Dissertation

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An Analysis of the Relationship between Stock Prices and Product
Announcements. Consumer electronic product announcements and
their impact on stock prices.
Name: Brian O’Sullivan
Supervisor: Dr. Ella Kavanagh.
Honours Dissertation – EC3144.

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Contents
1. List of Figures. .......................................................................................................................3
2. List of Tables. ........................................................................................................................4
3. Abstract ..................................................................................................................................5
4. Introduction............................................................................................................................6
5. Literature Review...................................................................................................................8
5.1. Previous Research. ..........................................................................................................8
5.2. Theories and Concepts. .................................................................................................12
6. Data and Data Analysis........................................................................................................13
7. Methods of Analysis. ...........................................................................................................18
8. Results and Conclusions. .....................................................................................................24
9. Appendix..............................................................................................................................34
10. References..........................................................................................................................39
11. Bibliography.......................................................................................................................41

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1. List of Figures.
Figure 1: Apple Stock Prices. ..................................................................................................17
Figure 2: Samsung Stock Prices. ............................................................................................17
Figure 3: Sony Stock Prices.....................................................................................................18
Figure 4: S&P500 Stock Prices................................................................................................18
Figure 5: Abnormal Returns for iPhone...................................................................................26
Figure 6: Cumulative Abnormal Returns for Apple 3Gs & iPhone 4......................................27
Figure 7: Samsung S3 vs S&P500 Abnormal Returns. ...........................................................28
Figure 8: Abnormal Return for Apple iPhone 4. .....................................................................29
Figure 9: Abnormal Return for Samsung S4. ..........................................................................29
Figure 10: Cumulative Abnormal Return Samsung S1 and S5. ..............................................29
Figure 11: Cumulative Abnormal Return Sony Z....................................................................31
Figure 12: Cumulative Abnormal Return Apple 3Gs and iPhone 4. .......................................37
Figure 13: Cumulative Abnormal Return Samsung S1 and S5. ..............................................37
Figure 14: Cumulative Abnormal Return Sony Z2..................................................................38

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2. List of Tables.
Table 1: Apple Product Announcement and Stock Returns ....................................................13
Table 2: Samsung Product Announcement and Stock Returns. ..............................................14
Table 3: Sony Product Announcement and Stock Returns. .....................................................15
Table 4: Summary Statistics for Stock Returns. ......................................................................15
Table 5: Abnormal Returns for Apple. ....................................................................................21
Table 6: Cumulative Abnormal Returns for Apple..................................................................22
Table 7: Abnormal Returns for Samsung. ...............................................................................22
Table 8: Cumulative Abnormal Returns for Samsung.............................................................23
Table 9: Abnormal Returns for Sony. ......................................................................................23
Table 10: Cumulative Abnormal Returns for Sony. ................................................................24
Table 11: Product Innovations. ................................................................................................35

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3. Abstract
Stock Market Efficiency has been a concept that has been argued since the 1965 and to this
day there is evidence to support weak form, semi-strong form and strong form efficiency, but
other concepts such as First-mover advantage and innovation is rarely taken into account.
This dissertation assesses and reviews the consumer electronic goods market with use of
concepts such as First-mover advantage and reviews the impact innovation has on this
emerging market. Existing literature has not looked at this new market and led to a clear gap
in the literature when it comes to the inner-mechanisms of this market.
Data relating to Apple, Samsung and Sony has been gathered in the form of Daily Returns.
After an estimation period has been established, the data is converted into Abnormal and
Cumulative Abnormal Returns for each of these firms around the time of a new product
announcement. To convert the data into the above, we used a regression analysis to get the
intercept term and the coefficient for an event study.
Results from the data analysis suggested that first-mover advantage begins to dissipate with
the introduction of competition. While first-mover advantage benefits a monopoly market, a
market as innovative as the consumer electronic goods market benefits more with an
oligopoly form of competition. The data also suggests that firms who innovate most
frequently see most consistent positive returns. Finally, the market tends to over-react and
fixes itself within a 2 day period, meaning that there is evidence of semi-strong form
efficiency.

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4. Introduction
This dissertation analyses the impact of product announcements on the stock returns of
consumer electronic companies over the period 2007 to 2015. We examine the impact on the
stock price of the leading companies directly before and after the announcement of the new
product. The specific market that will be examined in this dissertation is the electronic goods
market for mobile smartphones, and the companies investigated are Samsung, Apple, and
Sony, who are competitors within the market. We examine the effect of first-mover advantage
in this market with reference to Apple as the first-mover, Samsung as the second-mover and
Sony being the final entrant.
Although there is some literature surrounding product announcements and stock prices i.e.
Eddy & Saunders (1980), Pauwels et al (2004) and Markovitch & Steckel (2012), there is little
research done around this new market. With use of the law of diminishing returns, we expect
to see a decline in returns for the series of phones i.e. a decline in returns on the iPhone 3G in
comparison to the original iPhone.
The research question focusses on the consumer electronic goods market which has seen
continuous growth since its origins in 2007 with the announcement of Apple’s radical
innovative product, the iPhone. The consumer electronic goods market began slowly, but with
the introduction of competition both the market, and the firms within the market grew
exponentially. Greater competition in the market has increased firm innovation following
Schumpeter’s notion of creative destruction as new innovations replace older technologies.
Aghion & Howitt (1992) p323 came up with the idea that a “model of endogenous growth is

