Dissertation

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........................................................................................................................2

2. List of Tables..........................................................................................................................3

3. Abstract..................................................................................................................................4

4. Introduction............................................................................................................................5

5. Literature Review...................................................................................................................8

5.1. Previous Research...........................................................................................................8

5.2. Theories and Concepts..................................................................................................11

6. Data and Data Analysis........................................................................................................13

7. Methods of Analysis............................................................................................................17

8. Results and Conclusions......................................................................................................23

9. Appendix..............................................................................................................................33

10. References..........................................................................................................................38

11. Bibliography.......................................................................................................................40

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1. List of Figures.

Figure 1: Apple Stock Prices....................................................................................................21

Figure 2: Samsung Stock Prices..............................................................................................21

Figure 3: Sony Stock Prices.....................................................................................................22

Figure 4: S&P500 Stock Prices................................................................................................22

Figure 5: Abnormal Returns for iPhone...................................................................................30

Figure 6: Cumulative Abnormal Returns for Apple 3Gs & iPhone 4......................................31

Figure 7: Samsung S3 vs S&P500 Abnormal Returns.............................................................32

Figure 8: Abnormal Return for Apple iPhone 4.......................................................................33

Figure 9: Abnormal Return for Samsung S4............................................................................33

Figure 10: Cumulative Abnormal Return Samsung S1 and S5................................................33

Figure 11: Cumulative Abnormal Return Sony Z....................................................................35

Figure 12: Cumulative Abnormal Return Apple 3Gs and iPhone 4........................................41

Figure 13: Cumulative Abnormal Return Samsung S1 and S5................................................41

Figure 14: Cumulative Abnormal Return Sony Z2..................................................................42

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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......................................................14

Table 4: Summary Statistics for Stock Returns.......................................................................15

Table 5: Abnormal Returns for Apple.....................................................................................20

Table 6: Cumulative Abnormal Returns for Apple..................................................................21

Table 7: Abnormal Returns for Samsung.................................................................................22

Table 8: Cumulative Abnormal Returns for Samsung.............................................................22

Table 9: Abnormal Returns for Sony.......................................................................................23

Table 10: Cumulative Abnormal Returns for Sony.................................................................23

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

developed in which vertical innovations, generated by a competitive research sector,

constitute the underlying source of growth”.

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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 ($).

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

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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 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

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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.

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

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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 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 &

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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 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.

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5.2. Theories and 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.

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

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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.

Apple Product Announcement and Stock Returns. Table 1.Sources: Yahoo Finance & Apple Website.

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%

Table 1: Apple Product Announcement and Stock Returns

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Samsung Product Announcement and Stock Returns. Table 2.Sources: Yahoo Finance & Samsung Website.

Product Name. Announcement Date. Release Date. 6 Month Return. 12 Month Return.

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%

Table 2: Samsung Product Announcement and Stock Returns.

Sony Product Announcement and Stock Returns. Table 3.Sources: Yahoo Finance & Sony Website.

Product Name. Announcement Date. Release Date. 6 Month Return. 12 Month Return.

Xperia Z 07/01/2013 09/02/2013 38% 16%

Xperia Z1 04/09/2013 04/09/2013 -14% -3%

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.

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

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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.

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

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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

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.

Figure 1: Apple Stock Prices.

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Figure 2: Samsung Stock Prices.

04/01/200510/04/200617/07/200717/10/200825/01/201028/04/201101/08/201206/11/201312/02/20150

10

20

30

40

50

60

70

Sony Stock Price (2005 - 2015) Figure 3.Source: Yahoo Finance. 

Year

Stoc

k Pr

ice U

SD $

Figure 3: Sony Stock Prices.

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1/3/2

005

5/23/

2005

10/1

0/20

05

2/27/

2006

7/17/

2006

12/4

/200

6

4/23/

2007

9/10/

2007

1/28/

2008

6/16/

2008

11/3

/200

8

3/23/

2009

8/10/

2009

12/2

8/20

09

5/17/

2010

10/4

/201

0

2/21/

2011

7/11/

2011

11/2

8/20

11

4/16/

2012

9/3/2

012

1/21/

2013

6/10/

2013

10/2

8/20

13

3/17/

2014

8/4/2

014

12/2

2/20

14

5/11/

2015

9/28/

2015

0

500

1000

1500

2000

2500

S&P500 Stock Price (2005 - 2015) Figure 4.Source: Yahoo Finance

Year

Stoc

k Pr

ice U

SD ($

)

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 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.

