A STUDY ON KERALA FLOODS EFFECT ON STOCK MARKET - WITH REFERENCE TO DHANALAXMI BANK, SOUTH INDIAN BANK AND FEDERAL BANK
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ISSN: 2455-2631 © April 2021 IJSDR | Volume 6, Issue 4
A STUDY ON KERALA FLOODS EFFECT ON STOCK
MARKET - WITH REFERENCE TO DHANALAXMI
BANK, SOUTH INDIAN BANK AND FEDERAL BANK
1
Narra Avinash Reddy, 2Dr.C.Mallesha
Department of Business Management
Anurag University
Abstract: This project investigates the impact of nature events and disasters in Kerala on Kerala's stock market return. The
dataset used includes the daily return on prices and accumulations (including dividends and capitalisation changes) from
July 2019 to September 2019 and the full timing and duration of severe flooding during this period. A T-test model is used
to model the return sequences, and nature events and disasters are specified as explanatory exogenous variables. The results
show that natural disasters and disasters at market level do not have a significant impact on returns, however defined they
may be.
INTRODUCTION
Stock Market:
The stock market refers to the collection of markets and exchanges of the issue and trading of shares (shares of public companies),
bonds and other types of securities takes place, either through formal exchanges or on over-the-counter markets. Also known as the
stock market, the stock market is one of the most vital components of a market economy, as it gives companies access to capital in
exchange for giving investors like real estate lice.
How the Stock Market Work:
The stock market can be divided into two main sections: the primary market and the secondary market. The primary market is where
new issues are first sold through an initial public offering (IPO). Institutional investors generally buy most of these shares from
investment banks; the value of the company "going public" and the amount of the shares in the current issue determine the price of
the opening share of the IPO. All subsequent transactions will take place on the secondary market, where participants include both
institutional and individual investors. (A company uses the money raised during its IPO to grow, but once it trades, it does not
receive money from the buying and selling of its shares.).
THE EFFECT OF FLOODS ON THE STOCK MARKET
Natural disasters and their impact on commercial activities are one of the main risk factors that supply chain risk management
practitioners still find difficult to manage. Researchers in this field have thoroughly studied the impact of these unpredictable
incidents on supply chain performance, such as the impact of the Japanese earthquake and tsunami in 2011 on the electronics and
automotive industries worldwide. As a result, Toyota lost its first position as a global automaker in 2011 and handed the title to
General Motors. Despite this, research documents also discussed the effects of flooding in Thailand and U.S. hurricanes such as
Sandy, Katrina, etc. Therefore, this case study is the first attempt to highlight the impact of the severe flooding that occurred in
December 2015 in Chennai from a supply chain perspective.
Need for study:
This particular study will focus on the economic impact of the stock market floods, as while volatility and the relationship with
stock prices in developed financial markets have been well studied; little attention has been paid to an in-depth study of the volatility
ofIndia's emerging stock market.
Scope of the study:
This study examines the three major banks affected by the flood, including Dhanalaxmi Bank, South India Bank, the Federal Bank
and their stock market index from August 12, 2018 to September 12, 2018, a month.
Objectives of the study:
1. Examine the stock price developments of Dhanalaxmi Bank, the South Indian Bank and the Federal Bank before and after
the floods.
2. Research into the impact of the floods on Dhanalaxmi Bank's share price
3. Investigation into the impact of the floods on the south Indian bank's share price
4. Investigation into the impact of floods on the Federal Bank's share price
Research Methodology:
This study was highlighted on secondary data using descriptive statistical tools. The following variables were taken into account
for the study and applied different statistical tools according to the objectives.
IJSDR2104031 International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org 183
ISSN: 2455-2631 © April 2021 IJSDR | Volume 6, Issue 4
Data Collection:
The study only worked on secondary sources. Selected stock market index yields were studied during the 30-day period leading up
to the floods and 30 days after the floods.