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developed in which vertical innovations, generated by a competitive research sector, constitute
the underlying source of growth”.
The question is important and relevant because, due to the electronic goods market being
relatively new, there is very little research done around the market. The research and results
also expand on how stock price movements are effected by new product announcements as a
test of market efficiency (Fama, 1965). The research question is also unique due to the fact that
it focusses on a single market while looking at the product successors, whereas earlier
literature, Chaney & Devinney (1992), Bernard & Thomas (1990) and Markovitch & Steckel
(2012), analyse multiple markets and only single products, and products without successors.
The research question examines the relationship between first-mover advantage and stock
returns in the consumer electronic goods market. The results found within the research is
beneficial to stock brokers and firms who are considering to become competitors within the
market, such as Huawei (Chinese telecommunications MNC), through providing a better
understanding of the market and the stock prices of firms. The research question also gives a
better insight into first-mover advantage and stock returns. The question is addressed through
the use of an event study, a method used extensively in finance and accounting since Brown
and Warner’s (1980, 1985) examination of event studies. The event study methodology is
outlined in the methods section.
The data is obtained from Yahoo Finance for the period 2007 to 2015, this is the beginning of
the Smartphone era, and Apple first launched their iPhone smartphone series which was highly
innovative, introducing the first smartphone without the need for a stylus or keyboard. We will
focus on daily stock prices for the companies outlined above. All companies stocks for this
dissertation are in US Dollars ($).

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The dissertation will flow as follows; Section 2 will include an in-depth examination of the
relevant literature around this topic by discussing previous research around stock market
reactions. We review previous literature such as Chaney & Devinney (1992), and Markovitch
& Steckel (2012) which allows the author to compare results, and to outline gaps in the
literature where the research question builds on the existing knowledge to fill in these gaps.
Section 3 will examine the theories and concepts underlying the author’s research question.
Chiefly, Fama’s Efficient Market Hypothesis and the link between product innovation and firm
growth Aghion & Howitt (1992). Section 4 analyses the data used within the research question
by providing the source of the data, we evaluate the graphs of stock prices produced by the
data, and provide a description of the data used. Section 5 will examine the methodology. The
author finishes the report with a detailed analysis of the results achieved, and concludes about
the findings in the context of the existing literature.
5. Literature Review.
5.1. Previous Research.
By examining previous empirical literature, it helps us to demonstrate how others have
researched and tested similar research questions in the past, be it for different markets or
different time frames. This market is still relatively new in comparison to other markets that
have been thoroughly researched, hence this research question attempts to fill in some of the
gaps surrounding this market. The author will be reviewing literature spanning across various
authors who researched and published their research using mainly time series data and event
study methodologies.
Studies done by Ettlie & Rubenstein (1987) surrounding innovation hypothesised that larger
firms were more capable of innovating. Their studies tested capabilities of firms to implement
radical innovation. Their findings indicated that for firms up to 1,000 employees, there is not a

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strong relationship between radical innovation and number of employees and between 1,200
and 11,000 employees there is a significant and direct relationship. Acs & Audretsch (1987)
considered firms with fewer than 500 employees to be ‘small’, and firms with 500 or more
employee to be ‘large’. Their findings showed that larger firms tend to be more involved in
process innovation whereas smaller firms are more involved in product innovation. These are
interesting results because by these concepts, we should see more highly innovative products
coming from smaller firms. This study was beneficial as it gave us a better understanding of
the relationship between innovation and firm size, however, the lack of a mid-size firm range
made the data more biased to larger firms. In this dissertation, Apple employ 115,000 people,
Samsung employ 489,000 people including their subsidiaries and Sony employ 131,700
people. Other literature found similar results, with Audretsch et al. (1988) finding larger firms
proving to be more innovative in a number of industries. Cohen (1995) found that the degree
of innovation is positively associated to large firm size. Innovation in this market could lead to
imitation of innovative ideas, so although it is new to the firm, it is not new to the market. There
being so few competitors within this market we expect to see some imitation, this is beneficial
as it creates “neck-and-neck” competition driving growth, due to firms wanting to escape
competition and enter a monopoly market. If more competitors were to be introduced to the
market it could reduce growth, an idea outlined by Aghion et al. 2001 through their concept of
an inverted U-shape model.

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Evidence shown by Poletti et al. (2008) provides information that shows that initial first mover
advantages begin to dissipate with the entry of competition. In this research question Apple
were the first movers, with Samsung being the secondary movers. Using a sample of 423
products over the period 1985 to 2004, Poletti et al.’s (2008) findings showed that first-movers
initially benefit economically but over time these advantages decrease. With technological
leadership being a first-mover advantage (Lieberman & Montgomery 1988), one would assume
it would be difficult for any competitors to gain traction. Their research data seems to show
where competition is presented, it is leading to more innovative designs which in turn are
leading to an increase in stock prices as long as they are capable of keeping up with the
innovative designs. This dissertation looks deeper into the idea of first mover advantage, giving
a better understanding of the consumer electronic goods market, and the effects of first mover
advantage.
Studies done by Markovitch and Steckel (2012) used a standard event study methodology to
examine the stock market’s immediate reactions to the introduction of new product
announcements and those product’s subsequent commercial performance in the US.
Markovitch and Steckel concluded that the market reacts ‘incorrectly’ either by over-reactions
or by under-reactions to new product announcements, this goes against the concept of an

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efficient market. This study reviewed 117 products from 1989 – 2000 and the commercial
performance of each product, using an estimation window of 200 days, and looked at different
markets, using products without successors. This research question fills in the gap in literature
surrounding product successors.
Previous literature surrounding the topic of the relationship of stock prices and product
innovation would include Eddy & Saunders (1980) who found that there was no evidence to
suggest that the stock market reacted positively to the announcements of products. . Woolridge
and Snow (1990) found a positive relationship between an announcement of corporate strategic
investment decisions and the stock market. Their data analysed 767 strategic investment
decisions announced by 248 companies in 102 industries. Chaney & Devinney (1992) also
found there that there was a .60i excess return for firms over the subsequent 3 days after
announcing a product or service innovation. This is closer to the conclusion that the author
came to, with the idea that there is a relationship between stock prices and announcements,
though they are more random than of a consistent positive nature.
Authors such as Pauwels et al. (2004) examined new product introductions for the automobile
market, a market that was well established at the time, they looked at “California dealerships,
containing every new car sales transaction of a sample of 1,100 dealerships from October 1996
through December 2001”. Their research investigates the short-term and long-term impact of
marketing actions on financial metrics including stock performance. In comparison to the
research question, Pauwels et al. (2004) examined their data through a multivariate time series
whereas this research question uses an event study and ordinary least squares regression
analysis of the daily stock returns. Their multivariate time series was used in tandem with a
refined Vector Auto-regressive model which too included changes to the construction index
and also the firm’s financial performance and future earnings forecasts. Their results showed
that the introduction of new product releases had a high correlation with increased long-term

