1 10176.4 18446.4 19701.25 8681.6 9453.75
22-Nov-16 8002.3 10251.05 18548.65 19934.3 8852.9 9509.45
23-Nov-16 8033.3 10255.45 18540.9 19976.1 8891.15 9549.85
24-Nov-16 7965.5 10040.7 18256.1 19851.7 8754.5 9656.25
25-Nov-16 8114.3 10200.95 18507.3 20157.15 8780.4 10114.9
28-Nov-16 8126.9 10110.8 18301.45 20426.05 8826.65 10106.2
29-Nov-16 8142.15 10073.35 18223.75 20306.6 9017.95 10059.6
30-Nov-16 8224.5 10290.65 18627.8 20487.9 9113.8 10087.65
01-Dec-16 8192.9 10172.45 18428.45 20554.7 9041 10049.75
02-Dec-16 8086.8 10061.75 18247.65 20179.85 8888.7 9922.75
05-Dec-16 8128.75 10148.6 18408.9 20478.8 9046.25 9836
06-Dec-16 8143.15 10154.15 18420.9 20293.2 9014 9834.95
07-Dec-16 8102.05 10064.45 18234.15 20137.35 9053.25 9777.9
08-Dec-16 8246.85 10219.65 18515.45 20510.25 9299.05 9935.9
09-Dec-16 8261.75 10304.95 18695.8 20622.4 9252.3 9997.5
12-Dec-16 8170.8 10130.1 18392.95 20319.7 9092 9953.9
13-Dec-16 8221.8 10166.7 18466.05 20491.65 9196 10058.65
14-Dec-16 8182.45 10111.4 18341.5 20313.55 9146.05 10128.95
15-Dec-16 8153.6 10135.95 18401.15 20136.05 9118.25 10195.75
16-Dec-16 8139.45 10071.25 18312.8 19982.6 9153.5 10222.95
19-Dec-16 8104.35 10055.2 18257.05 19957.2 9098.05 10223.65
20-Dec-16 8082.4 9961.1 18069.4 20039.05 9023.05 10322.85
21-Dec-16 8061.3 9964.5 18084.5 19809.7 9013 10228.25

Change in
Index Level
Change in
Volatility
Indices
30 Days
Average
(Before)
30 Days
Average
(After)
% Change in
average
Absolute %
Change from
Nov 9 to Dec
21
30 Days
Volatility
(Before)
30 Days
Volatility
(After)
% Change
volatility
NIFTY 8647.76 8148.15 -5.78 -5.64 93.3 122.24 31.02
Auto 10107.65 9062.88 -10.34 -10.58 140.06 252.01 79.93
Bank 19447.63 18621.04 -4.25 -7.26 244.14 507.3 107.79
FMCG 21603.87 20300.55 -6.03 -9.55 239.98 416.17 73.41
IT 10226.18 9862.69 -3.55 2.2 172.41 273.53 58.64
Private
Banks
10861.38 10269.42 -5.45 -9 145.25 295.46 103.41

-5.64
-10.58
-7.26
-9.55
2.2
-9
-12
-10
-8
-6
-4
-2
0
2
4
NIFTY Auto Bank FMCG IT Private
Banks
Absolute % Change from Nov 9 to Dec 21
Absolute % Change from Nov 9 to Dec
21

Descriptive Statistics
1. Mean (the average value of the series, obtained by adding up the series and dividing
by the number of observations)
2. Median (the middle value (or average of the two middle values) of the series when
the values are ordered from the smallest to the largest)
3. Maximum (the maximum value of the series in the current sample)
4. Minimum- (the minimum value of the series in the current sample)
5. Std. Dev. (standard deviation) is a measure of dispersion or spread in the series
6. Skewness (the skewness of a symmetric distribution, such as the normal distribution,
is zero)
7. Kurtosis (the kurtosis of the normal distribution is 3 )
8. Jarque-Bera test (under the null hypothesis of a normal distribution, the Jarque-
Bera statistic is distributed as χ2 with 2 degrees of freedom 4.
9. Probability (the probability that a Jarque-Bera statistic exceeds the observed value
under the null indicates that a small probability value leads to the rejection of the null
hypothesis of a normal distribution

