The Release of Google Home & The Impact On Stock Values · 2 SWOT Analysis Figure 1: SWOT Analysis...

The Release of Google Home & The Impact On Stock Values

Journal of Student Research

Proceedings of the Flint Student Research Conference

Faculty Sponsor:

Sandun Perera, Ph.D.

School of Management

The University of Michigan-Flint

Joylynn Clement and Bo Xia

School of Management, The University of Michigan-Flint, Flint, Michigan 48502.

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Abstract

The purpose of our research is to study the release of Google Home speaker and analyze how it has

affected the company’s stock price. Our research includes a company analysis of Google, highlighting areas

such as the company’s restructuring, a SWOT analysis, their organizational strategy and their operational

strategy. These areas of research provide a deeper understanding of the company; however, our main focus

is to analyze Google’s stock values. We specifically look at values before and after the release of the

speaker. We use the values to create several forecasts using various methods. Errors between our forecasted

values and the actual values were then tested by running multiple error calculations to measure each of the

forecast’s accuracies.

Google has always shown innovation by creating a mixture of intangible services and physical

products. The company has also shown innovation in their decision to acquire subsidiary companies in new

markets. This paralleled innovation has allowed for the company to release a new line of products, in areas

that Google had not been in previously. Google Home is one of the most popular recent releases, and is a

voice activated speaker with multiple functions.

By creating a SWOT analysis for Google, we found that one of the company’s main strengths is

the revenue earned from online advertising. However, this revenue strategy is also one of their greatest

weaknesses. The company became extremely susceptible to market changes and fierce competition in the

online advertising market. Google has planned to counteract this situation by releasing new products, and

utilizing the Google brand to attract buyers in new markets.

For the forecasting section of this report, we first organized Google’s stock values for a selected

date range (10/03/2016 to 3/14/2017). To create our forecasted values, techniques of naïve, simple moving

average, weighted average, exponential smoothing, and linear trend lines were used. Errors in forecasting

were analyzed using the methods of MAD, MSE, and MAPE.

The research conducted of the stock values proved that the naïve forecast produced the most

accurate results. The project also provides research and explanation to why the naïve method provided the

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most accurate forecasting results in this situation, and how this simplistic method of forecasting can prove

the most accurate for even a company that is as big of a player as Google. We then compared our results to

market trends and norms, to find out if this forecasting method is commonly used in current stock market

practices. Our prediction formulated is that Google’s stock values will continue to increase in the immediate

future.

1 Introduction

In this project, we chose to focus on Google and their decision to release the Google Home. The

Google Home speaker was released on November 4th, 2016, as a competitive product against Amazon's

Echo. The Google Home is a smart speaker that enables users to speak voice commands to interact with

services using the Google Assistant. Some supported services of the speaker include Netflix, YouTube, The

Wall Street Journal, with new services being integrated on an ongoing basis. Google Assistant serves as an

“intelligent assistant” that allows for the hands-free experience of the speaker [8].

The significance of this product release is that it has become arguably the most popular products in

Google’s recent new releases. Within the past year, the company has been expanding their product line

extensively. In the beginning of November 2016, Google released the new version of their digital media

player, the Chromecast Ultra. November 10th, the company released Google Daydream - a virtual reality

platform [9]. From this group of products, the Google Home has proved to be the most popular new product

release, which is why we chose to specifically focus on this product and its release date.

By using several forecasting methods, we analyzed Google’s stock values and created predictions

for their immediate future values. We found the naïve forecasting method provided the most accurate

predictions, which was also our most simplistic method used. We then studied the naïve forecast’s relation

to the mathematical probability theory, the Martingale theory, and how this theory is commonly used for

making stock market predictions.

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2 SWOT Analysis

Google is the most popular search engine on the web. There are many search engines today that

provide results based on keyword searches, but Google is the best at providing relevant search results. As

of April 2017, Google holds 77.43% of the global market share on search engines [1]. To better understand

both the company and their decision to release the Google Home, we conducted a SWOT analysis. In this

analysis, we highlight some of the company’s strengths, weaknesses, opportunities and threats.

One of Google’s main strengths is the amount of revenue that is earned through online advertising.

In 2015, 90% of Google’s revenue came from advertising [7]. However, this great strength is also

considered to be one of Google’s main weaknesses. By earning most of their revenue from advertising, the

company is susceptible to changing market trends, competitive advertising websites, and marketing budget

Figure 1: SWOT Analysis Table – Google

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adjustments. Google realized that they were limiting themselves by remaining solely in the search engine

market, and has since taken steps to enter new markets. They began to examine their opportunities, and use

the revenues made from advertisements to begin acquiring new companies.

Google has found both success and failure in their quest to enter new markets. One of their

successful products, the Android Operating System, was released in 2008. By 2015, Android had surpassed

Apple’s iOS, claiming an astonishing 81% of the total smartphone unit shipments globally [16]. Google’s

primary competitor, MS Windows, currently has 38.59% of the world market share on all platforms

(desktop, mobile, and tablet), while Google’s operating system, Android, trails close behind with 37.42%.

The Apple operating system iOS, falls in third place, holding 12.99% of the market share [11]. Despite the

success that Google has found in the Android Operating System, other products have been discontinued to

avoid extensive financial loss.

The company also suffers from other weaknesses, one being compatibility issues with Google

Chrome. One of the more recent compatibility issues happened with the Windows 10 update. A significant

bug popped up in the Chrome browser, which would cause the browser to crash unexpectedly [4]. While

the company has taken steps to resolve Chrome’s compatibility issue, they have still risked losing users

who see these compatibility issues as unacceptable.