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financial performance through positive changes to the firm’s stock value. Their research was
limited due to the focus solely on Californian dealerships and not dealerships from other areas.
As shown above, a lot of the relevant literature and studies done around this topic have been
done several years ago, and none looked at the more recent market. The research question also
builds on the knowledge base around first mover advantage, and the importance of innovation
in new markets.
5.2. Theoriesand Concepts.
There are a few key underlying theories and concepts to this research question beginning with
Eugene Fama’s (1965) Efficient Market Hypothesis which asserted that stock markets always
traded at their fair value, meaning that continuous excess returns cannot be obtained from
‘beating’ the market. The Efficient Market Hypothesis states that the market will discount new
information into the stock price and that the market reflects this information. This theory
outlined three different forms of market efficiency, Weak Form Efficiency where excess
returns cannot be earned in the long run by using investment strategies based on historical share
prices or other historical data. Semi-Strong form - which implies that share prices adjust to
publicly available new information very rapidly and in an unbiased fashion, such that no excess
returns can be earned by trading on that information. Strong Form – is where share prices reflect
all information at any given stage including product announcements and random shocks,
making it impossible for anyone to ever earn excess returns. Essentially testing the nature of
the relationship between stock prices and announcements, is a test of semi-strong form market
efficiency.

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The test for semi-strong form efficiency within this research question comes in the form of an
Event Study. In order to grasp a better understanding of event studies and their methodology
the author reviewed MacKinlay (1997) a study titled- Event Studies in Economics and Finance.
This was key to the understanding of event study methodology which measures the impact of
a specific event on a firm’s value. This paper gave a detailed explanation of how to apply and
analyse an event study in order to understand the impact of announcements of new product
developments on the stock price of relevant firms in the smart phone market.
The author also reviewed Aghion & Howitt (1992) for a lens into the understanding of
innovation. In their literature, Aghion & Howitt come to the conclusion that a “model of
endogenous growth is developed in which vertical innovations, generated by a competitive
research sector, constitute the underlying source of growth”. The author also looked at the
Schumpeterian concept of Creative Destruction. Creative destruction was first outlined by
Joseph Schumpeter in 1942 in his work “Capitalism, Socialism and Democracy”, the idea
outlines that when new innovation occurs it destroys older innovations. This idea gave the
author of the research question another view of the idea of innovation whereby new innovations
in the market should destroy older innovations.
Finally, the author reviewed the idea of first-mover advantage discussed by Poletti et al (2008).
This allowed the author to build a rationale behind why Apple’s initial success in the market
and then with the introduction of competition, the market saw a change in dynamic in how it
worked. The concept of first mover advantage allowed for the author to look at both the pros
and cons received by Apple for being first movers into this new market.
6. Data and Data Analysis.
Table 1: Apple Product Announcement and Stock Returns
Apple Product Announcement and Stock Returns. Table 1.
Sources: Yahoo Finance & Apple Website.

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Table 2: Samsung Product Announcement and Stock Returns.
Product Name. Announcement Date. Release Date. 6 Month Return. 12 Month Return.
iPhone 09/01/2007 29/06/2007 64% 39%
iPhone 3G 09/06/2008 11/07/2008 12% -23%
iPhone 3GS 08/06/2009 19/06/2009 40% 96%
iPhone 4 07/06/2010 24/06/2010 20% 21%
iPhone 4S 04/10/2011 14/10/2011 43% 50%
iPhone 5 12/09/2012 21/09/2012 -35% -32%
iPhone 5C + 5S 10/09/2013 20/09/2013 14% 49%
Samsung Product Announcement and Stock Returns. Table 2.
Sources: Yahoo Finance & Samsung Website.
Galaxy S1 23/03/2010 04/05/2010 20% 9%
Galaxy S2 13/02/2011 02/05/2011 6% 54%
Galaxy S3 03/03/2012 29/05/2012 6% 22%
Galaxy S4 14/03/2013 27/04/2013 1% -2%
Galaxy S5 24/02/2014 11/04/2014 -4% 5%
Sony Product Announcement and Stock Returns. Table 3.
Sources: Yahoo Finance & Sony Website.
Xperia Z 07/01/2013 09/02/2013 38% 16%
Xperia Z1 04/09/2013 04/09/2013 -14% -3%

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Table 3: Sony Product Announcement and Stock Returns.
Three strategic firms are analysed using the event study. These firms are Apple, Samsung, and
Sony. Shown in the tables above are the date of announcement and release date of each key
product with the 6 month and 12 month return on each product. For some devices such as the
Sony M4, return data was not available. All of these firms have been involved with developing
smartphones and other related technologies. Daily data of the stock price for each company is
retrieved from Yahoo Finance (The currency used is US Dollars). Daily data comprised of
Monday to Friday, as stock prices on Saturday and Sunday are not recorded, so when the author
refers to one week, this refers to a week of Monday - Friday. We used the opening price for
each firm and placed into an excel file where the data returns could be calculated and the
regressions for the event study could be run also. The data retrieved is valid to the research
question, and is reliable as Yahoo Finance is not associated with any of the companies,
therefore it is not biased towards the stock price of any of the firms. The S&P500 is used as the
index for the stock price comparison, in the market model to determine normal returns. The
Standard & Poor 500 (S&P500) index is an American Stock market index based on the market
capitalisations of 500 large companies which have their stocks traded on NASDAQ.
Table 4: Summary Statistics for Stock Returns.
Xperia Z2 24/02/2014 24/03/2014 -2% 56%
Xperia Z3 03/09/2014 19/09/2014 63% 54%
Xperia M2 19/08/2014 30/04/2014 5% 72%
Xperia M4 02/03/2015 25/06/2015 Not Available. Not Available.

