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Impact of Demonetisation on Indian
Stock Market
Mini Project Report
submitted in partial fulfilment of the requirements for
the award of the Degree of
MAS TE R OF TECHNOLOGY
in Mechanical Engineering (Financial Engineering)
of the APJ Abdul Kalam Kerala Technological University
Submitted by:
Anagha V Mukundan
TVE16MEFE02
DEPARTMENT OF MECHANICAL ENGINEERING
COLLEGE OF ENGINEERING TRIVANDRUM
June 2017
DEPARTMENT OF MECHANICAL ENGINEERING
COLLEGE OF ENGINEERING TRI VAN DRUM
2017
CERTIFICATE
This is to certify that the Mini Project report titled Impact of Demoneti-
sation on Indian Stock Market being submitted by Anagha V Mukun-
dan, Roll No. TVE16MEFE02, in partial fulfilment of the requirements
for the award of the Degree of Master of Technology, is a bonafide record
of the Mini Project work done by Anagha V Mukundan at College of En-
gineering Trivandrum.
Dr. Bijulal D Dr. K. Jayaraj
Project Supervisor P. G. Coordinator
Dr. K. Krishnakumar
Department Head
Declaration
I undersigned, hereby declare that the mini-project titled “ Impact of Demoneti-
sation on Indian Stock Market” submitted in partial fulfilment of the requirements for
the award of degree of Master of Technology of the APJ Abdul Kalam Technological
University, Kerala is a bonafide work done by me under the supervision of Dr. Bijulal
D. This submission represents my ideas in my own words and where ideas or words of
others have been included, I have adequately and accurately cited and referenced the
original sources. I also declare that I have adhered to ethics of academic honesty and
integrity and have not misrepresented or fabricated any data or idea or fact or source
in my submission. I understand that any violation of the above will be a cause for dis-
ciplinary action by the institute and/or the University and can also evoke penal action
from the sources which have thus not been properly cited or from whom proper permis-
sion has not been obtained. This report has not been previously formed the basis for the
award of any degree, diploma, or similar title of any other university.
Place: Signature
Date: Anagha V Mukundan
ii
Acknowledgements
I am very thankful to Dr. Bijulal D (Associate Professor, Mechanical Engineer-
ing Dept.CET) for his careful guidance, support and valuable suggestions which im-
proved the quality of the work and shaped it into its existing form. I would like to thank
Dr. Unnikrishnan V S, the Stream head,Financial Engineering., Dr.K.C.Raveendranathan,
the Principal, CET. and Dr. K. Krishnakumar, the Head of the Mechanical Engineering
Department, CET for providing the facilities and the right atmosphere for doing this
work. I express my gratitude towards all other staff members of our department for
their advices and encouragement. I appreciate the service of the management and the
staff of the college.
Anagha V Mukundan
iii
iv
Abstract
Demonetisation is the act of stripping a currency unit of its status as legal ten-
der. On 8 November 2016, the Government of India announced the demonetisation
of all Rs.500 and Rs.1, 000 banknotes of the Mahatma Gandhi Series. The sudden
nature of the announcement—and the prolonged cash shortages in the weeks that fol-
lowed—created significant disruption throughout the economy, threatening economic
output. The move was heavily criticized as poorly planned and unfair, and was met
with protests, litigation, and strikes. The aim of this project is to study the impact of
demonetisation and its effects on stock market. The historical data was collected for 54
companies from 13 different sectors. The data collected from 1 July 2016 to 28 Feb
2017 was analysed to test whether there is a significant difference in average price, total
traded quantity and total trades before and after demonetisation.
Keywords: demonetisation, impact, cash shortage, protests, litigation, data, normal
distribution, one sample two tailed test, difference in trades
v
vi
Contents
Page
List of Tables x
List of Figures xi
1 Introduction 1
1.1 ProblemDefinition ............................ 2
1.2 Objectives of the Project Work . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Scope of the Project Work . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 ResearchMethodology .......................... 3
1.5 Limitations of the Project Work . . . . . . . . . . . . . . . . . . . . . . 4
2 Literature Review 5
3 Data Collection 7
3.1 Introduction................................ 7
3.2 SourceofData .............................. 7
3.3 Data Record Preparation . . . . . . . . . . . . . . . . . . . . . . . . . 8
4 Data Analysis 9
4.1 Introdution ................................ 9
4.2 Hypothesis ................................ 9
4.2.1 Hypothesis1 ........................... 9
4.2.2 Hypothesis2 ........................... 9
4.2.3 Hypothesis3 ........................... 9
4.3 Testingofhypothesis ........................... 10
5 Results 11
5.1 Results on Average Price . . . . . . . . . . . . . . . . . . . . . . . . . 11
5.2 Results on Total Traded Quantity . . . . . . . . . . . . . . . . . . . . . 15
5.3 Results on Total Trades . . . . . . . . . . . . . . . . . . . . . . . . . . 20
6 Analysis of the results 25
6.1 Analysis of companies . . . . . . . . . . . . . . . . . . . . . . . . . . 25
6.1.1 Effect on average price . . . . . . . . . . . . . . . . . . . . . . 25
6.1.2 Effect on total traded quantity . . . . . . . . . . . . . . . . . . 25
6.1.3 Effect on total trades . . . . . . . . . . . . . . . . . . . . . . . 26
vii
List of Tables
Table Page
5.1 AUTOMOTIVES ............................. 11
5.2 CEMENT................................. 12
5.3 CLOTHING................................ 12
5.4 COTTON ................................. 12
5.5 FOODS .................................. 13
5.6 PAPER .................................. 13
5.7 REALESTATE.............................. 13
5.8 RETAIL.................................. 13
5.9 RUBBER ................................. 14
5.10STEEL .................................. 14
5.11SUGAR.................................. 14
5.12TEA.................................... 15
5.13TEXTILES ................................ 15
5.14AUTOMOTIVE.............................. 16
5.15CEMENT ................................. 16
5.16CLOTHING................................ 16
5.17COTTON ................................. 17
5.18FOODS .................................. 17
5.19PAPER .................................. 17
5.20REALESTATE .............................. 18
5.21RETAIL.................................. 18
5.22RUBBER ................................. 18
5.23STEEL .................................. 19
5.24SUGAR.................................. 19
5.25TEA.................................... 19
5.26TEXTILES ................................ 20
5.27AUTOMOTIVE.............................. 20
5.28CEMENT ................................. 21
5.29CLOTHING................................ 21
5.30COTTON ................................. 21
5.31FOODS .................................. 21
5.32PAPER .................................. 22
5.33REALESTATE .............................. 22
5.34RETAIL.................................. 22
5.35RUBBER ................................. 23
ix
xii
Chapter 1
Introduction
Demonetization is the act of stripping a currency unit of its status as legal tender.
