Amazon's Market Efficiency & Trading Strategies During Covid-19

This case study assesses the efficient market hypothesis (EMH) in the context of Amazon's stock performance during the COVID-19 pandemic. It examines the relevance of EMH in a volatile setting, conducts event analysis to understand the impact of the pandemic on Amazon's stock prices, and explores trading strategies for exploiting price inefficiencies. The report considers different forms of market efficiency (hard, semi-strong, and weak) and analyzes the correlation between market prices and Amazon's price index. It further discusses the challenges in detecting value discrepancies, the importance of understanding a company's intrinsic value, and the advantages individual investors have over institutions. The analysis includes the impact of incorrect data, market mispricing, and the significance of strategic inaction, providing a comprehensive overview of Amazon's market behavior during the global crisis. Desklib provides solved assignments for students.

ASSESSMENT

Executive Summary
The efficient market hypothesis (EMH) states that it is difficult to exit or win the market because
stocks and stocks are always valued at a reasonable value. Reasonable market-determined value
binds everyone who is freely accessible to an organization in its share cost. As a financial backer,
you should decide whether to accept that the fertile return theory is valid. Investor’s choice of
potential market theory will have a major impact on how you should move towards contributing.
This report has brought a contextual look at Amazon.
Amazon outperformed Berkshire Hathaway in a market worthy of becoming the fifth largest
listing company in the United States. Amazon currently has a market potential of around $ 356
billion while Berkshire Hathaway has a market value of $ 355 billion. To our encouragement, we
can say that the market has decided that Amazon and Berkshire Hathaway are basically the same
as Amazon by a small margin. Let us consider these two groups in more detail.

Table of Contents
Executive Summary.........................................................................................................................2
Introduction......................................................................................................................................4
1. Efficient market Hypothesis (EMH) and its relevance in a highly volatile setting such as a
global pandemic...............................................................................................................................4
2. Event Analysis.............................................................................................................................6
3. Trading strategies and instruments that would be appropriate for exploiting price
efficiency/inefficiency during the global pandemic........................................................................7
Conclusion.......................................................................................................................................9
References......................................................................................................................................10
Appendices....................................................................................................................................11

Introduction
Efficient Market Profitability (EMH) is a money and business concept that seeks to clarify how
sectors of the monetary industry move. It was created by financial analyst Eugene Fama in the
1960s, who claimed that the costs of each defense are completely reasonable and reflect the
natural value of the assets at random.
EMH argues that the only volatile movements occur after unexpected news, but that once the
information is digested, the efficient market resumes. The following research report is based on
the analyses of EMH to test the volatility in the market due to Covid19. The selected company is
Amazon.
1. Efficient market Hypothesis (EMH) and its relevance in a
highly volatile setting such as a global pandemic
It is clear from the financial situation around the planet that COVID-19 is having a devastating
effect on the world economy. Countries have lifted the block chain or total barrier to the
reduction or disruption of currency exercises, causing the organizations to continue to cash in on
them. We can see from the news that currency exchanges have also fallen, which is a matter of
great concern as it reflects the currency states of a country. Different sectors and groups have a
major impact; this pandemic has a devastating effect on various sectors such as agriculture,
development, education, products, the travel industry, transport and oil and gas due to the spread
of financial years. However, areas such as wellbeing and online administration are thriving in the
midst of a pandemic. The aim of this review is to find out how the pandemic (COVID-19) is
affecting financial exchanges and to examine the level of impact power in different countries
according to when they became known (Rossi and Gunardi, 2018).
The meaning of a fruitful market in Beaver's words is "the efficiency of the market in relation to
a data object means that the costs go as if everyone understands that data". market efficiency is
divided into three structures: the hard structure, the semi-hard structure and the powerless
structure and each one ensures that data at different levels are appropriate in the cost of inventory
means that the amount you pay for stock or other security and the return you get when you are