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Table 1 shows the summary statistics for the stock returns for the 3 firms and the S&P index.
The table contains the mean stock price for each of the firms, along with the standard deviation
and the highest price the stock reached during the period 2007 – 2015. Volatility in finance can
be measured by the standard deviation, in this case by applying it to the daily rate of return.
High volatility tells us how much the stock return is deviating from the expected normal
returns. We can see that Apple is the most volatile with the highest standard deviation, followed
by Sony and then Samsung being closest to the S&P index. Towards the end of the period, after
2013, we begin to see high-volatility in Samsung’s stock price in comparison to the others.
Reviewing the graphs below, it is obvious that the S&P500 index is by far the least volatile.
We also notice that Apple has the highest max stock return over the period with Samsung
having the second highest. The figures below show us the change in stock prices over time.
From Figure 1, Apple’s stock price remained relatively stable until the introduction of Samsung
who entered the market with their original announcement in 2010, after this period we can see
how rapidly both Apple and Samsung grow in stock value. Both Apple and Samsung show
signs of positive growth whereas Sony who entered the market later than both other
competitors, struggles to lift the stock value.
Summary Statistics for Stock Returns (2007 – 2015.) Table 4.
Source: Author’s own calculations.
Apple Samsung Sony S&P 500
Mean Stock Return 0.093 0.055 0.127 0.028
Standard Deviation 2.89 1.915 2.281 1.26
Max Stock Return 13.9 13.72 13.41 11.58

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Figure 1: Apple Stock Prices.
Figure 2: Samsung Stock Prices.

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Figure 3: Sony Stock Prices.
Figure 4: S&P500 Stock Prices
7. Methods of Analysis.
Step 1: Defining the event and the length of the event window.
This dissertation uses the standard event study methodology as used by MacKinlay (1997). The
event study comprises of five steps. The first step is to define the event and the event window.
The event is the announcement of a new product or significant development of an existing
0
10
20
30
40
50
60
70
StockPriceUSD$
Year
Sony Stock Price(2005 - 2015) Figure3.
Source: Yahoo Finance.
0
500
1000
1500
2000
2500
StockPriceUSD($)
Year
S&P500 Stock Price(2005 - 2015) Figure4.
Source: Yahoo Finance

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product. The event window chosen is the period of 3 days prior to the event, and 5 days
following the event. The event window varies, with Chaney and Devinney (1992) using a
window of 3 days prior and 3 days after the event, whereas Markovitch and Steckel (2012) a
window of 3 days prior and 2 days after the event. Graphical analysis suggested that the event
had an impact on the stock market for up to 5 days after the event. Adding the extra 2 days
allows for more accurate reading of the impact of the event on the stock prices.
Step 2: Estimate the Expected Returns.
To estimate the normal returns, the market model was over a period of 120 days leading up to
the event. Equation (i) is the basic market model which is used to estimate the normal returns.
On the left R is the dependent variable, in this case the return on Apple stock, at time period t.
This regression determines the relationship between the returns of Apple and the returns of the
S&P500 stock market index for the period of the announcement, in other words, to determine
the normal relationship between the two.
Where:
𝛼 = 𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡 𝑇𝑒𝑟𝑚
𝛽 = 𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡
𝑅 𝑡( 𝐴𝑝𝑝𝑙𝑒) = 𝑅𝑒𝑡𝑢𝑟𝑛 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑝𝑒𝑟𝑖𝑜𝑑 𝑡 𝑜𝑓 𝐴𝑝𝑝𝑙𝑒 𝑠𝑡𝑜𝑐𝑘.(𝑖. 𝑒. 𝑅−3( 𝐴𝑝𝑝𝑙𝑒) 𝑖𝑠3 𝑑𝑎𝑦𝑠 𝑏𝑒𝑓𝑜𝑟𝑒 𝐴𝑝𝑝𝑙𝑒 𝑒𝑣𝑒𝑛𝑡)
𝑅 𝑡(𝑆&𝑃500) = 𝑅𝑒𝑡𝑢𝑟𝑛 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑝𝑒𝑟𝑖𝑜𝑑 𝑡 𝑓𝑜𝑟 𝑡ℎ𝑒 𝑆&𝑃500.
𝑅𝑡(𝑎𝑝𝑝𝑙𝑒) = 𝛼 + 𝛽𝑅𝑡(𝑆&𝑃) (i)