AUTO BANK FMCG IT NIFTY_50 P_BANK
Mean 9062.878 18621.04 20300.55 9870.020 8148.153 10269.42
Median 9032.025 18437.43 20236.53 9929.325 8127.825 10169.58
Maximum 9819.800 20200.25 21561.10 10322.85 8525.750 11210.45
Minimum 8681.600 18069.40 19701.25 9434.600 7929.100 9961.100
Std.Dev. 252.0098 507.3039 416.1654 280.4223 122.2434 295.4629
Skewness 1.421131 1.578799 1.435806 -0.164435 1.150137 1.780212
Kurtosis 5.463614 4.879789 5.307650 1.644165 5.084642 5.600925
Jarque-Bera 17.68481 16.88004 16.96426 2.433054 12.04624 24.30179
Probability 0.000144 0.000216 0.000207 0.296257 0.002422 0.000005
Sum 271886.3 558631.1 609016.4 296100.6 244444.6 308082.6
Sum Sq.Dev. 1841760. 7463359. 5022616. 2280464. 433360.3 2531652.
Observations 30 30 30 30 30 30
DESCRIPTIVE STATISTICS

Impact on Nifty 50
• Mean is 8148.153 and median is 8127.825 of NIFTY
50 (dependent variable) which show where center of
data is located.
• Skewness of this data is 1.150137 which is > than 0
• Value of Kurtosis is 5.084642 which are more than
normal distributioan value of 3 that means it has
leptokurtic distribution because here is mean is more
than median.
• The value of Jarque-Bera is 12.04624 which is more
than normal value of 5.99 and probability is 0.002422
which shows data is not normally distributed.

Impact on Auto Sector
• Mean is 9062.878 and median is 9032.025 of AUTO
(independent variable) which show where center of
data is located.
normal distribution value of 3 that means it has
than median.

Impact on IT Sector
• Mean is 9870.020 and median is 9929.325 of IT
data is located.
• Skewness of this data is -0.164435 which is < than 0.
• Value of Kurtosis is 1.644165 which is less than
normal distribution value of 3 that means it don’t have
leptokurtic distribution rather it has platykurtic
distribution.
which shows data is normally distributed

Impact on Banking Sector
• Mean is 18621.04 and median is 18437.43 of BANK
(independent variable) which show where center of data is
located.
• Skewness of this data is 1.578799 which is > than 0 so its
distribution of right, having extreme values at right side,
and mean is having concentration of most values at the left
side.
• Value of Kurtosis is 4.879789 which are more than normal
distribution value of 3 that means it has leptokurtic
distribution because here is mean is more than median.
• The value of Jarque-Bera is 6.88004 which is more than
normal value of 5.99 and probability is 0.000216 which
shows data is not normally distributed.

Impact on FMCG Sector
• Mean is 20300.55 and median is 20236.53 of FMCG
data is located.
• Skewness of this data is 1.435806 which is > than 0 so
than median.
which shows data is not normally distributed

Impact on Private Bank
• Mean is 10269.42 and median is 10169.58 of PVT
BANK (independent variable)
than median.

Granger Casualty Test
• Granger causality is used to examine whether there is short run
relationship existed between each of the variables. It was significant
to note that the null hypothesis of Granger Causality indicated there
was no granger causality while rejection of null hypothesis indicated
that there was a relationship existed between the variables.
• Granger causality means that one variable past can help predict
another variable.
• There has hypothesis testing in the Granger Causality Test to
examine the causality relationship between dependent variable and
independent variable.
• Ho : Independent variable does not granger cause dependent
variable
• H1 : Independent variable does granger cause dependent
variable.