Statistical tools and techniques:
A t-test is a type of referential metric used to determine whether there is a significant difference between the means of two groups,
which can be linked in certain attributes. It is mainly used when datasets, such as the dataset recorded 100 times as a result of the
coin rollover, would follow a normal distribution and potentially have unknown anomalies. A t-test is used as a hypothesis testing
tool, which can be used to test a hypothesis that applies to a population. A t-test examines t-metrics, t-distribution values, and
degrees of freedom to determine the likelihood of difference between two sets of data. To perform a test with three or more variables,
a variance analysis is required.
Study period:
The study period runs from 12 August 2018 to 12 September 2018, i.e. one month
Limitations of the study:
1. There is no range of primary data.
2. On the basis of the results of a limited study, a general picture of the entire banking sector shall be released.
3. Due to limited time.
REVIEW OF LITERATURE
Literature Review:
S. Raja Mohan, M. Muthukamu (2015) budget is a financial statement that shows that future economic activity is the most viewed
event in the country, it has an immediate impact on the behavior of the stock market in India, NIFTY and SENSEX are the two
main broader indices of the market that danco to the indexes of the budget speech by the finance minister on budget day. Depending
on the nature of the information, the oral sect indices behave too positively or negatively on the stock market. This study examined
the impact of union budgets on the behaviour of the 11 NHS sector indices based on movement before and after budget day, the
event window period was categorised as short, medium and long term sector indices, and for analysis the Wicoxon match pair test
was applied to measure the nature and extent of the budget impact.
Sergiyladokhin, (2009) Studied the "problem of financial market volatility forecasts." The article examines the accuracy of many
of the most popular methods used in predicting volatility: historical volatility models (including the exponential weighted moving
average), the implicit volatility model, and regressive, heteroskedastic car models.
Kumar (2006) assessed in an article entitled "Comparative performance of volatility forecasting models in Indian markets" the
comparative capacity of different models for predicting static and economic volatility in the context of Indian equity and forex
markets. Based on the non-sample forecasts and the number of measures assessed that classify a particular method as superior, it
concluded that it can be inferred that this will lead to an improvement in stock market volatility forecasts. As he concluded, his
conclusions contradicted the conclusions of brails ford and paff (1996) which found no superior single method, but the stock market
results were similar to those of akigray (1989), McMillan (2001), Anderson and Bollerslev (1998) and Anderson et al. (1999) on
the foreign exchange market.
Atra (2004) article titled "Stock return volatility patterns in India," Atra examines the variable time model of stock yield volatility.
It also examined the sudden changes in volatility and the possibility of a confluence of these sudden changes affecting major
economic and political events both nationally and globally. In addition, it examined the stock market cycles for changes in the size,
duration and volatility of the bull and the declining phases during the reporting period. Its analysis showed that the liberalisation of
the stock market or F11 in particular, does not have a direct impact on the volatility of stock market returns. There has been no
structural change in stock price volatility around a liberalisation event or, more importantly, around the break dates for F11 volatility
and purchases in India. The apparent link has generally been established between stock price volatility and F11's sudden withdrawal
or mass purchase, i.e. F11's volatile investment in the stock market did not appear to be true for India. At all stages, as defined by
their analysis of structural fractures, the period between 1991:05 and 1993:12 was the most volatile period, with the standard
deviation in equity returns being greater than that of other periods. The study also showed that, in general, during the reference
period, the bull phases are longer, the amplitude of the bull is higher, and volatility in the phases is also higher. It also concluded
that the gains during the expansions are longer than the losses incurred during the declining phases of the stock market cycle. The
bullish phase, compared to preliberalization, was more stable in the postliberal phase. The results of their analysis also showed that
stock market cycles have slowed in the recent past. Finally, the study showed that volatility decreased in the post-liberalisation
phase for the bullish and bearish phase of stock market cycles.
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INDUSTRY AND COMPANY PROFILE
INDUSTRY PROFILE:
According to the Reserve Bank of India (RBI), the Indian banking sector is sufficiently capitalised and well regulated. The financial
and economic conditions of the country are much better than any other country in the world. Studies on credit, market and liquidity
risks suggest that Indian banks are generally resilient and have weathered the global downturn well.
The Indian banking sector has recently witnessed the deployment of innovative banking models such as payments and small
financial banks. The new RBI measures could go a long way in restructuring the national banking sector.