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Step 3: Retrieve 𝛼̂ 𝑎𝑛𝑑 𝛽̂ and calculate the normal returns.
After running the regression for all of our products over the estimation period (120 days before)
we retrieve 𝛼̂ and 𝛽̂. From each regression these values for 𝛼̂ 𝑎𝑛𝑑 𝛽̂ need to be inputted into
the equation, as shown in (ii)
Step 5: Calculate the Cumulative Abnormal Returns.
𝑅̂−3(𝑎𝑝𝑝𝑙𝑒) = 𝛼̂ + 𝛽̂ 𝑅−3(𝑆&𝑃)
The author uses the original Apple iPhone, 3 days prior to the event (Day-3) to show
an example of how the formula works. The values for α and β are inserted into
equation (ii) along with the return on the S&P Index, 3 days prior to the
announcement. (See equation (iii))
(ii)
𝑅̂−3(𝑎𝑝𝑝𝑙𝑒) = 0.170 + 0.444(−0.119)
Calculate the values of our Expected Return ( 𝑅̂ ) for Apple. This gives us equation
(iv).
(iii)
Step 4: Calculate the Abnormal Return.
Expected Return = 𝑅̂−3(𝑎𝑝𝑝𝑙𝑒) = 0.117𝑖
To calculate the Abnormal return, we subtract the Actual Return from the Expected
Return on the same day. Which gives us equation (v).
(iv)
𝐴𝑏𝑛𝑜𝑟𝑚𝑎𝑙 𝑅𝑒𝑡𝑢𝑟𝑛−3 = 𝑅−3(𝑎𝑝𝑝𝑙𝑒) − 𝑅̂−3(𝑎𝑝𝑝𝑙𝑒)
By inputting in the values into equation (vi) we can calculate the Abnormal Return
in a percentage format. Below is an example of the regression.
(v)
𝐴𝑏𝑛𝑜𝑟𝑚𝑎𝑙 𝑅𝑒𝑡𝑢𝑟𝑛 = 0.49 − 0.117 = 0.373𝑖
See Tables 2, 4 and 6 for Abnormal Returns.
(vi)

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The author used abnormal returns, which for this research question are defined as returns
generated by the stock over a period of time that is different from the expected rate of return.
The expected return is based off of the long run historical average of the 120 days previous to
the announcement. The abnormal returns were then transformed into Cumulative Abnormal
Returns for the event window. To do this the author gets the sum of each Abnormal Return by
successive addition going from Day-3 to Day+5. See tables 3, 5 and 7 for Cumulative
Abnormal Returns.
Abnormal Returns – Apple
Source: Yahoo Finance – Table 5.
iPhone iPhone 3G iPhone 3Gs iPhone 4 iPhone 4s iPhone 5 iPhone 5s
Day -3 0.373 0.004 0.626 0.836 -3.99 -2.22 0.472
Day -2 7.978 1.113 0.475 0.72 0.09 -0.735 1.101
Day -1 4.5913 -3.47 -0.844 -1.094 -2.44 1.059 -2.53
Day 0
(Announcement) 4.347 2.209 -1.438 -2.14 -0.01 0.42 -5.56
Day +1 -1.683 -1.03 -1.763 -3.94 -1.76 0.699 1.108
Day +2 -1.6256 -4.947 -1.057 -0.46 -1.6 1.14 -1.982
Day +3 2.273 -3.413 -2.53 1.107 2.339 0.223 -3.292
Day +4 -2.501 4.509 1.07 0.217 2.659 -0.267 1.048
Day +5 -6.315 8.655 0.954 -0.68 -0.522 -0.6184 1.69
Table 5: Abnormal Returns for Apple
Cumulative Abnormal Returns – Apple
iPhone iPhone 3G iPhone 3Gs iPhone 4 iPhone 4s iPhone 5 iPhone 5s
Day -3
0.373 0.004 0.626 0.836 -3.99 -2.22 0.472
Day -2
8.351 1.117 1.101 1.556 -3.9 -2.955 1.573
Day -1
12.9423 -2.353 0.257 0.462 -6.34 -1.896 -0.957

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Day 0
(Announcement)
17.2893 -0.144 -1.181 -1.678 -6.35 -1.476 -6.517
Day +1
15.6063 -1.174 -2.944 -5.618 -8.11 -0.777 -5.409
Day +2
13.9807 -6.121 -4.001 -6.078 -9.71 0.363 -7.391
Day +3
16.2537 -9.534 -6.531 -4.971 -7.371 0.586 -10.683
Day +4
13.7527 -5.025 -5.461 -4.754 -4.712 0.319 -9.635
Day +5
7.4377 3.63 -4.507 -5.434 -5.234 -0.2994 -7.945
Table 6: Cumulative Abnormal Returns for Apple.
Abnormal Returns – Samsung.
S1 S2 S3 S4 S5
Day -3 2.008 -0.309 -0.3888 0.368 -0.659
Day -2 0.783 -1.479 0.928 -0.34 3.385
Day -1 0.082 -2.47 1.72 1.286 0.17
Day 0 (Announcement) 0.411 -0.775 -1.809 0.21 0.413
Day +1 0.854 4.68 -0.9 -4.28 0.462
Day +2 0.156 -1.867 -0.47 -0.603 -0.047
Day +3 0.9784 -0.182 0.806 0.805 0.197
Day +4 -1.735 2.56 0.987 0.301 0.03
Day +5 1.9456 -0.46 1.756 -0.501 -1.28
Table 7: Abnormal Returns for Samsung.
Cumulative Abnormal Returns – Samsung.
S1 S2 S3 S4 S5
Day -3
2.008 -0.309 -0.3888 0.368 -0.659
Day -2
2.791 -1.788 0.5392 0.028 2.726
Day -1
2.873 -4.258 2.2592 1.314 2.896
Day 0 (Announcement)
3.284 -5.033 0.4502 1.524 3.309

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Day +1
4.138 -0.353 -0.4498 -2.756 3.771
Day +2
4.294 -2.22 -0.9198 -3.359 3.724
Day +3
5.2724 -2.402 -0.1138 -2.554 3.921
Day +4
3.5374 0.158 0.8732 -2.253 3.951
Day +5
5.483 -0.302 2.6292 -2.754 2.671
Table 8: Cumulative Abnormal Returns for Samsung.
Abnormal Returns – Sony.
Z Z1 Z2 Z3 M2 M4
Day -3 0.146 4.07 1.147 0.8581 1.508 1.313
Day -2 -3.54 -1.7476 0.164 1.4575 0.39 0.541
Day -1 -0.96 -0.432 1.027 1.532 1.73 -1.68
Day 0 (Announcement) -1.66 -0.686 0.308 -0.469 0.34 -0.95
Day +1 -0.033 1.308 0.759 -2.111 -0.495 0.244
Day +2 1.966 0.278 0.349 0.03887 -0.061 0.129
Day +3 0.3672 -0.185 -0.002 0.604 0.548 -2.427
Day +4 1.923 -1.0511 -0.767 1.196 -0.497 -1.586
Day +5 -0.503 3.26 -0.875 1.45 -0.085 -1.239
Table 9: Abnormal Returns for Sony.
Cumulative Abnormal Returns – Sony.
Z Z1 Z2 Z3 M2 M4
Day -3
0.146 4.07 1.147 0.8581 1.508 1.313
Day -2
-3.394 2.3224 1.311 2.3156 1.898 1.854
Day -1
-4.354 1.8904 2.338 3.8476 3.628 0.174
Day 0 (Announcement)
-6.014 1.2044 2.646 3.3786 3.968 -0.776