It occurs whenever there is a change of national currency. The current form or forms
of money is pulled from circulation and retired, often to be replaced with new notes or
coins. The government has implemented a major change in the economic environment
by demonetizing the high value currency notes – of Rs 500 and Rs 1000 denomination
on 8th November 2016. The RBI issued Two thousand rupee notes and new notes of
Five hundred rupees which were placed in circulation from 10th November 2016. Notes
of one hundred, fifty, twenty, ten, five, two and one rupee will remain legal tender and
will remain unaffected by this decision. The government’s goal (and rationale for the
abrupt announcement) was to combat India’s thriving underground economy on several
fronts: eradicate counterfeit currency, fight tax evasion (only 1% of the population pays
taxes), eliminate black money gotten from money laundering and terrorist-financing
activities, and to promote a cashless economy. The aim of the action was to curb cor-
ruption, counterfeiting, the use of high denomination notes for terrorist activities and
especially the accumulation of black money, generated by income that has not been
declared to the tax authorities.
According to Dr.Balamurugan and Hemalatha (2017), the sudden move to de-
monetise Rs 500 and Rs 1,000 currency notes is not new. Rs 1,000 and higher denom-
ination notes were first demonetised in January 1946 and again in 1978. The highest
denomination note ever printed by the Reserve Bank of India was the Rs 10,000 note
in 1938 and again in 1954. But these notes were demonetised in January 1946 and
again in January 1978, according to RBI data. Rs 1,000 and Rs 10,000 bank notes were
in circulation prior to January 1946. Higher denomination banknotes of Rs 1,000, Rs
5,000 and Rs 10,000 were reintroduced in 1954 and all of them were demonetised in
January 1978. The move was enacted under the High Denomination Bank Note (De-
monetisation) Act, 1978. It was termed as “an Act to provide in the public interest for
the demonetisation of certain high denomination bank notes and for matters connected
therewith or incidental thereto.” The Rs 1,000 note made a comeback in November
2000. Rs 500 note came into circulation in October 1987. The move was then justified
as attempt to contain the volume of banknotes in circulation due to inflation. However,
in the days following the demonetisation, banks and ATMs across the country faced
severe cash shortages. The cash shortages had detrimental effects on a number of small
businesses, agriculture, and transportation, while people seeking to exchange their notes
had lengthy waits, and several deaths were linked to the rush to exchange cash but 36
years before during the situation of demonetisation, higher value notes were almost im-
possible to possess by the common man given the value of these amounts then. It has
been a choppy ride for the Indian equity markets that had been hit by the demonetisa-
tion and the surprise victory of Donald Trump in the US Presidential elections, exactly
a month ago on November 8. India’s Nifty (down 3.5%) was the second worst perform-
ing index in Asia after Philippines, and the fourth globally – after Mexico (down 5.9%),
Brazil (4.3%) and Philippines (down 3.9%). The frontline benchmark indices – S and
P BSE Sensex and the Nifty50 – that lost around 7.5% during the month, though have
managed to recoup some losses. The Nifty50 index, for instance, hit an intra-day low
of 7,916 levels on November 21 has clawed back to 8,200 levels by December 08.
1.1 Problem Definition
India is a highly diversified economy. Demonetisation has been applied in many
developed countries before but it haven’t been implemented in such a highly demo-
graphically and economically diversified country. Therefore the impact of such a move-
ment on Indian economy should be studied.
Stock Market data reflects the economic development in each sector of the coun-
try. Average price represents the value of the considered sector. Total traded quantity
gives the idea about the impact on manufacturers and the extent to which each stock
can be purchased by the consumers in the economy. Total trades represent the direct
quantitative result of demand and supply in the economy after the demonetisation. Due
to demonetisation, the average price and the total traded quantity of Indian stock mar-
ket has been affected. So it is important to find the changes happened in the economy
to prevent any future complications. The stock market data after demonetisation when
compared to the past data will help to find the extend of impact on demand and supply
in the economy as well as on consumers and manufacturers.
In this miniproject, the impact of demonetisation on average price, total traded
quantity and the total traded values of stocks of 54 industries are analysed and studied.
2
These industries are selected from 13 different sectors such as automotives, cement,
cotton, clothing, food, paper, retail, rubber, real estate, steel, sugar, tea and textiles.
This problem is a statistical analysis problem considering the mean and variance of
trading data of these industries.
1.2 Objectives of the Project Work
The objectives of this project work are:
•To study the impact of demonetization on different sectors of stock market.
•To find out if there is a significant difference in average price, total traded quantity
and total trading value of the stocks after demonetization.
•To find out whether there is an increase or decrease in average price, total traded
quantity and total trading value of the stocks after demonetization.
•To find out whether the change in total trading value is either due to average price
of the stock or the total trading quantity.
1.3 Scope of the Project Work
Demonetisation has the potential to generate long-term benefits in terms of re-
duced corruption, greater digitalization of the economy, increased flows of financial
savings, and greater formalization of the economy, all of which could eventually lead
to higher GDP growth, better tax compliance and greater tax revenues. Notwithstand-
ing its long-term potential, demonetisation will impose short term costs on the economy
like decrease in denomination cash, inflation, increase in cashless transactions, decrease
in sales for small traders and retailers This work aims to find out whether there is a sig-
nificant difference in trading after demonetization on various sectors of stock market.
1.4 Research Methodology
Stock market data for various sectors are collected from National Stock Ex-
change. The data collected from July 2016 to Feb 2017 was assumed to be in normal
distribution and analysed to test whether there is a significant difference in average
price, total traded quantity and total traded value before and after demonetisation. The
data was analysed using one sample two tailed test.
3
1.5 Limitations of the Project Work
•Only 13 sectors were considered for the analysis.
•Time limit is a major constraint. The study has taken into consideration only
112 days after demonetisation thus the study was done between the period of 9th
November 2016 to 28th February 2017 but the result may vary during the long
run.
•The sample selected was limited to 55 industries only. Therefore the findings
given on the basis of this research cannot be extrapolated (applied) to the entire
population.
4
Chapter 2
Literature Review
Demonetisation announced on November 8, 2016 was aimed at addressing cor-
ruption, black money, counterfeit currency and terror financing. Although demoneti-
sation holds huge potential benefits in the medium to long-term, given the scale of
operation, it was expected to cause transient disruption in economic activity. In addi-
tion to its impact on the agrarian sector, demonetisation also had a significant impact
on the informal sector, which currently employs more than 80% of India’s workforce,
including through micro, small and medium enterprises (MSME). MSMEs are heavily
cash dependent, often managed by individual proprietors with small turnovers, limited
reserves and access to finance. According to Peter and Reema (2017), demonetisation
caused serious disruptions to such businesses, many of which were already struggling
due to the steady decline in credit flows and a surge in non-performing assets in rural
banks. This reportedly led to a substantial drop in production.
According to Dr.Sumathy and Savitha (2017), demonetization has affected every
Indian, but it has hit the agricultural sector the hardest. Agriculture in India accounts
for 50% of the workforce. Farmers, who are the backbone of our national economy,
were severely affected by the note demonetization of which invalidated 86% of India’s
currency. Most of them will get loans from cooperative banks which now don’t have
cash to supply them. So farmers cannot buy seeds, fertilizers and other things required
for farmingThe cash transactions in this economy are far more than the total number
of electronic transactions done on a daily basis. Agriculture is truly dependent upon
cash transactions via cash is direct burden to the farmers markets because they should
purchase all their agriculture inputs even bigger landholders may face problems such as
paying daily wages to the farmers and purchasing agricultural needs for growing crop.