put down according to your risk measure will not give a net worth of zero at the end of the day
without a means of success to beat the market reliably (Jovanovic, 2018).
1. Free data sharing for onlookers;
2. Zero exchange taxes during protected exchanges;
3. Healthy lenders.
Hard structure
The robust structure implies that all open and private data is captured in the cost of the shares,
which means that whether or not you were able to access the internal data, it would still not have
been possible to reach the currency exchange. In practice, it may seem gimmicky. There are,
always, ring times on Wall Street that use preferred data in various ways to get a margin on a
stock, such as obtaining previous income data when it should be delivered on time, prevention,
getting early results as an example of drugs . start-ups when they are still classified and private,
such data cookies can clearly make you money. In addition, lenders have at one time or another
received rings with the Securities and Exchange Commission (SEC) and is involved in
investigating such cases. Therefore, the robust structure seems misleading because it is still
worth trying to include the data even though most of this is undoubtedly illicit and inaccurate
(Hamid et al 2017).
Semi-rigid structure
The next structure is the semi-rigid structure where all the data is distributed in the value; The
information for this position refers to specific data on the annual reports, everything that has
been disseminated in the disclosure, everything that the organization selects and distributes is
accessible to all. This is the fact that commercial banking analysts are ineffectively searching for
business organizations looking for small chunks of data that could be captured in the cost of
shares. Overall, it is promising to see the market improve in terms of inventory analysis. Be that
as it may, from time to time make a reasonable or good estimate of the stock and it goes as you
need it and in that case it would seem that you have come a long way, in various cases you are
content with horrible decisions and it doesn't work (Singh, Babshetti and Shivaprasad, 2021).
So it is difficult to hit the market without having a method of executing it quickly. Today, high
repurchase buyers basically do this by placing orders that can expect the data enough to have any

news that it is a very advanced data cookie. is about an organization and quickly orders are
placed for an offer from that group which then pushes the cost to satisfy the new assumptions.
On the off chance that you can’t submit your request in a way until it arrives before those orders,
you can make money in that sense, but this is basically a running which is not really a valid
move (Tokić, Bolfek and Peša, 2018).
Result of correlation between Market price and Amazon’s price index
The correlations have been calculated for 40 days from the day the number of new cases of
pollution begins to rise. The links for the new contaminated cases and the stock exchange cost
tables for Amazon show a negative direct relationship in the main half and then the other half
appears. It was when Amazon started seeing this as a difficult problem and the underlying frenzy
affected safe exchanges. It is the second 50% of the ratio (0.58) and it shows that the stock
exchange is starting to rise regardless of the number of cases growing and not everything that has
been valued has been stuck at this level. Similarly to these lines, the same reports show that the
stock was initially affected by a series of new issues but for a long time the financial exchanges
were useless in terms of the number of contaminated cases. In this way, the exchange costs that
went down after that point were due to various factors and by taking a group on demand for time
it can well be said that the reduction was due to locking, thus reducing the money exercises
(Kang, Lee and Park, 2021).
2. Event Analysis
The opportunity being considered is to examine the effective hypothesis of the market to
examine the cost development of Amazon shares in the previous 40 days. Opportunity to
consider starting to profit from what a unique opportunity means for a company's value.
Profitability has changed that the value of the organization is translated into investment costs
reflecting an unusual return. Coupled with the notion that data is quickly captured at a cost, the
notion of weird return (or execution) is a key aspect of foot scan methods. How does a particular
event affect the value of an organization? We should be careful as when we sense a combination
of factors across the market and many other chances of success. To accurately measure the
impact of a particular event, we need to control these random parts. Determining the criteria used
or the model to measure typical results is therefore crucial for periodic analysis. The

experimental model can be described as follows: When the opportunity arises, market members
renew their credits causing a change in the volume of the company’s output (Delcey and Sergi,
2019).
Results
This study will test the semi-austerity level market profitability potential for the Palestine Stock
Exchange as a developing business sector through event analysis. The event authorizes the
required disclosure of registered eye groups. The results show that we can refute the invalid
theory that there is no difference between the normal result and the abnormal zero; this shows
that the Palestinian waste market is at a semi-rigid level and that the data is constantly reflected
in inventory costs. In this particular case, the authorization of the disclosure principles in the
LSE largely does not lead to an improvement in market behavior. So the market is still growing.
A number of different developments are expected to revive appropriate efforts on the eye; at the
internal market level or in the reserved and valid external climate. Finally, more attention should
be paid to considering LSE productivity at the powerless facility and the impact of different
timing.
3. Trading strategies and instruments that would be
appropriate for exploiting price efficiency/inefficiency
during the global pandemic
1. Incorrect data
The more security is introduced, the more difficult it is to detect differences in the value of
features according to the typical valuation types. They are inconsistent and very rare.
One should make sure to test one's skills as a financial candidate. Individuals working in the
money sector are relatively strong and energetic. It's very difficult to have an educational margin