India's digital payment system has evolved the most from 25 countries, with India's Immediate Payment Service (IMPS) being the
only system at level five of the Innovation Index for Faster Payments (FPII). *
Market size
The Indian banking system includes 18 public sector banks, 22 private sector banks, 46 foreign banks, 53 rural regional banks,
1,542 urban cooperative banks and 94,384 rural cooperative banks as of September 2019. In 2007-2019, deposits increased by a
CAGR of 11.11 per cent to US$1.86 trillion in 2019. As of February 2020, deposits Rs 132.35 lakh crore (US$1,893.77 billion).
Total equity financing for the microfinance sector increased by 42 years in 2018-19 to 14,206 rs crore (US$2.03 billion).
Investments/developments
The main investments and developments in the Indian banking sector are:
In February 2020, the Cabinet's Committee on Economic Affairs agreed to continue the process of recapitalisation of
regional rural banks (RRB)by providing minimum regulatory capital to RRB&a3;s one year after 2019-20, i.e. until 2020-21 for
RRB's unable to maintain a risk-weighted minimum capital asset ratio (CRAR) of 9% , in accordance with the legal standards
prescribed by the Reserve Bank of India.
In October 2019, the Ministry of Post launched the mobile banking service for all holders of postal savings accounts at cbs
headquarters (basic banking solutions).
Deposits under Pradhan Mantri Jan Dhan Yojana (PMJDY) were Rs 1.06 crore lakh (US$15.17 billion)
In October 2019, the government's e-Marketplace (GeM) signed a memorandum of understanding with Union Bank of
India to facilitate an unnumbered, paperless and transparent payment system for a range of services.
Transactions through the Unified Payments Interface (UPI) amounted to R$1.32 billion in February 2020, representing
2.21.995 reais (US$31.76 billion).
Government initiatives
According to the EU budget 2019-2020, the Government has proposed a fully automated GST discount module and an
electronic billing system that will eliminate the need for a separate electronic route account.
As part of the 2019-2020 budget, the government has proposed Rs 70,000 (US$10.2 billion) to the public bank.
The government proceeded smoothly to consolidate, reducing the number of public sector banks by eight.
Since September 2018, the Indian government of the Pradhan Mantri Jan Dhan Yojana (PMJDY) program has created an
open regime and added more incentives.
India's government plans to inject 42,000 rs crore (US$5.99 billion) into public sector banks by March 2019 and will inject
the next tranche of recapitalisation in mid-December 2018.
Performance
Here are the government's achievements:
As of March 31, 2019, the number of debit and credit cards issued was 925 million and 47 million, respectively.
According to RBI, India had registered foreign exchange reserves of approximately US$476.09 billion as of February 14,
2020
India ranks seventh among the seventh economies with a GDP of US$2.730 billion in 2018 and the economy is expected
to grow by 7.3% in 2018.
To improve infrastructure in villages, 204,000 point-of-sale terminals (PoS) have been sanctioned by the National Bank
for Agriculture and Rural Development's Fund for Financial Inclusion (NABARD).
The total number of bank accounts opened under Pradhan Mantri Jan Dhan Yojana (PMJDY) reached 373.4 million opened
accounts (as of August 2019)
COMPANY PROFILE:
The three selected banks are:
DHANALAKSHMI BANK
SOUTH INDIAN BANK
FEDERAL BANK
IJSDR2104031 International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org 185ISSN: 2455-2631 © April 2021 IJSDR | Volume 6, Issue 4
DHANALAKSHMI BANK:
Dhanalakshmi Bank Ltd was founded on November 14, 1927 in the city of Thrissur, Kerala with a capital of 11,000 and 7
employees. It became a commercial bank planned in 1977. Today, it has 280 branches and 398 ATMs across the state.
Dhanlaxmi Bank reported an operating profit of Rs.12.58 crore in the first quarter of 15-16 and posted a net loss of Rs.22.71 crore
in the same period. Basel Bank III CRAR was 30.06.15 9.20% compared with 9.06% in the same period last year. Net interest
income increased by 9.35%, from Rs.75.9 crore to Rs.83.07 crore on an annual basis. Nim rose 2.41% to 2.62% on a Y-o-Y basis.