According to GeetaRani (2016), the production for some companies has been
impacted by the non-availability of select raw materials (e.g., agri- commodities). Com-
panies are also cutting down production to adjust for the unanticipated rigger levels of
unsold finished goods across the supply chain. Supply chains are further getting af-
fected by the cash crunch faced by transportation vendors. Traders and distributors are
unable to pick up stock because of the liquidity crunch, which is leading to manufac-
turers extending the credit period. Demand disturbances are likely to have an impact
on the credit-repayment ability of traders and distributors in the near future, leading to
defaults.
There are 118.9 million cultivators across the country (or 24.6% of the total
workforce of over 481 million) and 144.3 million are agricultural laborers, with over
600 million Indians dependent on agriculture. According to Dr.Rathore (2017), demon-
etisation has severely hurt this sector, which includes farmers and daily wage laborers,
and employs 93 per cent of India’s workers. Indian farmers use cash transaction for
buying seeds and fertilizers, for trading products and also for trading commodity. Daily
agriculture laborers are paid in cash and cash is used daily for buying utility items and
grocery. With 78 branches per million people in rural and semi-urban areas and the total
number of KCC in India are 79.6 million, around 80 per cent of farmers are bound to
suffer.
The Indian real estate sector has been facing significant challenges in the past
few years in terms of sales and overall growth. As against general perception, real
estate prices actually increased across the country during the demonetisation quarter
going by latest House Price Index figures released on RBI’s website. According to
Dr.Balamurugan and Hemalatha (2017), the all India House Price Index (HPI) increased
by 2.3% from 234.9 for second quarter of 2016-17 to 240.2 in Q3 (October to December
2016) of 2016-17 as per provisional figures. This quarter would reflect the impact of
demonetisation of the old Rs 500 and Rs 1000 notes by the government on November
8, 2017. The rubber market, already in the doldrums due to factors like fall in price and
collapse of a few prominent rubber marketing societies, has been dealt a severe blow by
demonetisation. The domestic rubber market reeling under the impact of demonetisa-
tion has not gained anything from the rise in the prices in the international market which
have hit a three-year high. The tight supply-demand situation, expectations of further
firming up of crude oil prices and buoyant demand from China have helped in scaling
up the prices in the global markets. The latest figures from automobile sector are also
painful enough for rubber growers in the country. Natural rubber is used more in the
tyres of commercial vehicles. On account of demonetisation commercial vehicles sales
is down by 11.58 per cent in November this year. Meaning reduced consumption from
the industry.
6
Chapter 3
Data Collection
3.1 Introduction
The aim of this project is to study the impact of demonetisation on the sales of
various firms. The market data is price and trade-related data for a financial instrument
reported by a trading venue such as a stock exchange. Market data allows traders and
investors to know the latest price and see historical trends for instruments such as eq-
uities, derivatives and currencies. The market data for a particular instrument would
include the identifier of the instrument and where it was traded such as the ticker sym-
bol and exchange code plus the latest bid and ask price and the time of the last trade. It
may also include other information such as volume traded, bid and offer sizes and static
data about the financial instrument that may have come from a variety of sources. There
are a number of financial data vendors that specialise in collecting, cleaning, collating
and distributing market data and this has become the most common way that traders
and investors get access to market data. Market price data is not only used in real time
to make on-the-spot decisions about buying or selling, but historical market data can
also be used to project pricing trends and to calculate market risk on portfolios of in-
vestments that may be held by an individual or an institutional investor.The market data
determines the performance of a firm on a certain period of time.
3.2 Source of Data
The trading data of 13 different sectors were collected from National Stock Ex-
change of India Ltd. The sectors include 54 companies from automotives, cement,
clothing, cotton, food, paper, real estate, retail, rubber, steel, sugar, tea and textiles. The
data was collected from 1 July 2016 to 28 Feb 2017.
3.3 Data Record Preparation
The data record consists of daily trading data from 1 July 2016 to 28 Feb 2017
which comprises of the average price and the total traded quantity of the stocks of 54
companies. The total trades is obtained by the product of average price and the total
traded quantity.
8
Chapter 4
Data Analysis
4.1 Introdution
Data Analysis is the process of inspecting, cleaning, transforming, and mod-
elling data with the objective of discovering useful information, arriving at conclusions.
The data collected includes sales data for each month from January 2015 to February
2017. The sales data collected from 1 January 2015 to 28 February 2017 is assumed to
be in normal distribution. The data collected from 1 January 2015 to 8 November 2016
is considered as one population and 9 November 2016 to 28 January 2017 as another
population.
4.2 Hypothesis
4.2.1 Hypothesis 1
H0: There is no significant difference in average price after demonetisation
H1: There is a significant difference in average price after demonetisation
4.2.2 Hypothesis 2
H0: There is no significant difference in total traded quantity after demonetisa-
tion
H1: There is a significant difference in total traded quantity after demonetisation
4.2.3 Hypothesis 3
H0: There is no significant difference in total trades after demonetisation
9
H1: There is a significant difference in total trades after demonetisation
The hypothesis will be checked at 95% confidence interval
H0:µ1=µ2(orµ1−µ2= 0)
H1:µ16=µ2(orµ1−µ26= 0)
4.3 Testing of hypothesis
Let x1be the mean of sales from 1 July 2016 – 8 Nov 2016 and x2be the mean
of sales from 9 Nov 2016 to 28 Feb 2017. Since, the population variances are known,
Zstatistic is used
Z0=((x1–x2)–(µ1−µ2))
q(σ2
1
n2
1+σ2
2
n2
2)
Figure 4.1: Normal distribution
The normal distribution is the most important and most widely used distribution
in statistics. The figure shows a normal distribution curve with 95% confidence interval.
•The data is analyzed using one sample two tailed test.
•Zcritical is checked at 95% confidence interval
•If Zcalc <Zcritical, we accept H0
•It means there is no significant difference in sales after demonetisation
•If Zcalc >Zcritical, we accept H1
•It means there is a significant difference in sales after demonetisation
10
Chapter 5
Results
The industries are analyzed to check whether there is a significant increase or
decrease in average price, total traded quantity and total trading values of the stocks.
The results obtained are given below.
5.1 Results on Average Price
Average price represents the value of the stocks of each industry. The following
tables represent the values for Z0for each sector.