LOG_OF_AUTO LOG_OF_BANK LOG_OF_FMCG LOG_OF_IT LOG_OF_NIFTY LOG_OF_PVT_BANK
11/10/2016 -0.006298 0.034345 0.006830 -0.002948 0.011057 0.026997
11/11/2016 -0.047196 -0.023109 -0.034693 -0.023066 -0.027281 -0.024709
11/15/2016 -0.052910 -0.023012 -0.025185 0.005539 -0.022903 -0.033650
11/16/2016 0.010148 -0.009462 -0.008995 0.016102 0.000388 -0.010471
11/17/2016 -0.000342 -0.001060 0.001385 -0.015133 -0.003909 -0.000349
11/18/2016 0.007626 -0.006771 -0.009678 -0.002082 -0.000724 -0.005771
11/21/2016 -0.034223 -0.027412 -0.013043 -0.002398 -0.018122 -0.021826
11/22/2016 0.019539 0.005528 0.011760 0.005875 0.009189 0.007309
11/23/2016 0.004311 -0.000418 0.002095 0.004239 0.003866 0.000429
11/24/2016 -0.015489 -0.015480 -0.006247 0.011080 -0.008476 -0.021162
11/25/2016 0.002954 0.013666 0.015269 0.046404 0.018508 0.015834
11/28/2016 0.005254 -0.011185 0.013252 -0.000860 0.001552 -0.008877
11/29/2016 0.021441 -0.004255 -0.005865 -0.004622 0.001875 -0.003711
11/30/2016 0.010573 0.021929 0.008889 0.002785 0.010063 0.021342
12/01/2016 -0.008020 -0.010759 0.003255 -0.003764 -0.003850 -0.011553
12/02/2016 -0.016989 -0.009859 -0.018405 -0.012718 -0.013035 -0.010942
12/05/2016 0.017570 0.008798 0.014706 -0.008781 0.005174 0.008595
12/06/2016 -0.003571 0.000652 -0.009104 -0.000107 0.001770 0.000547
12/07/2016 0.004345 -0.010190 -0.007710 -0.005818 -0.005060 -0.008873
12/08/2016 0.026788 0.015309 0.018348 0.016030 0.017714 0.015303
12/09/2016 -0.005040 0.009693 0.005453 0.006181 0.001805 0.008312
12/12/2016 -0.017477 -0.016331 -0.014787 -0.004371 -0.011070 -0.017113
12/13/2016 0.011374 0.003966 0.008427 0.010469 0.006222 0.003606
12/14/2016 -0.005447 -0.006768 -0.008729 0.006965 -0.004798 -0.005454
12/15/2016 -0.003044 0.003247 -0.008776 0.006573 -0.003532 0.002425
12/16/2016 0.003858 -0.004813 -0.007650 0.002664 -0.001737 -0.006404
12/19/2016 -0.006076 -0.003049 -0.001272 6.85E-05 -0.004322 -0.001595
12/20/2016 -0.008278 -0.010331 0.004093 0.009656 -0.002712 -0.009402
12/21/2016 -0.001114 0.000835 -0.011511 -0.009206 -0.002614 0.000341
Log of Different Sectors

LOG_OF_AUTO does not Granger Cause LOG_OF_BANK 4.00562 0.0328
LOG_OF_FMCG does not Granger Cause LOG_OF_AUTO 27 0.28915 0.7517
LOG_OF_AUTO does not Granger Cause LOG_OF_FMCG 0.47037 0.6309
LOG_OF_IT does not Granger Cause LOG_OF_AUTO 27 0.94654 0.4033
LOG_OF_AUTO does not Granger Cause LOG_OF_IT 0.99840 0.3846
LOG_OF_NIFTY does not Granger Cause LOG_OF_AUTO 27 0.14426 0.8665
LOG_OF_AUTO does not Granger Cause LOG_OF_NIFTY 0.41334 0.6665
LOG_OF_PVT_BANK does not Granger Cause LOG_OF_AUTO 27 4.15807 0.0294
LOG_OF_AUTO does not Granger Cause LOG_OF_PVT_BANK 7.64678 0.0030
LOG_OF_FMCG does not Granger Cause LOG_OF_BANK 27 1.89575 0.1739
LOG_OF_BANK does not Granger Cause LOG_OF_FMCG 3.90602 0.0353
LOG_OF_IT does not Granger Cause LOG_OF_BANK 27 0.65480 0.5294
LOG_OF_BANK does not Granger Cause LOG_OF_IT 1.12319 0.3432
LOG_OF_NIFTY does not Granger Cause LOG_OF_BANK 27 2.71855 0.0881
LOG_OF_BANK does not Granger Cause LOG_OF_NIFTY 8.64241 0.0017
LOG_OF_PVT_BANK does not Granger Cause LOG_OF_BANK 27 1.25034 0.3060
LOG_OF_BANK does not Granger Cause LOG_OF_PVT_BANK 2.98671 0.0712
LOG_OF_IT does not Granger Cause LOG_OF_FMCG 27 0.77955 0.4709
LOG_OF_FMCG does not Granger Cause LOG_OF_IT 0.51377 0.6052
LOG_OF_NIFTY does not Granger Cause LOG_OF_FMCG 27 0.49040 0.6189
LOG_OF_FMCG does not Granger Cause LOG_OF_NIFTY 0.51136 0.6066
LOG_OF_PVT_BANK does not Granger Cause LOG_OF_AUTO 27 4.15807 0.0294
LOG_OF_AUTO does not Granger Cause LOG_OF_PVT_BANK 7.64678 0.0030
LOG_OF_FMCG does not Granger Cause LOG_OF_BANK 27 1.89575 0.1739
LOG_OF_BANK does not Granger Cause LOG_OF_FMCG 3.90602 0.0353
LOG_OF_NIFTYdoes not Granger Cause LOG_OF_BANK 27 2.71855 0.0881
LOG_OF_NIFTYdoes not Granger Cause LOG_OF_FMCG 27 0.49040 0.6189
LOG_OF_PVT_BANK does not Granger Cause LOG_OF_FMCG 27 3.41888 0.0509
LOG_OF_FMCG does not Granger Cause LOG_OF_PVT_BANK 2.67104 0.0915
LOG_OF_NIFTYdoes not Granger Cause LOG_OF_IT 27 1.77466 0.1930
LOG_OF_IT does not Granger Cause LOG_OF_NIFTY 0.57162 0.5728
LOG_OF_NIFTYdoes not Granger Cause LOG_OF_BANK 27 2.71855 0.0881
LOG_OF_NIFTYdoes not Granger Cause LOG_OF_FMCG 27 0.49040 0.6189
LOG_OF_NIFTYdoes not Granger Cause LOG_OF_IT 27 1.77466 0.1930
LOG_OF_PVT_BANK does not Granger Cause LOG_OF_IT 27 1.75131 0.1969
LOG_OF_IT does not Granger Cause LOG_OF_PVT_BANK 0.52695 0.5977
LOG_OF_PVT_BANK does not Granger Cause LOG_OF_NIFTY 27 5.48466 0.0117
LOG_OF_NIFTYdoes not Granger Cause LOG_OF_PVT_BANK 3.01531 0.0696
LOG_OF_NIFTY does not Granger Cause LOG_OF_IT 27 1.77466 0.1930
LOG_OF_NIFTY does not Granger Cause LOG_OF_PVT_BANK 3.01531 0.0696
LOG_OF_NIFTY does not Granger Cause LOG_OF_IT 27 1.77466 0.1930
LOG_OF_NIFTY does not Granger Cause LOG_OF_PVT_BANK 3.01531 0.0696