Dhanlaxmi Bank has implemented Centralised Banking Solution (CBS) on the Flexcube platform in all its branches to expand
anywhere/ anytime / banking to its customers through multiple delivery channels. The bank has set up a data center in Bangalore to
keep the system up and running 24 hours a day. Thrissur also has a disaster response centre operational to respond to various
unforeseen circumstances.
Name change.
The bank changed its name on August 10, 2010 from Dhanalakshmi Bank to Dhanlaxmi Bank.
Partnerships
The bank is a custodian of NSDL (National Security Depository Limited) which offers Demat services through certain branches.
The Bank offers online transactions in combination with religare securities. It works with AGS Infotech to install ATMs. It offers
customers visa-brand debit and credit cards. It also provides insurance services through CANARA HSBC OBC LIFE as a
bancassurance partner.
SOUTH INDIAN BANK:
South Indian Bank Limited (SIB) (ESB:532218, NSE:SOUTHBANK) is a large private sector bank headquartered in Thrissur,
Kerala, India.South Indian Bank has 871 branches, 4 service offices, 53 ext.counters and 20 regional offices in more than 27 states
and 3 trade union areas in India. It has set up more than 1500 [2] ATMs and 91 bulk note acceptor/ATMs across India.
Step:
First private bank in India to open a currency vault in April 1992.
First private sector bank to open an INR branch in Jhumritalaiya in November 1992.
First private sector bank to set up an industrial financial arm in March 1993.
First private sector bank in Kerala to open a branch abroad in June 1993.
Kerala develops fully integrated internal automation software for affiliates.
First Kerala-based bank to implement the base banking system.
8th largest branch network among private sector banks in India.
FEDERAL BANK:
Federal Bank is a private, commercial bank planned in India, headquartered in Aluva, Kochi. The Bank also has its representative
offices abroad in Abu Dhabi and Dubai.
With a customer base of 10 million, including 1.5 million INR customers and an extensive network of money transfer partners
around the world, the Federal Bank claims to manage more than 15% of India's domestic remittances. The Bank has remittance
agreements with more than 110 banks/currency companies around the world. The Bank is also listed on the BSE, ESN and the
London Stock Exchange and has a branch in the first International Financial Services Centre (IFSC) in Gujarat.
Federal Bank Limited (formerly Travancore Federal Bank Limited) was established with a permitted capital of 5,000 rupees in
Nedumpuram, a location near Tiruvalla in downtown Travancore on 23/4/1931 under the Travancore Act. It began its auction
activities and other banking transactions related to agriculture and industry.
The name of the bank was named Federal Bank Limited on December 2, 1949, after completing the formalities of the Banking
Regulation Act, 1949.
DATA ANALYSIS AND INTERPRETATION
1) Investigate the evolution of the share price of the Dhanalaxmi bank's impact before and after the floods
2) Research into the impact of the floods on Dhanalaxmi Bank's share price
Dhanalaxmi Bank closing rate on NSE 10 days before and 10 days after floods
BEFORE NSE AFTER NSE
10 days before the floods 10 days after the floods
16.2 15.75
16.4 15.6
16.35 15.55
16.45 15.45
17.25 17
17.05 16.45
17.2 16.05
17.9 15.75
17.85 15.7
17.9 15.35
IJSDR2104031 International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org 186ISSN: 2455-2631 © April 2021 IJSDR | Volume 6, Issue 4
Chart 1: Graphical representation of Dhanalaxmi Bank's closing price at 10 days before and 10 days after the floods
Null hypothesis:
There is no significant difference in NSE Sensex closing prices 10 days before and 10 days after the floods, as the t stat value is
greater than 0.05 accept the null hypothesis and reject the alternative hypothesis
Alternative hypothesis:
There is a significant difference in the closing prices of NSE Sensex 10 days before and 10 days after the floods, as the t stat value