Table 5.1: AUTOMOTIVES
Company Name Z0Zcritical Accept/Reject H0
Bajaj-Auto Limited -8.58 1.96 Reject H0(-)
Mahindra CIE Automotive Limited 3.10 1.96 Reject H0(+)
Maruti Suzuki India Limited 4.77 1.96 Reject H0(+)
PPAP Automotives Limited 3.79 1.96 Reject H0(+)
SETCO Automotives Limited -24.15 1.96 Reject H0(-)
From Table 5.1, it can be observed that all 5 industries has rejected the null
hypothesis. Since their Z0values vary greatly from Zcritical, a significant decrease in
average price could be observed in the automotives sector. Mahindra CIE Automotive
Ltd, Maruti Suzuki India Ltd and PPAP Automotives Ltd showed an increase in their
average price while Bajaj-Auto Limited and SETCO Automotives showed a decrease.
From Table 5.2, it can be observed that only 3 companies(Ambuja cements Ltd,
JK Cements Ltd and UltraTech Cements Ltd) have rejected the null hypothesis. Since
their Z0values vary greatly from Zcritical, a significant decrease in average price could
be observed on these companies of the cement sector. The companies which showed
Table 5.2: CEMENT
Company Name Z0Zcritical Accept/Reject H0
Ambuja Cements Limited -23.29 1.96 Reject H0(-)
JK Cements Limited -5.05 1.96 Reject H0(-)
Ramco Cements Limited 1.92 1.96 Accept H0(+)
Sagar Cements Limited 0.67 1.96 Accept H0(+)
UltraTech Cements Limited -10.24 1.96 Reject H0(-)
an increase in average price accepted the null hypothesis. So there is no significant
difference in Ramco Cements Limited and Sagar Cements Limited.
Table 5.3: CLOTHING
Company Name Z0Zcritical Accept/Reject H0
Indian card clothing companyLimited -16.12 1.96 Reject H0(-)
Kewal Kiran Clothing Limited -17.41 1.96 Reject H0(-)
VIP Clothing Limited -28.36 1.96 Reject H0(-)
Zodiac Clothing Company Limited -21.55 1.96 Reject H0(-)
From Table 5.3, it can be observed that all 4 industries has rejected the null
hypothesis. Since their Z0values vary greatly from Zcritical, a significant decrease in
average price could be observed in the clothing sector because the average price of the
stocks of Indian card clothing company Limited, Kewal Kiran Clothing Limited, Zodiac
Clothing Company, and VIP Clothing Limited has decreased after demonetisation.
Table 5.4: COTTON
Company Name Z0Zcritical Accept/Reject H0
Ambika Cotton mills Limited 18.47 1.96 Reject H0(+)
Malwa cotton sponging mills Limited 4.08 1.96 Reject H0(+)
Surya Lakshmi cotton mills Limited -12.47 1.96 Reject H0(-)
From Table 5.4, it can be observed that all 4 industries has rejected the null
hypothesis. Since their Z0values vary greatly from Zcritical, a significant difference
in average price could be observed in the cotton sector. The average price of Ambika
Cotton mills Limited and Malwa cotton sponging mills Limited has increased after
demonetization while Surya Lakshmi cotton mills Ltd showed a decrease.
From Table 5.5, it can be observed that all 5 industries has rejected the null
hypothesis. Since their Z0values vary greatly from Zcritical, a significant difference
in average price could be observed in the foods sector. The average price of Heritage
Foods Limited and Kohinoor Foods Limited has increased after demonetization while
12
Table 5.5: FOODS
Company Name Z0Zcritical Accept/Reject H0
Agro Tech Foods Limited -6.71 1.96 Reject H0(-)
Heritage Foods Limited 9.97 1.96 Reject H0(+)
Kohinoor Foods Limited 5.38 1.96 Reject H0(+)
Sita Shree Food products Limited -25.70 1.96 Reject H0(-)
Vidhi Speciality food ingredients Limited -6.12 1.96 Reject H0(-)
Agro Tech Foods Limited, Sita Shree Food products Limited and Vidhi Speciality food
ingredients Limited showed a decrease.
Table 5.6: PAPER
Company Name Z0Zcritical Accept/Reject H0
BalKrishna Paper mills Limited 4.72 1.96 Reject H0(+)
International Paper APPM Limited 5.85 1.96 Reject H0(+)
Rainbow Papers Limited 6.23 1.96 Reject H0(+)
Ruchira Papers Limited 11.59 1.96 Reject H0(+)
Star paper mills Limited 23.96 1.96 Reject H0(+)
From Table 5.6, it can be observed that all 5 industries has rejected the null hy-
pothesis. Since their Z0values vary greatly from Zcritical, a significant increase in aver-
age price could be observed in the paper sector. The average price of BalKrishna Paper
mills Limited, International Paper APPM Limited, Rainbow Papers Limited, Ruchira
Papers Limited, Star paper mills Limited has increased after demonetization.
Table 5.7: REAL ESTATE
Company Name Z0Zcritical Accept/Reject H0
Indiabulls Real Estate Limited -16.08 1.96 Reject H0(-)
From Table 5.7, it can be observed that the real estate sector has rejected the
null hypothesis. Since its Z0values vary greatly from Zcritical, a significant decrease
in average price could be observed in the real estate sector.
Table 5.8: RETAIL
Company Name Z0Zcritical Accept/Reject H0
Aditya Birla Fashion and Retail Limited -5.15 1.96 Reject H0(-)
Cantabil Retail India Limited -18.71 1.96 Reject H0(-)
V2 Retail Limited 8.30 1.96 Reject H0(+)
V-Mart Retail Limited 1.13 1.96 Accept H0(+)
13
From Table 5.8, it can be observed that out of 5 industries, 3 have rejected the
null hypothesis. Since their Z0values vary greatly from Zcritical, a significant differ-
ence in average price could be observed in the retail sector. The average price of V-Mart
Retail Limited and V2 Retail Limited has increased after demonetization while Aditya
Birla Fashion and Retail Limited and Cantabil Retail India Limited showed a decrease.
Table 5.9: RUBBER
Company Name Z0Zcritical Accept/Reject H0
Elgi Rubber Company Limited 6.80 1.96 Reject H0(+)
The Anandam Rubber Company Limited -0.85 1.96 Accept H0(-)
From Table 5.9, it can be observed that out of 2 industries, the average price
of Elgi Rubber Company Ltd showed a significant increase in average price since it
rejected the null hypothesis while The Anandam Rubber Company Limited showed a
little decrease since it accepted H0.
Table 5.10: STEEL
Company Name Z0Zcritical Accept/Reject H0
Jindal Steel Limited -0.25 1.96 Accept H0(-)
JSW Steel Limited -4.54 1.96 Reject H0(-)
Kalyani Steel Limited 1.78 1.96 Accept H0(+)
Lloyds Steel Industries Limited 4.05 1.96 Reject H0(+)
Tata Steel Limited 11.37 1.96 Reject H0(+)
From Table 5.10, it can be observed that out of 5 industries, the average price of
Kalyani Steel Limited, Lloyds steel Industries Ltd and Tata Steel Limited has increased
whilw Jindal Steel Ltd and JSW Steel Ltd has decreased. Sine the Z0values do not vary
widely, there is no significant difference in average price of the steel sector.