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Day +1
-6.047 2.5124 3.405 1.2676 3.473 -0.532
Day +2
-4.081 2.7904 3.754 1.30647 3.412 -0.403
Day +3
-3.7138 2.6054 3.752 1.91047 3.96 -2.83
Day +4
-1.7908 1.5543 2.985 3.10647 3.463 -4.416
Day +5
-2.2938 4.8143 2.11 4.55647 3.378 -5.655
Table 10: Cumulative Abnormal Returns for Sony.
8. Results and Conclusions.
Examining the tables of Cumulative Abnormal Returns for the three firms, we can see that first
mover-advantage is evident with Apple’s return on the first Apple iPhone being much higher
than any other entry to the market. We can see that the returns for the iPhone declined with the
decline in innovation, and declined even more rapid with the introduction of competition in the
form of innovative products from Samsung. Reviewing Samsung’s Cumulative Abnormal
Return in Table 8 we see that new to firm innovations have the highest return on stock price
with use of imitation of existing technology, evidence of this is with the S1 having a much
higher return than other successors. Third entrants to the market have even less positive returns
than first and secondary entrants. Examining the Cumulative Abnormal Returns seen above in
Table 10, we came to the conclusion that any new entrants to the market need to produce new
to the market technology in order to produce positive returns. Evidence of this is with Sony’s
release of waterproofed phones with the release of the Sony Z1 which could take better
photographs than any other smartphones at this time. This was also at a time when applications
and online web services such as Snapchat1 and Instagram2 became increasingly popular.
1 Snapchatis a smartphone application which allows for imagemessaging.
2 Instagramis an onlinemobilephoto & video sharingservice.

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Looking at Table 2 for Abnormal Returns for Apple we can see a jump in the stock price
leading up to the event as knowledge of the first iPhone leaked ahead of the announcement.
The biggest positive reaction for Apple is from their original product, comparing the returns of
the iPhone with other subsequent developments we can see that the biggest positive return came
from the initial iPhone announcement. The returns based on the stock market reaction to the
iPhone series begins to diminish immediately with the iPhone 3G getting about half the
abnormal return as the original iPhone, and decreasing into negative territory thereafter with
the release of the iPhone 3Gs.
With the iPhone series there is very little radical innovation (See Appendix for innovations),
with more incremental innovation being seen with an improvement in camera and processor,
there is a lack of any radical innovation within the iPhone series during the period of 2007 to
the announcement of the iPhone 5s in September 2013. The reason the iPhone 6 is left out of
the data is due to limitations on reliable sources for the time period.
Generally speaking the reaction to the announcement comes on the days before the
announcement, this is followed by the stock market price changing quickly depending on if it
had over or under-reacted. By looking at the graph for the original iPhone (Figure 5), we can
see that the market over-reacts before the announcement and spikes upwards, then directly after
the announcement begins to decline. However, by day 5, the cumulative abnormal return for
the iPhone was 7.4377i. Looking at the cumulative abnormal returns to iPhones, again, we see
diminishing returns on successor products with the original iPhone having a much higher level
of return than the next, and with the latest iPhone in the data having an overall negative return.
The data shows how the market is actually rather inefficient in its information guessing, often
over-reacting or under-reacting to announcements, even within the iPhone series we see that
the market over-reacted to the original iPhone and under-reacted to the iPhone 4. It is evident
that the market under-reacted to the iPhone 4 by the way that directly before the event the share

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price plummeted to -3, and then the market compensates for this fall in the days afterwards,
trying to bring the stock price back to a steady level, but having such an under-reaction meant
that the announcement had a severely negative impact on stock price, this is evident in the
Cumulative Abnormal Return Table for Apple (Table 6).
Figure 5: Abnormal Returns for iPhone.
With the Cumulative abnormal returns for the iPhone 3Gs and iPhone 4, we can see from Figure
6 below that there is a slight pattern in how the market under-reacts to the product, reaching a
low about 2/3 days after the event and then rising back up again. We also see that the iPhone 5
had a negative reaction in stock returns, this is due to lack of innovation in Apple’s products,
and also the release of the Samsung S3, which introduced more competition to Apple. Due to
lack of patents on Apple’s ideas, Samsung were able to enter the market soon after through use
of initial imitation. This allowed for Samsung to free-ride on Apple’s R&D investments. Where
Apple stock prices suddenly plummet in 2012, this is down to the fact that the iPhone 5’s initial
sales were down in comparison to predecessors and also rumours leaked early in the year about
a product that never launched, this drove down investor confidence. On the other hand,
Samsung began to do really well around this time, this was down to the fact that Apple had
-8
-6
-4
-2
0
2
4
6
8
10
AbnormalReturn
Event Window.
AbnormalReturn for Apple iPhone.
Figure5.