is greater than 0.05, so accept the alternative hypothesis and reject the null hypothesis
2. CLOSING PRICE ON NSE 20 DAYS BEFORE AND 20 DAYS AFTER
BEFORE NSE AFTER NSE
10 days before the floods 10 days after the floods
16.2 15.75
16.4 15.6
16.35 15.55
16.45 15.45
17.25 17
17.05 16.45
17.2 16.05
17.9 15.75
17.85 15.7
17.9 15.35
17.35 15.05
16.8 15.4
16.3 15.45
15.85 15.4
15.55 15.1
15.65 14.75
15.95 14.9
16 14.95
16.45 14.6
16.1 14.45
IJSDR2104031 International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org 187ISSN: 2455-2631 © April 2021 IJSDR | Volume 6, Issue 4
Chart 2: Graphical representation of Dhanalaxmi Bank's closing price on the NSE 20 days before and 20 days after the
floods
Null hypothesis:
There is no significant difference in NSE Sensex closing prices 20 days before and 20 days after the floods, as the t stat value is
greater than 0.05 accept the null hypothesis and reject the alternative hypothesis
Alternative hypothesis:
There is a significant difference in NSE Sensex closing prices 20 days before and 20 days after the floods, as the t stat value is
greater than 0.05 accept the alternative hypothesis and reject the null hypothesis
3. CLOSING PRICE ON THE NSE 30 DAYS BEFORE AND 30 DAYS AFTER THE FLOODS
BEFORE NSE AFTER NSE
30 days before the floods 30 days after the floods
16.2 15.75
16.4 15.6
16.35 15.55
16.45 15.45
17.25 17
17.05 16.45
17.2 16.05
17.9 15.75
17.85 15.7
17.9 15.35
17.35 15.05
16.8 15.4
16.3 15.45
15.85 15.4
15.55 15.1
15.65 14.75
15.95 14.9
16 14.95
16.45 14.6
16.1 14.45
16.7 14
16.95 14
IJSDR2104031 International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org 188ISSN: 2455-2631 © April 2021 IJSDR | Volume 6, Issue 4
Chart 3: Graphical representation of Dhanalaxmi Bank's closing price at 30 days before and 30 days after the floods
Null hypothesis:
There is no significant difference in the closing prices of NSE Sensex 30 days before and 30 days after the floods, as the t stat value
is greater than 0.05 accept the null hypothesis and reject the alternative hypothesis.
Alternative hypothesis:
There is a significant difference in the closing prices of NSE Sensex 30 days before and 30 days after the floods that the t stat value
exceeds 0.05 accept alternative hypothesis and reject the null hypothesis
SOUTH INDIAN BANK
1) Investigate the evolution of the share price of the bank's impact in southern India before and after the floods
2) Research into the impact of floods on bank share prices in southern India
3)
4. CLOSING PRICE ON THE NSE 10 DAYS BEFORE AND 10 DAYS AFTER THE FLOODS
BEFORE NSE AFTER NSE
10 days before the floods 10 days after the floods
17.65116 17.50407
17.99438 17.5531
17.70019 17.70019
17.70019 17.99438
17.89632 17.50407
17.79826 17.35698
17.84729 17.20988
17.50407 17.20988
17.89632 17.20988
17.99438 17.20988
IJSDR2104031 International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org 189ISSN: 2455-2631 © April 2021 IJSDR | Volume 6, Issue 4
Chart 4: Graphical representation of South India Bank's closing price on the NSS 10 days before and 10 days after the
floods
Null hypothesis:
There is no significant difference in the closing prices of NSE Sensex 10 days before and 10 days after the floods, as the t stat value
is greater than 0.05 accept the null hypothesis and reject the alternative hypothesis
Alternative hypothesis:
There is a significant difference in the closing prices of NSE Sensex 10 days before and 10 days after the floods that the t stat value
exceeds 0.05 rejects the alternative hypothesis and rejects the null hypothesis
5 CLOSING PRICE ON THE NSE 20 DAYS BEFORE AND 10 DAYS AFTER THE FLOODS
BEFORE NSE AFTER NSE
20 days before the floods 20 days after the floods
17.65116 17.50407
17.99438 17.5531
17.70019 17.70019
17.70019 17.99438
17.89632 17.50407
17.79826 17.35698
17.84729 17.20988
17.50407 17.20988
17.89632 17.20988
17.99438 17.20988
17.74923 17.40601
17.45504 17.20988
17.70019 17.20988
18.09244 17.20988
18.14147 16.42539
22.06395 16.52345
21.27946 16.52345
22.21105 16.57248
20.64205 16.18023
21.1814 15.73895