Table 5.11: SUGAR
Company Name Z0Zcritical Accept/Reject H0
Bajaj Hindusthan Sugar Limited -19.02 1.96 Reject H0(-)
Dalmia Bharat sugar and industries Limited 5.35 1.96 Reject H0(+)
Dhampura Sugar mills Limited 7.19 1.96 Reject H0(+)
Sakthi Sugars Limited -7.49 1.96 Reject H0(-)
Shree Renuka sugars Limited -12.86 1.96 Reject H0(-)
From Table 5.11, it can be observed that all 5 industries has rejected the null
hypothesis. Since their Z0values vary greatly from Zcritical, a significant difference
14
in average price could be observed in the sugar sector. The average price of Dalmia
Bharat sugar and industries Limited and Dhampura Sugar mills Limited has increased
after demonetization while Bajaj Hindusthan Sugar Limited, Sakthi Sugars Limited and
Shree Renuka sugars Limited has decreased after demonetization
Table 5.12: TEA
Company Name Z0Zcritical Accept/Reject H0
Dhanusri Tea and Industries Limited -2.03 1.96 Reject H0(-)
Jayshree Tea and Industries Limited -5.24 1.96 Reject H0(-)
PK Tea and Produce Company Limited 4.39 1.96 Reject H0(+)
The Grob Tea company Limited -6.01 1.96 Reject H0(-)
The United Nilgiri Tea Estate Company Limited -19.01 1.96 Reject H0(-)
From Table 5.12, it can be observed that all 5 industries has rejected the null
hypothesis. Since their Z0values vary greatly from Zcritical, a significant difference
in average price could be observed in the tea sector. The average price of PK Tea and
Produce Company Limited has increased after demonetization while The United Nilgiri
Tea Estate Company Limited, Jayshree Tea and Industries Limited, The Grob Tea com-
pany Limited and Dhanusri Tea amd Indistries Ltd has decreased after demonetization
Table 5.13: TEXTILES
Company Name Z0Zcritical Accept/Reject H0
Arrow Textiles Limited 1.32 1.96 Accept H0(+)
GTN Textiles Limited 0.10 1.96 Accept H0(+)
Lambodhara Textiles Limited -25.19 1.96 Reject H0(-)
Morarjee Textiles Limited 1.16 1.96 Accept H0(+)
Sutlej Textiles and Industries Limited 12.26 1.96 Reject H0(+)
From Table 5.13, the average price of Arrow Textiles Limited, GTN Textiles
Limited, Morarjee Textiles Limited and Sutlej Textiles and Industries Limited has in-
creased after demonetization while Lambodhara Textiles Ltd showed a decrease. Since
the Z0values donot vary widely from Zcritical, there is no significant difference in the
textiles sector.
5.2 Results on Total Traded Quantity
Total traded quantity gives the idea about the impact on manufacturers and the
extent to which each stock can be purchased by the consumers in the economy. The
following tables represent the values for Z0for each sector.
15
Table 5.14: AUTOMOTIVE
Company Name Z0Zcritical Accept/Reject H0
Bajaj-Auto Limited 0.22 1.96 Accept H0(+)
Mahindra CIE Automotive Limited -3.76 1.96 Reject H0(-)
Maruti Suzuki India Limited -0.01 1.96 Accept H0(-)
PPAP Automotives Limited -0.94 1.96 Accept H0(-)
SETCO Automotives Limited -4.58 1.96 Reject H0(-)
From Table 5.14, it can be observed that Mahindra CIE Automotives Ltd and
SETCO Automotives Ltd have rejected the null hypothesis and a decrease in total traded
quantity could be noticed. Bajaj Auto Ltd, Maruti Suzuki India Ltd and PPAP Automo-
tives Ltd have accepted H0. Since the Z0donot vary widely from Zcritical, there is no
significant difference in total traded quantity of the automotive sector.
Table 5.15: CEMENT
Company Name Z0Zcritical Accept/Reject H0
Ambuja Cements Limited -1.09 1.96 Accept H0(-)
JK Cements Limited 1.07 1.96 Accept H0(+)
Ramco Cements Limited 2.32 1.96 Reject H0(+)
Sagar Cements Limited -0.26 1.96 Accept H0(-)
UltraTech Cements Limited 2.02 1.96 Reject H0(+)
From Table 5.15, it can be observed that Ramco Cements Ltd and UltraTech
Cements Ltd have rejected the null hypothesis and a increase in total traded quantity
could be noticed. JK Cements Ltd, Ambuja Cements Ltd and Sagar Cements Ltd have
accepted H0. Since the Z0donot vary widely from Zcritical, there is no significant
difference in total traded quantity of the cement sector.
Table 5.16: CLOTHING
Company Name Z0Zcritical Accept/Reject H0
Indian card clothing company Limited -3.17 1.96 Reject H0(-)
Kewal Kiran Clothing Limited 0.68 1.96 Accept H0(+)
VIP Clothing Limited -5.25 1.96 Reject H0(-)
Zodiac Clothing Company Limited -1.27 1.96 Accept H0(-)
From Table 5.16, it can be observed that Indian card clothing company Limited
and VIP Clothing Limited have rejected the null hypothesis and a decrease in total
traded quantity could be noticed. Kewal Kiran Clothing Limited and Zodiac Clothing
Company Limited have accepted H0. Since the Z0donot vary widely from Zcritical,
there is no significant difference in total traded quantity of the clothing sector.
16
Table 5.17: COTTON
Company Name Z0Zcritical Accept/Reject H0
Ambika Cotton mills Limited -0.50 1.96 Accept H0(-)
Malwa cotton sponging mills Limited 2.57 1.96 Reject H0(+)
Surya Lakshmi cotton mills Limited -2.90 1.96 Reject H0(-)
From Table 5.17, it can be observed that Malwa cotton sponging mills Limited
and Surya Lakshmi cotton mills Limited have rejected the null hypothesis and a dif-
ference in total traded quantity could be noticed. Ambika Cotton mills Limited, have
accepted H0. Since the Z0donot vary widely from Zcritical, there is no significant
difference in total traded quantity of the cotton sector.
Table 5.18: FOODS
Company Name Z0Zcritical Accept/Reject H0
Agro Tech Foods Limited 0.06 1.96 Accept H0(+)
Heritage Foods Limited -4.52 1.96 Reject H0(-)
Kohinoor Foods Limited 4.89 1.96 Reject H0(+)
Sita Shree Food products Limited -1.74 1.96 Accept H0(-)
Vidhi Speciality food ingredients Limited -1.88 1.96 Accept H0(-)
From Table 5.18, it can be observed that Heritage Foods Limited and Kohinoor
Foods Limited have rejected the null hypothesis and a difference in total traded quantity
could be noticed. Agro Tech Foods Ltd, Sita Shree Food products Limited and Vidhi
Speciality food ingredients Limited, have accepted H0. Since the Z0donot vary widely
from Zcritical, there is no significant difference in total traded quantity of the foods
sector.