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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

Rt ( Apple )=Return at time period t of Apple stock .(i . e . R−3 ( Apple ) is 3 daysbefore Apple event)

Rt (S∧P 500)=Returnat time period t for the S∧P 500.

Rt (apple)=α+β R t (S∧P ) (i)

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Step 3: Retrieve α̂∧ 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∧ β̂. From each regression these values for α̂∧ β̂ need to be inputted into

the equation, as shown in (ii)

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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)=0.117 i

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=0.373 i

See Tables 2, 4 and 6 for Abnormal Returns.

(vi)

Step 5: Calculate the Cumulative Abnormal Returns.

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 – AppleSource: 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 1.69Table 5: Abnormal Returns for Apple

Cumulative Abnormal Returns – AppleSource: Yahoo Finance – Table 6.

iPhone iPhone 3G iPhone 3Gs iPhone 4 iPhone 4s iPhone 5 iPhone 5s

Day -30.373 0.004 0.626 0.836 -3.99 -2.22 0.472

Day -28.351 1.117 1.101 1.556 -3.9 -2.955 1.573

Day -112.9423 -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 +115.6063 -1.174 -2.944 -5.618 -8.11 -0.777 -5.409

Day +213.9807 -6.121 -4.001 -6.078 -9.71 0.363 -7.391

Day +316.2537 -9.534 -6.531 -4.971 -7.371 0.586 -10.683

Day +413.7527 -5.025 -5.461 -4.754 -4.712 0.319 -9.635

Day +57.4377 3.63 -4.507 -5.434 -5.234 -0.2994 -7.945

Table 6: Cumulative Abnormal Returns for Apple.

Abnormal Returns – Samsung.Source: Yahoo Finance – Table 7.

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 -1.28Table 7: Abnormal Returns for Samsung.

Cumulative Abnormal Returns – Samsung.Source: Yahoo Finance – Table 8.

S1 S2 S3 S4 S5

Day -32.008 -0.309 -0.3888 0.368 -0.659

Day -22.791 -1.788 0.5392 0.028 2.726

Day -12.873 -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 +14.138 -0.353 -0.4498 -2.756 3.771

Day +24.294 -2.22 -0.9198 -3.359 3.724

Day +35.2724 -2.402 -0.1138 -2.554 3.921

Day +43.5374 0.158 0.8732 -2.253 3.951

Day +55.483 -0.302 2.6292 -2.754 2.671

Table 8: Cumulative Abnormal Returns for Samsung.

Abnormal Returns – Sony.Source: Yahoo Finance – Table 9.

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 -1.239Table 9: Abnormal Returns for Sony.

Cumulative Abnormal Returns – Sony.Source: Yahoo Finance – Table 10.

Z Z1 Z2 Z3 M2 M4

Day -30.146 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

Day +1-6.047 2.5124 3.405 1.2676 3.473 -0.532

23 | P a g e

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.

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.

1 Snapchat is a smartphone application which allows for image messaging.2 Instagram is an online mobile photo & video sharing service. 

24 | P a g e

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 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

25 | P a g e

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).

Day -

3Da

y -2

Day -

1Da

y 0 

(Annou

ncem

ent)

Day +

1Da

y +2

Day +

3Da

y +4

Day +

5

-8-6-4-202468

10

Abnormal Return for Apple iPhone.Figure 5.

Event Window.

Abno

rmal

 Ret

urn

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 taken a turn for the worse, and also Samsung

began to launch newly innovative products to their firm.

26 | P a g e

Day -

3Da

y -2

Day -

1

Day 0

 (Ann

ounc

emen

t)

Day +

1Da

y +2

Day +

3Da

y +4

Day +

5

-12-10-8-6-4-202

Cumulative Abnormal Return Apple 3Gs and 4Figure 6.

iPhone 3Gs iPhone 4s

Event Window.