IJSDR2104031 International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org 190ISSN: 2455-2631 © April 2021 IJSDR | Volume 6, Issue 4 Chart 5: Graphical representation of South India Bank's closing price on the NSS 20 days before and 20 days after the floods Null hypothesis: There is no significant difference in NSE Sensex closing prices 20 days before and 20 days after the floods, as the t stat value is greater than 0.05 accept the null hypothesis and reject the alternative hypothesis Alternative hypothesis: There is a significant difference in the closing prices of NSE Sensex 20 days before and 20 days after the floods that the t stat value exceeds 0.05 rejects the alternative hypothesis and rejects the null hypothesis 6 CLOSING PRICE ON THE NSE 30 DAYS BEFORE AND 30 DAYS AFTER THE FLOODS BEFORE NSE AFTER NSE 30 days before the floods 30 days after the floods 17.65116 17.50407 17.99438 17.553101 17.70019 17.700193 17.70019 17.994381 17.89632 17.50407 17.79826 17.356977 17.84729 17.209883 17.50407 17.209883 17.89632 17.209883 17.99438 17.209883 17.74923 17.406008 17.45504 17.209883 17.70019 17.209883 18.09244 17.209883 18.14147 16.425388 22.06395 16.523449 21.27946 16.523449 22.21105 16.572479 20.64205 16.180233 21.1814 15.738953 21.76977 15.787985 IJSDR2104031 International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org 191
ISSN: 2455-2631 © April 2021 IJSDR | Volume 6, Issue 4
Chart 6: Graphical representation of south Indian Bank's closing price on the NSS 10 days before and 10 days after the
floods
Null hypothesis:
There is no significant difference in the closing prices of NSE Sensex 30 days before and 30 days after the floods, as the t stat value
is greater than 0.05 accept the null hypothesis and reject the alternative hypothesis
Alternative hypothesis:
There is a significant difference in the closing prices of NSE Sensex 30 days before and 30 days after the floods that the t stat value
exceeds 0.05 rejects the alternative hypothesis and rejects the null hypothesis
FEDERAL BANK
To research stock price trends of the Federal Bank's impact before and after the floods
Research into the impact of flooding on federal banks' share prices
7. CLOSING PRICE ON THE NSE 10 DAYS BEFORE AND 10 DAYS AFTER THE FLOODS
BEFORE NSE AFTER NSE
10 days before the floods 10 days after the floods
88.03507 86.505318
88.38051 87.640305
87.09748 88.528542
86.25858 85.123604
87.29487 80.435638
8611054 80.781067
86.11054 80.435638
85.46903 80.534332
86.83498 78.856537
88.10335 78.609795
IJSDR2104031 International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org 192ISSN: 2455-2631 © April 2021 IJSDR | Volume 6, Issue 4 Chart 7: Graphical representation of the Federal Bank's closing price on the NSS 10 days before and 10 days after the floods Zero hypothesis: There is no significant difference in the closing prices of NSE Sensex 10 days before and 10 days after the floods, as the t stat value is greater than 0.05 accept the null hypothesis and reject the alternative hypothesis Alternative hypothesis: There is no significant difference in the closing prices of NSE Sensex 10 days before and 10 days after the floods, as the t stat value is greater than 0.05 alternative hypothesis reject 8 CLOSING PRICE ON THE NSE 20 DAYS BEFORE AND 20 DAYS AFTER THE FLOODS 20 days before the floods 20 days after the floods 88.03507 86.505318 88.38051 87.640305 87.09748 88.528542 86.25858 85.123604 87.29487 80.435638 8611054 80.781067 86.11054 80.435638 85.46903 80.534332 86.83498 78.856537 88.10335 78.609795 88.59119 79.794121 86.10322 79.794121 84.78606 80.040855 85.5666 79.695435 85.71294 75.994408 83.66403 76.734612 83.7616 76.339836 83.85916 77.030693 86.15201 74.563347 Chart 8: Graphical representation of the Federal Bank's closing price on the NSS 20 days before and 20 days after the floods IJSDR2104031 International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org 193