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taken a turn for the worse, and also Samsung began to launch newly innovative products to
their firm.
Figure 6: Cumulative Abnormal Returns for Apple 3Gs & iPhone 4.
The results for Samsung show lesser returns than Apple, which suggests first-mover advantage.
Although by looking at the Cumulative Abnormal Returns Table for Samsung (Table 8), we
see that there was still a higher return for the S1 than any of the other product releases. The S2
performed poorly in comparison to other models that Samsung released, this is due to similar
reasons to the iPhone 5, whereby Samsung failed to highly innovate their products as they were
still trying to catch up on Apple. The S3 is where Samsung released a product which is
innovative and new to their firm, showing investors that Samsung are capable of innovating.
This is evident in the data by looking at Table 8. As shown by Figure 7, we can see the market
over-reacted to the announcement of the Samsung S3, the reaction came 2 days before the
announcement of the product, with the stock price falling below the expected on the day of the
announcement, and this is using the S&P500 index as a benchmark for the reaction. This
reaction is evidence of semi-strong form market efficiency.
-12
-10
-8
-6
-4
-2
0
2
CumulativeAbn.Ret.
Event Window.
Cumulative AbnormalReturn Apple 3Gs and 4
Figure6.
iPhone 3Gs iPhone 4s

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Figure 7: Samsung S3 vs S&P500 Abnormal Returns.
Comparing the Abnormal Returns from the iPhone 4 and the Samsung S4 (Figures 8 & 9), we
can see a similar pattern again emerging between products where the market under-reacts to
the announcement but manages to fix itself within a two day period, which is very rapid,
showing semi-strong efficiency. Although the concept of Efficient Market Hypothesis by Fama
(1965) is not entirely correct, the market does manage to pre-emptively correct itself for a few
products, evidence of this in the Samsung series by looking at the S5 whereby the market
suddenly spiked in anticipation of the announcement and immediately returned to a normal
level. There is further evidence of a semi-strong efficient market by looking at the cumulative
abnormal return graph for the S1 and S5 announcements whereby we see a similar pattern. The
market reactions do not seem to deviate too much, this would suggest a semi-strong form
efficient market.
-3
-2
-1
0
1
2
3
4
Day -3 Day -2 Day -1 Day 0 Day +1 Day +2 Day +3 Day +4 Day +5
AbnormalReturn.
Event Window.
Samsung S3 v S&P500 AbnormalReturns.
Figure7.
S&P S3

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Figure 8: Abnormal Return for Apple iPhone 4.
Figure 9: Abnormal Return for Samsung S4.
Figure 10: Cumulative Abnormal Return Samsung S1 and S5.
-5
-4
-3
-2
-1
0
1
2
AbnormalReturn.
Event Window.
AbnormalReturn Apple iPhone 4
Figure8.
-5
-4
-3
-2
-1
0
1
2
AbnormalReturn.
Event Window.
AbnormalReturn Samsung S4.
Figure9.
-1
0
1
2
3
4
5
6
CumulativeAbn.Ret.
Event Window.
Cumulative AbnormalReturn Samsung S1 and S5
Source: Yahoo Finance. Figure10.
S1 S5

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Finally looking at Sony we can how later entrants to the market tend to receive the least benefits
from product innovation, unless they can offer an innovation that is new to the market.
Reviewing Sony’s initial launch of the Z, we see less of a positive reaction to the announcement
in comparison to the first-mover (Apple’s iPhone), or the launch of the Samsung S series. This
is because Sony did not bring any new innovations to the market, they simply imitated the
competition. With the introduction of the Sony Z1, where the phone had waterproof
capabilities, we see an increase in abnormal returns for Sony’s stock at this time. This is
because the idea of a waterproof smartphone had not been implemented yet, so Sony was highly
innovative with their design. Looking at Table 10 for Cumulative Abnormal returns for Sony,
we can see that the highest was the Z1 due to this highly innovative idea. After implementing
the idea of a waterproof phone, Sony began to expand on consumer needs, such as a better
camera. This gave Sony continuous boosts in stock price when releasing a new product. The
fact that Sony was such a late mover, the market under-reacted to the announcement of the Z,
evidence of this is shown in the Cumulative abnormal returns figure below (Figure 11). With
the Sony Z series, as shown in the Table 11 in the appendix, we can see how innovative Sony
have been with their products throughout the series. This is evidence of how constant
innovation can lead to improved abnormal returns to the market due to semi-strong form market
efficiency.

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Figure 11: Cumulative Abnormal Return Sony Z.
There is also a strong relationship between announcements and stock prices, though the
relationship being positive or negative is dependent on how innovative the product is, and not
who the first-mover to the market is. Evidence of this is shown by the diminishing returns of
Apple stock prices following announcements of products, and the relatively constant returns to
Sony’s stock prices through constant innovation. The author comes to the conclusion that there
is sufficient evidence to accept the concept that the consumer electronic goods market has semi-
strong market efficiency, evidence of this is shown through the reactions in stock prices in the
graphs and tables below. The market tends to react to the public knowledge within the 5 days
after the announcement, but very rarely does it manage to immediately react correctly to these
announcements, meaning that it does not show enough evidence of strong-form efficiency.
The author would recommend to existing and new firms to the market would not simply imitate
other competitors as Apple tried to do, as it will not work. Instead the firms should spend more
time innovating their products, or expanding into alternative markets such as Sony did with
their waterproof phones and then innovative idea of having a better camera. Firms looking to
enter the market should not become reliant on one innovative idea, such as Sony did with their
-7
-6
-5
-4
-3
-2
-1
0
1
CumulativeAbn.Ret.
Event Window.
Cumulative AbnormalReturn Sony Z
Figure11.
Z