Cum

ulati

ve A

bn. R

et.

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.

27 | P a g e

Day -3 Day -2 Day -1 Day 0 Day +1 Day +2 Day +3 Day +4 Day +5

-3

-2

-1

0

1

2

3

4

Samsung S3 v S&P500 Abnormal Returns.Figure 7.

S&P S3

Event Window.

Abno

rmal

 Ret

urn.

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.

28 | P a g e

Day -

3Da

y -2

Day -

1

Day 0

 (Ann

ounc

emen

t)

Day +

1Da

y +2

Day +

3Da

y +4

Day +

5

-5

-4

-3

-2

-1

0

1

2

Abnormal Return Apple iPhone 4Figure 8.

Event Window.

Abno

rmal

 Ret

urn.

Figure 8: Abnormal Return for Apple iPhone 4.

Day -

3Da

y -2

Day -

1

Day 0

 (Ann

ounc

emen

t)

Day +

1Da

y +2

Day +

3Da

y +4

Day +

5

-5

-4

-3

-2

-1

0

1

2

Abnormal Return Samsung S4.Figure 9.

Event Window.

Abno

rmal

 Ret

urn.

Figure 9: Abnormal Return for Samsung S4.

Day -

3Da

y -2

Day -

1

Day 0

 (Ann

ounc

emen

t)

Day +

1Da

y +2

Day +

3Da

y +4

Day +

5-1135

Cumulative Abnormal Return Samsung S1 and S5Source: Yahoo Finance. Figure 10.

S1 S5

Event Window.

Cum

ulati

ve A

bn. R

et.

Figure 10: Cumulative Abnormal Return Samsung S1 and S5.

29 | P a g e

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.

30 | P a g e

Day -

3Da

y -2

Day -

1

Day 0

 (Ann

ounc

emen

t)

Day +

1Da

y +2

Day +

3Da

y +4

Day +

5

-7

-6

-5

-4

-3

-2

-1

0

1

Cumulative Abnormal Return Sony ZFigure 11.

Z

Event Window.

Cum

ulati

ve A

bn. R

et.

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.

31 | P a g e

Firms looking to enter the market should not become reliant on one innovative idea, such as

Sony did with their 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

32 | P a g e

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.

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.

33 | P a g e

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 3GAllowed 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 4Introduced the front facing ‘selfie’ camera to iPhone series. Improved back facing

camera.

iPhone 4SUpgraded 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 5Increased 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 + 5SImproved camera functions. Finger-print access to unlock the phone, integrated into

the home button. Much quicker speed of the phone through dual core processor.

34 | P a g e

Product Innovations: Samsung.

Samsung Products.

Galaxy S1Android 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 S4Improved 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.

35 | P a g e

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.

Table 11: Product Innovations.

36 | P a g e

Day -

3Da

y -2

Day -

1

Day 0

 (Ann

ounc

emen

t)

Day +

1Da

y +2

Day +

3Da

y +4

Day +

5

-12

-10

-8

-6

-4

-2

0

2

Cumulative Abnormal Return Apple 3Gs and 4Figure 12.

iPhone 3Gs iPhone 4s

Event Window.

Cum

ulati

ve A

bn. R

et.

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.

Day -

3Da

y -2

Day -

1

Day 0

 (Ann

ounc

emen

t)

Day +

1Da

y +2

Day +

3Da

y +4

Day +

5-1

1

3

5

Cumulative Abnormal Return Samsung S1 and S5Figure 13.

S1 S5

Event Window.

Cum

ulati

ve A

bn. R

et.

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.

37 | P a g e

Day -

3Da

y -2

Day -

1

Day 0

 (Ann

ounc

emen

t)

Day +

1Da

y +2

Day +

3Da

y +4

Day +

501234

Cumulative Abnormal Return Sony Z2Figure 14.

Z2

Event Window.

Cum

ulati

ve A

bn. R

et.

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.

38 | P a g e

10. References.

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