Table 5.19: PAPER
Company Name Z0Zcritical Accept/Reject H0
BalKrishna Paper mills Limited -3.19 1.96 Reject H0(-)
International Paper APPM Limited -2.72 1.96 Reject H0(-)
Rainbow Papers Limited 0.42 1.96 Accept H0(+)
Ruchira Papers Limited -2.09 1.96 Reject H0(-)
Star paper mills Limited -0.43 1.96 Accept H0(-)
From Table 5.19, it can be observed that BalKrishna Paper mills Limited, In-
ternational Paper APPM Limited and Ruchira Papers Limited have rejected the null
hypothesis and a decrease in total traded quantity could be noticed. Rainbow Papers
Limited and Star paper mills Limited, have accepted H0. Since the Z0donot vary
17
widely from Zcritical, there is no significant difference in total traded quantity of the
paper sector.
Table 5.20: REAL ESTATE
Company Name Z0Zcritical Accept/Reject H0
Indiabulls Real Estate Limited -1.26 1.96 Accept H0(-)
From Table 5.20, it can be observed that Indiabulls Real Estate Limited has
accepted the null hypothesis and led to a decrease in total traded quantity of the real
estate sector.
Table 5.21: RETAIL
Company Name Z0Zcritical Accept/Reject H0
Aditya Birla Fashion and Retail Limited -5.30 1.96 Reject H0(-)
Cantabil Retail India Limited -0.68 1.96 Accept H0(-)
V2 Retail Limited -3.54 1.96 Reject H0(-)
V-Mart Retail Limited 2.91 1.96 Reject H0(+)
From Table 5.21, it can be observed that Aditya Birla Fashion and Retail Lim-
ited, V-Mart Retail Limited and V2 Retail Limited have rejected the null hypothesis
and a difference in total traded quantity could be noticed. Cantabil Retail India Limited
have accepted H0. Since the Z0donot vary widely from Zcritical, there is no significant
difference in total traded quantity of the retail sector.
Table 5.22: RUBBER
Company Name Z0Zcritical Accept/Reject H0
Elgi Rubber Company Limited 2.13 1.96 Reject H0(+)
The Anandam Rubber Company Limited -0.92 1.96 Accept H0(-)
From Table 5.22, it can be observed that Elgi Rubber Company Ltd has rejected
the null hypothesis and showed an increase in total traded quantity. While The Anandam
Rubber Company Ltd has accepted H0and showed a decrease. Since the Z0do not vary
significantly from Zcritical, there is no significant difference in total traded quantity of
the rubber sector.
From Table 5.23, it can be observed that JSW Steel Limited, Kalyani Steel Lim-
ited and Tata Steel Limited have rejected the null hypothesis and a difference in total
traded quantity could be noticed. Jindal Steel Limited and Lloyds Steel Industries Lim-
ited have accepted H0. Since the Z0donot vary widely from Zcritical, there is no
significant difference in total traded quantity of the steel sector.
18
Table 5.23: STEEL
Company Name Z0Zcritical Accept/Reject H0
Jindal Steel Limited 0.17 1.96 Accept H0(+)
JSW Steel Limited 7.11 1.96 Reject H0(+)
Kalyani Steel Limited -3.67 1.96 Reject H0(-)
Lloyds Steel Industries Limited -1.32 1.96 Accept H0(-)
Tata Steel Limited -4.62 1.96 Reject H0(-)
Table 5.24: SUGAR
Company Name Z0Zcritical Accept/Reject H0
Bajaj Hindusthan Sugar Limited 3.72 1.96 Reject H0(+)
Dalmia Bharat sugar and industries Limited -1.57 1.96 l Accept H0(-)
Dhampura Sugar mills Limited -2.68 1.96 Reject H0(-)
Sakthi Sugars Limited -2.07 1.96 Reject H0(-)
Shree Renuka sugars Limited -3.41 1.96 Reject H0(-)
From Table 5.24, it can be observed that Bajaj Hindusthan Sugar Limited, Dham-
pura Sugar mills Limited, Sakthi Sugars Limited and Shree Renuka sugars Limited have
rejected the null hypothesis and a difference in total traded quantity could be noticed
while Dalmia Bharat sugar and industries Limited have accepted H0. Since the Z0donot
vary widely from Zcritical, there is no significant difference in total traded quantity of
the sugar sector.
Table 5.25: TEA
Company Name Z0Zcritical Accept/Reject H0
Dhanusri Tea and industries Limited -1.46 1.96 Accept H0(-)
Jayshree Tea and Industries Limited -2.81 1.96 Reject H0(-)
PK Tea and Produce Company Limited -0.05 1.96 Accept H0(-)
The Grob Tea company Limited -0.73 1.96 Accept H0(-)
The United Nilgiri Tea Estate Company Limited 2.50 1.96 Reject H0(+)
From Table 5.25, it can be observed that Jayshree Tea and Industries Limited,
The United Nilgiri Tea Estate Company Limited have rejected the null hypothesis and a
difference in total traded quantity could be noticed while The Grob Tea company Lim-
ited, PK Tea and Produce Company Limited and Dhanusri Tea and Industries Limited
have accepted H0. Since the Z0donot vary widely from Zcritical, there is no significant
difference in total traded quantity of the tea sector.
From Table 5.26, it can be observed that Morarjee Textiles Limited and Sutlej
Textiles and Industries Limited have rejected the null hypothesis and a decrease in total
traded quantity could be noticed while Arrow Textiles Limited, GTN Textiles Limited
19
Table 5.26: TEXTILES
Company Name Z0Zcritical Accept/Reject H0
Arrow Textiles Limited 0.59 1.96 Accept H0(+)
GTN Textiles Limited -1.83 1.96 Accept H0(-)
Lambodhara Textiles Limited -0.88 1.96 Accept H0(-)
Morarjee Textiles Limited -2.24 1.96 Reject H0(-)
Sutlej Textiles and Industries Limited -2.51 1.96 Reject H0(-)
and Lambodhara Textiles Limited have accepted H0. Since the Z0donot vary widely
from Zcritical, there is no significant difference in total traded quantity of the textiles
sector.
5.3 Results on Total Trades
Total trades represent the direct quantitative result of demand and supply in the
economy after the demonetisation. The following tables represent the values for Z0for
each sector.
Table 5.27: AUTOMOTIVE
Company Name Z0Zcritical Accept/Reject H0
Bajaj-Auto Limited -0.28 1.96 Accept H0(-)
Mahindra CIE Automotive Limited -3.59 1.96 Reject H0(-)
Maruti Suzuki India Limited 0.51 1.96 Accept H0(+)
PPAP Automotives Limited -0.44 1.96 Accept H0(-)
SETCO Automotives Limited -5.39 1.96 Reject H0(-)
From Table 5.27, it can be observed that SETCO Automotives Limited and
Mahindra CIE Automotive Limited have rejected the null hypothesis and a decrease
in total trades could be noticed while Bajaj-Auto Limited, PPAP Automotives Limited
and Maruti Suzuki India Limited have accepted H0. Since the Z0donot vary widely
from Zcritical, there is no significant difference in total trades of the automotive sector.