Hypothesis and results
• Ho: Independent variables do not granger cause dependent variable.
• H1 : Independent variable granger cause dependent variable
• The results of Granger Causality test, where we examined cause effect
between the dependent variable (NIFTY) and independent variable of all
the five sectors (Bank, FMCG, IT, Private Bank, Auto).
• We reject the Null Hypothesis ( ‘p’ coefficient less than 0.05) In following
cases where –
• Bank causes change in Nifty and Private Bank causes Nifty.
• Further inter-linkages were seen in the form of uni-directional causality
– Bank→ Nifty.
– Private Bank → Nifty

FINDINGS
• According to our study we have found that demonetization has a significant impact on all the
sectors except IT.
• Average closing price of NIFTY for 30 days after the demonetization is 5.78% lower than the
average closing prices for 30 days before the demonetization.
• The volatility of Banking index increased by 107.79%, this is the largest increase in volatility
among the selected indices for this study.
• The volatiilty of Auto index increased by79.39%
• The volatility of FMCG index has increased by 73.41% after the demonetization.
• The volatility of Private Sector Bank index has increased by 103.41%. Whereas Public
Sector Bank index increased by 43.72%.
• In terms of distribution, all the variables are positively skewed except IT which are negatively
skewed.
• The Kurtosis of all the variables is more than 3 except IT. Therefore the distribution is
Laptokurtic.
• As a matter of fact the JB statistics of all the variables is more than the normal value i.e. 5.99
(chi-square at 2 df ). Therefore all the variables except the IT do not meet the normal
distribution criteria.
• In Granger Causality test we have found that there is a uni-directional causality between the
dependent and independent variables.

Conclusion
• The impact on stock market was temporary i.e. the prices of stocks were fluctuating in the
week of announcement of demonetization.
• Undoubtedly the exact impact of demonetization on Indian economy can be figured out only
in long run but in short run demonetization has considerable impact on people, businessmen,
small and medium scale industries, companies and economy.
• Since demonetization did not affected the stocks of the IT sector, it was a better opportunity
for Investors and speculators to purchase stocks at lower prices when they were trading at
lower prices in the week of announcement of demonetization.
• Due to the increase in demand for digitization , growth and expansion for IT companies is on
the rise. Service Providers and payment banks are also setting up new applications to attract
more users. Black Market transactions also got reduced to a great extent post note ban.
• All these indicators shall definitely have an impact on capital market in the long run as well.
As the stock market is an indicator of Economic Growth, to understand the effect of the
Government's Note ban, a further survey and analysis over a longer period can be taken up.

Suggestions
• Demonetisation is a one-time event and will not have much long
term effect. It alone is not sufficient to counter black money and
corruption in the country.
• Rather other measures are more crucial like bringing the offshore
tax evaders to book whose names figure in the Panama papers, raid
on benami properties, making donations to political parties open to
public scrutiny and making it mandatory for all donations above Rs
2000 to political parties and religious places to be through digital
means only.
• Demonetisation of old currency notes surely has had some positive
impact like reducing the cash flow to terror organisations,
dismantling of counterfeit currency infrastructure, better income tax
and indirect taxation, boost to digital economy.

Impact of Demonetisation on various sectors of Indian stock Market

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