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waterproof idea and the M4 Aqua, this was not successful and was detrimental to the stock
price for Sony, having a continuous negative impact for the 5 days after the event.
The study could have been improved with a test of significance of the results. We would have
also liked to examine the effect on other products within the market such as tablets released by
Microsoft and the Apple iPad but due to time constraints this was not possible. We believe it
would have been beneficial to include more competitors within the market, but due to other
competitors such as Huawei not being traded on similar stock exchanges, this was not feasible.
The results of Eddy & Saunders (1980) do not coincide with that of the author’s. We found that
announcement events do have some impact on stock prices, with the event having a more
positive impact on stock returns if the product provides innovative ideas. The results found by
Pauwels et al. (2004) whereby new product releases have a high-correlation with increased
long-term financial performance through positive changes to the firm’s stock value are similar
to the results of the research question. This dissertation concluded that the introduction of
original new products and new to market innovations seem to have the most positive returns
on stock market, with the exception of highly innovative product successors, such as Sony’s
waterproof feature innovations that wouldn’t have been introduced to the smartphone market
at the time of announcement. While product innovations were important, we cannot rule out
the equal importance of advertisement of these new innovations. The findings of positive
reactions in the stock market to product innovations and the announcement of a product is
backed by the findings of Chaney & Devinney (1992) who found there to be .60i positive return
over the subsequent 3 days of an announcement of an innovative product or service. In this
research question we see that Apple initially performed well due to being a monopoly in the
market, then with the introduction of Samsung and Sony, the first-mover advantages began to
dissipate with the introduction of new competition and innovation by competitors.

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We found that there is a semi-strong relationship between stock price and product
announcement. There is evidence that the more innovative a product is, with regards new to
the market services, there is a higher return for this product over a 5 day event window. There
is also evidence with the introduction of competition firms become more innovative with their
products. There also exists a first-mover advantage which dissipates with the introduction
competition.

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9. Appendix.
Product Innovations: Apple.
Table 11.
Apple Products.
iPhone (1st Gen) Established the main body of the smartphone with button placement and screen size.
iPhone 3G
Allowed for improved cellular data access through ‘3G’ mobile data. Added a new
operating software (iOS) to the iPhone series.
iPhone 3GS
Added a faster processor and higher resolution camera allowing for the phone to run
faster than previous generations. Better camera allowed for improved videos also.
iPhone 4
Introduced the front facing ‘selfie’ camera to iPhone series. Improved back facing
camera.
iPhone 4S
Upgraded the camera to an 8 Megapixel camera which allowed for higher definition
video recording. Also first phone with ‘Siri’ a voice controlled AI on the phone.
iPhone 5
Increased screen size to 4 inches. Introduced faster cellular data with use of LTE (4G)
data access. Upgraded the charging port to allow for quicker charging.
iPhone 5C + 5S
Improved camera functions. Finger-print access to unlock the phone, integrated into
the home button. Much quicker speed of the phone through dual core processor.

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Table 11: Product Innovations.
Product Innovations: Samsung.
Samsung Products.
Galaxy S1
Android operating system instead of Apple’s iOS operating system. Front facing
camera.
Galaxy S2
Doubled the internal storage of the phone allowing for more photograph and
applications. Improved camera than original Samsung. Introduced LTE cellular
connections.
Galaxy S3
Improved battery quality in comparison to predecessors of the phone. Optional
increased internal storage. Introduced HD video recording to the S series. HD screen
for better quality screen. Improved phone speed and introduced an improved
operating system.
Galaxy S4
Improved screen pixilation. High quality camera, moving from 8MP camera to 13MP
camera. Improved battery quality.
Galaxy S5
Camera quality improved to 16MP camera, far ahead of Apple. Increased screen size
5.1 inches. Same screen quality as S4 but with improved running speed of phone.
Waterproof and dust proof capabilities.

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Product Innovations: Sony.
Sony Products.
Xperia Z Runs on Android, same as Samsung. 5 inch screen with water and dust-proof
capabilities. 13MP camera, same as the Samsung S4, also has front facing camera.
16GB internal storage.
Xperia Z1 Better and more reliant water and dust-proof capabilities. Over 20MP camera, by
far the best in the market. Tempered glass screen making it difficult to break.
Aluminium case made it more sturdy and reliable. Better image and video viewing
capabilities.
Xperia Z2 Allowed for 4k video recording (higher resolution video recording). Latest android
operating system at the time. Small improvement on camera.
Xperia Z3 Allowed for 4k video recording. Allowed for PlayStation 4 remote play (unique to
this device at the market). Both internal and external storage level increased. Better
water and dust-proof capabilities.
Xperia M2 More of a mid-range priced phone, with high quality capabilities. 8MP camera
slightly above standard at this price range (€200). 8GB internal memory.
Xperia M4 Water and dust-proof function. Improved 13MP camera. Internal storage increased
to 16GB. Weaker battery than predecessor.

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Figure 12: Cumulative Abnormal Return Apple 3Gs and iPhone 4.
Figure 11. Apple iPhone 3Gs and Apple iPhone 4 showing similar patterns in market
reaction. Market ever so slightly under-reacts and we see a fall in returns directly after the
event, followed by the returns increasing again and levelling off.
Figure 13: Cumulative Abnormal Return Samsung S1 and S5.
Figure 12. The cumulative abnormal returns for both the Samsung S1 and the
Samsung S5 show how the market can be efficient, with the returns not being highly volatile
or dropping and having to be counteracted with a high increase, as seen in Figure 11.
-12
-10
-8
-6
-4
-2
0
2
CumulativeAbn.Ret.
Event Window.
Cumulative AbnormalReturn Apple 3Gs and 4
Figure12.
iPhone 3Gs iPhone 4s
-1
0
1
2
3
4
5
6
CumulativeAbn.Ret.
Event Window.
Cumulative AbnormalReturn Samsung S1 and S5
Figure13.
S1 S5

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Figure 14: Cumulative Abnormal Return Sony Z2.
Figure 14. The market over-reacts to Sony Xperia Z2. The market increases
cumulative abnormal returns to above the threshold and at Day +3 begins the cumulative
abnormal returns begin to fall, and level off on Day +5.
0
1
2
3
4
CumulativeAbn.Ret.
Event Window.
Cumulative AbnormalReturn Sony Z2
Figure14.
Z2

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