From Table 5.28, it can be observed that Ramco Cements Limited and Ambuja
Cements Limited have rejected the null hypothesis and a difference in total trades could
be noticed while JK Cements Limited, Sagar Cements Limited and UltraTech Cements
Limited have accepted H0. Since the Z0donot vary widely from Zcritical, there is no
significant difference in total trades of the cement sector.
From Table 5.29, it can be observed that Indian card clothing company Limited
and VIP Clothing Limited have rejected the null hypothesis and a decrease in total trades
20
Table 5.28: CEMENT
Company Name Z0Zcritical Accept/Reject H0
Ambuja Cements Limited -2.70 1.96 Reject H0(-)
JK Cements Limited 0.90 1.96 Accept H0(+)
Ramco Cements Limited 2.56 1.96 Reject H0(+)
Sagar Cements Limited -0.17 1.96 Accept H0(-)
UltraTech Cements Limited 1.65 1.96 Accept H0(+)
Table 5.29: CLOTHING
Company Name Z0Zcritical Accept/Reject H0
Indian card clothing company Limited -3.44 1.96 Reject H0(-)
Kewal Kiran Clothing Limited 0.65 1.96 Accept H0(+)
VIP Clothing Limited -5.85 1.96 Reject H0(-)
Zodiac Clothing Company Limited -1.51 1.96 Accept H0(-)
could be noticed while Zodiac Clothing Company Limited and Kewal Kiran Clothing
Limited have accepted H0. Since the Z0donot vary widely from Zcritical, there is no
significant difference in total trades of the clothing sector.
Table 5.30: COTTON
Company Name Z0Zcritical Accept/Reject H0
Ambika Cotton mills Limited 0.40 1.96 Accept H0(+)
Malwa cotton sponging mills Limited 2.75 1.96 Reject H0(+)
Surya Lakshmi cotton mills Limited -3.39 1.96 Reject H0(-)
From Table 5.30, it can be observed that Malwa cotton sponging mills Limited
and Surya Lakshmi cotton mills have rejected the null hypothesis and a difference in
total trades could be noticed while Ambika Cotton mills Limited has accepted H0. Since
the Z0donot vary widely from Zcritical, there is no significant difference in total trades
of the cotton sector.
Table 5.31: FOODS
Company Name Z0Zcritical Accept/Reject H0
Agro Tech Foods Limited 0.01 1.96 Accept H0(+)
Heritage Foods Limited -3.47 1.96 Reject H0(-)
Kohinoor Foods Limited 4.73 1.96 Reject H0(+)
Sita Shree Food products Limited -2.20 1.96 Reject H0(-)
Vidhi Speciality food ingredients Limited -2.01 1.96 Reject H0(-)
From Table 5.31, it can be observed that Heritage Foods Limited, Kohinoor
Foods Limited, Sita Shree Food products Limited and Vidhi Speciality food ingredients
21
Limited have rejected the null hypothesis and a difference in total trades could be no-
ticed while Agro Tech Foods Limited have accepted H0. Since the Z0donot vary widely
from Zcritical, there is no significant difference in total trades of the foods sector.
Table 5.32: PAPER
Company Name Z0Zcritical Accept/Reject H0
BalKrishna Paper mills Limited -3.05 1.96 Reject H0(-)
International Paper APPM Limited -2.54 1.96 Reject H0(-)
Rainbow Papers Limited 1.32 1.96 Accept H0(+)
Ruchira Papers Limited -1.44 1.96 Accept H0(-)
Star paper mills Limited 2.57 1.96 Reject H0(+)
From Table 5.32, it can be observed that BalKrishna Paper mills Limited, In-
ternational Paper APPM Limited and Star paper mills Limited have rejected the null
hypothesis and a difference in total trades could be noticed while Rainbow Papers Lim-
ited and Ruchira Papers Limited have accepted H0. Since the Z0donot vary widely
from Zcritical, there is no significant difference in total trades of the paper sector.
Table 5.33: REAL ESTATE
Company Name Z0Zcritical Accept/Reject H0
Indiabulls Real Estate Limited -2.91 1.96 Reject H0(-)
From Table 5.33, it can be observed that Indiabulls Real Estate Limited have
rejected the null hypothesis and a decrease in total trades could be noticed. Thus there
is a significant difference in total trades of the real estate sector
Table 5.34: RETAIL
Company Name Z0Zcritical Accept/Reject H0
Aditya Birla Fashion and Retail Limited -5.51 1.96 Reject H0(-)
Cantabil Retail India Limited -0.89 1.96 Accept H0(-)
V2 Retail Limited -2.20 1.96 Reject H0(-)
V-Mart Retail Limited 3.01 1.96 Reject H0(+)
From Table 5.34, it can be observed that Aditya Birla Fashion and Retail Lim-
ited, V-Mart Retail Limited and V2 Retail Limited have rejected the null hypothesis
and a difference in total trades could be noticed. Cantabil Retail India Limited have
accepted H0. Since the Z0donot vary widely from Zcritical, there is no significant
difference in total trades of the retail sector.
From Table 5.35, it can be observed that Elgi Rubber Company Ltd has rejected
the null hypothesis and showed an increase in total trades. While The Anandam Rubber
22
Table 5.35: RUBBER
Company Name Z0Zcritical Accept/Reject H0
Elgi Rubber Company Limited 2.63 1.96 Reject H0(+)
The Anandam Rubber Company Limited -0.95 1.96 Accept H0(-)
Company Ltd has accepted H0and showed a decrease. Since the Z0do not vary sig-
nificantly from Zcritical, there is no significant difference in total trades of the rubber
sector.
Table 5.36: STEEL
Company Name Z0Zcritical Accept/Reject H0
Jindal Steel Limited 0.68 1.96 Accept H0(+)
JSW Steel Limited -1.12 1.96 Accept H0(-)
Kalyani Steel Limited -3.48 1.96 Reject H0(-)
Lloyds Steel Industries Limited 2.14 1.96 Reject H0(+)
Tata Steel Limited -2.38 1.96 Reject H0(-)
From Table 5.36, it can be observed that Lloyds Steel Industries Limited, Kalyani
Steel Limited and Tata Steel Limited have rejected the null hypothesis and a difference
in total trades could be noticed. Jindal Steel Limited and JSW Steel Limited have
accepted H0. Since the Z0donot vary widely from Zcritical, there is no significant
difference in total trades of the steel sector.
Table 5.37: SUGAR
Company Name Z0Zcritical Accept/Reject H0
Bajaj Hindusthan Sugar Limited 1.71 1.96 Accept H0(+)
Dalmia Bharat sugar and industries Limited -0.45 1.96 Accept H0(-)
Dhampura Sugar mills Limited -1.01 1.96 Accept H0(-)
Sakthi Sugars Limited -2.55 1.96 Reject H0(-)
Shree Renuka sugars Limited -3.75 1.96 Reject H0(-)
From Table 5.37, it can be observed that Sakthi Sugars Limited and Shree Renuka
sugars Limited have rejected the null hypothesis and a decrease in total trades could be
noticed while Dalmia Bharat sugar and industries Limited, Bajaj Hindusthan Sugar
Limited and Dhampura Sugar mills Limited have accepted H0. Since the Z0donot vary
widely from Zcritical, there is no significant difference in total trades of the sugar sector.