over well-covered groups because countless people are twisting a problem like yours. Is it
possible to achieve a positive understanding by carefully examining annual reports and financial
information? Of course, however, it is inevitable that commission is much more difficult, and
there is no doubt that the product pays less compensation with big names as such shortcomings is
reported as data business becomes normal.
2. Costs generally do not reflect natural value
The suspicion behind each major study is that business sectors will ultimately see the
fundamental value of security. On the off chance that you run out of stock, you realize that there
is a difference in cost and value and that business segments make the mistake of lowering its
prices. If you are short of stocks, you are assuming that the market is delivering far more value
than the industry really deserves.
In fact, understanding how to value a company is the simplest contribution. Through careful
scrutiny and proper perseverance, it is not difficult to present yourself according to a company’s
value. The problem believes that the market is seeing what we believe is right.
The traditional way for a value lender is to find out which organizations are trading at intrinsic
value discount, and later trust that business segments will be revalued for what they are worth.
3. Try to do nothing when there is nothing to do
Above all, this is the biggest advantage of individual lenders over foundations. Foundations
thrive on action and should do something or nothing to legitimize their costs. Funds must be
fully invested, emotions must at least be conveyed, when smart goals cannot be set and, for the
most part, organizations in a group typically make a similar trade.
Individual lenders face none of these pressures and it is your most rewarding advantage. You can
sit still for uncertain times for the right tone to emerge before moving on and you should abuse
this advantage indefinitely.
The value of showing a model: let's say that the costs of the property are rising sharply, the
general opinion of the average lender is very optimistic and individuals are in a hurry to buy a
property "motivated by fear of paranoia for pass on its good chances. 'However, the perception of
profit reinforces undeniable levels of responsibility and the suspicion that interest rates remain

too uncertain and that housing costs continue to rise (or at least remain) as something that, a my
opinion, it is badly linked to an end (this is what the crisis of the subconscious in general was).
That way, this is where the same lender has a huge margin. Given the fact that I would rather not
invest in the building, I don't need it! Of course, the average person may feel the urge to do so
because each of his or her companions is getting rich by doing so, but this is not as good as
promising to do that (Loredana, 2019).
Conclusion
A number of financial experts and analysts have begun to accept that stock costs are at least
insignificant. Another type of business analyst has strengthened the mental and social
components of stock value determination, coming to assume that future stock costs are relatively
unrealistic based on designing stock value in the stock market, certainly passed as valuation
measures "moderate". Furthermore, there were at least a large number of these market analysts,
making the case undoubtedly more moot that the expected examples allow financial backers to
risk glut buying back rates of change.

References
Delcey, T. and Sergi, F., 2019. The Efficient Market Hypothesis and Rational Expectations. How
Did They Meet and Live (Happily?) Ever After.
Hamid, K., Suleman, M.T., Ali Shah, S.Z. and Imdad Akash, R.S., 2017. Testing the weak form
of efficient market hypothesis: Empirical evidence from Asia-Pacific markets. Available at SSRN
2912908.
Jovanovic, F., 2018. A comparison between qualitative and quantitative histories: the example of
the efficient market hypothesis. Journal of Economic Methodology, 25(4), pp.291-310.
Kang, H.J., Lee, S.G. and Park, S.Y., 2021. Information Efficiency in the Cryptocurrency
Market: The Efficient-Market Hypothesis. Journal of Computer Information Systems, pp.1-10.
Loredana, M.E., 2019. A CRITICAL THEORETICAL ANALYSIS ON THE IMPLICATIONS
OF EFFICIENT MARKET HYPOTHESIS (EMH). Annals of'Constantin Brancusi'University of
Targu-Jiu. Economy Series, (6).
Rossi, M. and Gunardi, A., 2018. Efficient market hypothesis and stock market anomalies:
Empirical evidence in four European countries. Journal of Applied Business Research
(JABR), 34(1), pp.183-192.
Singh, J.E., Babshetti, V. and Shivaprasad, H.N., 2021. Efficient Market Hypothesis to
Behavioral Finance: A Review of Rationality to Irrationality. Materials Today: Proceedings.
Tokić, S., Bolfek, B. and Peša, A.R., 2018. Testing efficient market hypothesis in developing
Eastern European countries. Investment Management & Financial Innovations, 15(2), p.281.

Appendices
Appendix 1: Correlation between Bond and Gold Prices

Appendix 2: Amazon historical performance
Appendix 3: Relation between Amazon and Market Price change