ISSN: 2455-2631 © April 2021 IJSDR | Volume 6, Issue 4 Null hypothesis: There is no significant difference in NSE Sensex closing prices 20 days before and 20 days after the floods, as the t stat value is greater than 0.05 accept the null hypothesis and reject the alternative hypothesis Alternative hypothesis: There is a significant difference in the closing prices of NSE Sensex 20 days before and 20 days after the floods that the t stat value exceeds 0.05 rejects the alternative hypothesis and rejects the null hypothesis FINDINGS, SUGGESTIONS & CONCLUSION FINDINGS 1) The results of the corresponding t-test show that there is no significant difference in Dhanalaxmi NSE Bank 10 days before and 10 days after the floods 2) The results of the corresponding t-test show that there is no significant difference in Dhanalaxmi NSE Bank 20 days before and 20 days after the floods 3) The results of the corresponding t-test show that there is no significant difference in Dhanalaxmi NSE Bank 30 days before and 30 days after the floods 4) The results of the linked t-test show that there is no significant difference in the south Indian bank NSE 10 days before and 10 days after the floods 5) The results of the linked t-test show that there is no significant difference in the south Indian bank NSE 20 days before and 20 days after the floods 6) The results of the linked t-test show that there is no significant difference in the south Indian bank NSE 30 days before and 30 days after the floods 7) The results of the linked t-test show that there is no significant difference in the Federal Bank NSE 10 days before and 10 days after the floods 8) The results of the linked t-test show that there is no significant difference in the Federal Bank NSE 20 days before and 20 days after the floods 9) The results of the matched t-test show that there is no significant difference in the federal bank NSE 30 days before and 30 days after the floods SUGGESTIONS: 1. It is impossible to anticipate natural disasters such as cyclones and flash floods. However, disaster preparation plans and protocols during civil administration can be very useful for rescue and assistance and to reduce losses and negative effects on human life and socio-economic conditions. 2. Greater public awareness is needed to ensure an organised and calm approach to disaster relief. 3. Periodic simulated exercises and the exercise of disaster response protocols in the general population can be useful. CONCLUSION: The floods in Kerala have affected not only the region or those involved, but also the state economy. This project analyses the impact of damage caused by natural disasters in banks and commercial regions on the risk of bank failure. We note that disaster damage plays an important role in bank failure, in addition to the role of key banking functions available in bank financial reports. Moreover, we see that banks are no longer more likely to go bankrupt because of damage caused by a short-term disaster after a natural disaster, but in the medium term. Overall, our results indicate that bank management, as well as policymakers and regulators, should be more cautious about banks' exposure to areas where natural disasters have been severely damaged.As a result, natural disasters have the greatest negative impact on the capital market. IJSDR2104031 International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org 194
ISSN: 2455-2631 © April 2021 IJSDR | Volume 6, Issue 4 BIBLIOGRAPHY Articles: https://www.indiainfoline.com/article/general-blog/kerala-floods-impact-stocks-likely-to-be-most-impacted- 118082400542_1.html https://economictimes.indiatimes.com/markets/stocks/news/coffee-cardamom-and-rubber-crops-worth-rs-600-crore-lost-to- kerala-floods/articleshow/65437038.cms https://www.dsij.in/DSIJArticleDetail/ArtMID/10163/ArticleID/3486/Kerala-floods-and-its-impact-on-sectors Websites: https://www.southindianbank.com/ https://www.dhanbank.com/ https://www.federalbank.co.in/ Books: Ecological Devastation in the Western Ghats (City Plains) Pocket Book - Import, August 19,2019ByVijuB (Author) IJSDR2104031 International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org 195
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