From Table 5.38, it can be observed that Jayshree Tea and Industries Limited
have rejected the null hypothesis and a decrease in total trades could be noticed while,
The United Nilgiri Tea Estate Company Limited, The Grob Tea company Limited, PK
23
Table 5.38: TEA
Company Name Z0Zcritical Accept/Reject H0
Dhanusri Tea and industries Limited -1.53 1.96 Accept H0(-)
Jayshree Tea and Industries Limited -2.85 1.96 Reject H0(-)
PK Tea and Produce Company Limited 0.38 1.96 Accept H0(+)
The Grob Tea company Limited -1.08 1.96 Accept H0(-)
The United Nilgiri Tea Estate Company Limited 1.70 1.96 Accept H0(+)
Tea and Produce Company Limited and Dhanusri Tea and Industries Limited have ac-
cepted H0. Since the Z0donot vary widely from Zcritical, there is no significant differ-
ence in total trades of the tea sector.
Table 5.39: TEXTILES
Company Name Z0Zcritical Accept/Reject H0
Arrow Textiles Limited 0.69 1.96 Accept H0(+)
GTN Textiles Limited -1.84 1.96 Accept H0(-)
Lambodhara Textiles Limited -1.63 1.96 Accept H0(-)
Morarjee Textiles Limited -2.26 1.96 Reject H0(-)
Sutlej Textiles and Industries Limited -2.09 1.96 Reject H0(-)
From Table 5.39, it can be observed that Morarjee Textiles Limited and Sutlej
Textiles and Industries Limited have rejected the null hypothesis and a decrease in to-
tal trades could be noticed while Arrow Textiles Limited, GTN Textiles Limited and
Lambodhara Textiles Limited have accepted H0. Since the Z0donot vary widely from
Zcritical, there is no significant difference in total trades of the textiles sector.
24
Chapter 6
Analysis of the results
55 industries from 13 different sectors had been analyzed to find the change in
average price, total traded quantity and total trades after demonetization. The results
are summarized below.
6.1 Analysis of companies
Number of companies analysed = 54
6.1.1 Effect on average price
Companies which showed a difference in trades due to a difference in average
price [when average price and total trades reject H0] = 24 = 44.44%
Companies which showed an increase in average price after demonetization [Z0
= +ve value] = 27 = 50%
•Reject H0= 20 = 37.04%
•Accept H0= 7 = 12.70%
6.1.2 Effect on total traded quantity
Companies which showed a difference in trades due to a difference in total traded
quantity [when average price and total trades reject H0] = 22 = 40.74%
Companies which showed an increase in total traded quantity after demonetiza-
tion [Z0= +ve value] = 16 = 29.63%
•Reject H0= 9 = 16.67%
•Accept H0= 7 = 12.96%
6.1.3 Effect on total trades
Companies which showed a difference in trades after demonetization [Reject
H0] = 28 = 51.85%
Companies which showed no difference in trades after demonetization [Accept
H0] = 26 = 48.14%
Companies which showed an increase in trades after demonetization [Z0= +ve
value] = 19 = 35.18%
•Reject H0= 7 = 12.96%
•Accept H0= 12 = 22.22%
6.2 Analysis of sectors
13 sectors were analyzed to find the change in average price, total traded quantity
and total trades after demonetization. The results are summarized below:
•Automotive: The average price of the sector has increased after demonetization.
Since there is a decrease in total traded quantity, total trades of the sector has
decreased.
•Cement: The average price of the sector has decreased after demonetization.
Since there is an increase in total traded quantity, total trades of the sector has
improved.
•Clothing: Both the average price and total traded quantity of the sector has de-
creased after demonetization. Therefore, the total trades of the sector has consid-
erably decreased.
•Cotton: Both the average price and total traded quantity of the sector has in-
creased after demonetization. Therefore, the total trades of the sector has im-
proved.
•Foods: Both the average price and total traded quantity of the sector has de-
creased after demonetization. Therefore, the total trades of the sector has consid-
erably decreased.
•Paper: The average price of the sector has increased after demonetization. Since
there is a decrease in total traded quantity, total trades of the sector has decreased.
26
•Real estate: Both the average price and total traded quantity of the sector has
decreased after demonetization. Therefore, the total trades of the sector has con-
siderably decreased.
•Retail: Both the average price and total traded quantity of the sector has de-
creased after demonetization. Therefore, the total trades of the sector has consid-
erably decreased.
•Rubber: Both the average price and total traded quantity of the sector has in-
creased after demonetization. Therefore, the total trades of the sector has im-
proved.
•Steel: The average price of the sector has increased after demonetization. Since
there is a decrease in total traded quantity, total trades of the sector has decreased.
•Sugar: Both the average price and total traded quantity of the sector has de-
creased after demonetization. Therefore, the total trades of the sector has consid-
erably decreased.
•Tea: Both the average price and total traded quantity of the sector has decreased
after demonetization. Therefore, the total trades of the sector has considerably
decreased.
•Textiles: Both the average price and total traded quantity of the sector has de-
creased after demonetization. Therefore, the total trades of the sector has consid-
erably decreased.
27
28
Chapter 7
Conclusions
The demonetisation undertaken by the government is a large shock to the econ-
omy. The impact of the shock in the medium term is a function of how much of the
currency will be replaced at the end of the replacement process and the extent to which
currency in circulation is extinguished. By analysing trades data of various sectors, it is
found that the impact of demonetization varies from one sector to another.
From the analysis of results, following conclusions are made.
Average price
•About 44.44% of total companies analyzed, showed a difference in average price
after demonetization, when average price and total trades showed a significant
difference.
•50% of the companies showed an increase in average price after demonetization
and 37.04% of the total companies had a significant increase in average price.
•From the sector-wise analysis, automotive, cotton, paper, rubber and steel showed
an increase in average price while cement, clothing, foods, real estate, retail,
sugar, tea and textiles showed a decrease after demonetization.
Total Traded Quantity
•About 40.74% of total companies analyzed, showed a difference in total traded
quantity after demonetization, when average price and total trades showed a sig-
nificant difference.
•29.63% of the companies showed an increase in total traded quantity after de-
monetization and 16.67% of the total companies had a significant increase in
total traded quantity.
•From the sector wise analysis, cement, cotton and rubber showed an increase in
total traded quantity while automotive, clothing, foods, paper, real estate, retail,
steel, sugar, tea and textiles showed a decrease after demonetization.
Total Trades
•About 51.85% of total companies analyzed, showed a difference in total trades
after demonetization.
•35.18% of the companies showed an increase in total trades after demonetization
and 12.96% of the total companies had a significant increase in total trades.
•From the sector wise analysis, cement, cotton and rubber showed an increase
in total trades while automotive, clothing, foods, paper, real estate, retail, steel,
sugar, tea and textiles showed a decrease after demonetization.
30
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