The main threats that the company faces include competition from other companies, regulations,

and foreign currency risk. These threats can directly affect the profits made by the company. For example,

if another country’s currency is stronger against the U.S.’s dollar, this could mean money lost when the

company is doing transactions with currency exchanges across different countries. With regulations, it can

become very costly to keep up with current and changing laws. The company also faces risk of future

regulations that may impact the production of their products.

After completing the SWOT analysis, we gained a deeper insight of the strategy behind the release

of the Google Home. The product was a perfect medium to incorporate successful technology the company

already had, such as Google Search, Analytics, and Translate. Google was also able to capitalize on their

subsidiary, Nest Labs, by including the capabilities to connect smart home products such as thermostats

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and security cameras. This product was also a way for Google to get people ready for artificial intelligence

by incorporating the “intelligent assistant”. Finally, the product served as a sign to the company’s

competitors that Google is still a force to be reckoned with.

3 Organizational Strategy

Organizational growth has traditionally been characterized as either organic (internally generated)

or inorganic (from acquisition). Companies will typically follow one method or the other when choosing a

strategy for growth. The organizational strategy of Google is inherently different from these traditional

approaches, by practicing a mixture of both organic and inorganic strategies. Google has a “semi-organic”

strategy for growth, which is when revenue results from acquired technology or assets attaching to a

company’s existing capabilities in a complementary manner [5].

The Google Home is an excellent product to use as an example of Google’s organizational strategy.

In 2013, Google acquired the company Nest Labs, which is a home automation company that produces Wi-

fi enabled products. The Google Home has been enabled with the capabilities to connect to these “smart

home” devices created by Nest. This connectivity capability provides an additional feature to the speaker,

and will ideally also increase sales of smart home products in the future.

Google’s main source of revenue has been from offering ads on their online websites. This business

strategy has been the first of its kind, and Google has remained dominant in the search engine world, holding

77.43% of the market share currently [14]. However, Google realized that they cannot rely on this main

source of income forever. The company has taken the steps to look for new ways to earn revenues, while

practicing a very forward-thinking strategy to ensure the company’s survival. One of the most influential

steps the company has taken to achieve this goal was their restructuring.

In 2015, Google restructured into a company called Alphabet, Inc. The purpose of Alphabet is to serve

as a parent company to several subsidiary companies [Figure 6: Alphabet’s Structure]. Google serves as

Alphabet’s largest subsidiary. In addition, Alphabet owns seven subsidiaries companies including: Verily,

Google X, Google Fiber, Google Ventures, Google Capital, Calico and Nest Labs [3]. Co-founder of

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Google, Larry Page serves as Alphabet’s CEO, while the other co-founder, Sergey Brin, serves as President.

By restructuring into Alphabet, Page and Brin now have the ability to oversee the many businesses and

ventures of the company in a “bird’s eye view”. Sergey Brin and Larry Page have chosen CEOs to run each

business, thus allowing the pair to aid and provide insight to the areas that need it the most.

An additional benefit of the restructuring from an organizational perspective, is the vast range of

diversity within the subsidiaries. For example, the company Calico is focused on combating aging and

disease, while Google X is currently working on self-driving cars. The vastly different subsidiaries of

Alphabet each have independent listing opportunities. Each subsidiary can individually attract talent, by

providing higher salaries compared to other start-up companies. These strategies increase the overall talent

in employees, which will give the companies a better chance at finding success.

In typical Google fashion, the company’s decision to restructure in this manner was extremely

innovative, and analysts became “gaga” over the situation. According to Doug Anmuth, analyst at J.P.

Morgan, “We are bullish on Google’s new operating structure. Overall, we view the new structure as an

elegant way for Google to continue to pursue long-term, life-changing initiatives while simultaneously

increasing transparency and management focus in the core business [13].”

4 Operational Strategy

Google follows a strategy of differentiation. We can understand this by looking at the Google

Search engine and seeing how much the product was unlike anything else before. With many competitive

companies now following a similar strategy, Google has decided to slightly alter their overall strategy.

Through the acquisitions of companies, Google has begun to combine their technologies with others. While

this strategy is certainly advantageous, the company has faced some bumps along the road. It will be

interesting to see where the company finds success from the many projects that are in the works.

Google’s operations involve having separate management teams and goals. These teams are

individually focused on existing or future products and services. However, the company encourages a

universal strategy for these areas of operation to maintain an overall connectivity between these different

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teams. The decision areas that Google focuses on for their operations are: design of goods and services,

quality management, process and capacity design, location strategy, layout design and strategy, human

resources and job design, supply chain management, inventory management, scheduling, and maintenance

[18].

Google has also taken innovative steps to improve their supply chain. One of their changes can be

seen in their release of a Google mobile phone. Previously, Google had to work with other companies to

produce their Nexus units. This allowed for those companies to have a significant control over Google’s

supply chain. By creating their own phone, Google can now control the entire process of manufacturing,

and allow them to function independently from other companies [17]. By intentionally purchasing

subsidiary businesses that can create products for Google, they have eliminated many of the steps and

companies they previous had to work with.

5 Forecasting

The preliminary plan for our forecasting technique was to record sales data for the Google Home

speaker. While we could find some information from Alphabet’s financial reports filed through the SEC,

the reports were not sub-divided in appropriate detail for us to recover enough relevant data. Upon finding

that sales data was not a viable option, we decided to focus on Alphabet’s (GOOGL) stock values and apply

our selected forecasting techniques for analyzation.

The first step in our forecasting process was to collect the data of stock values within our date

parameters. The Google Home was released in November 2016, so we began collecting stock data from

October 1st, 2016. We found and recorded this data from the Nasdaq webpage and collected the information

of date, low price, high price, closing price and volume amount traded [2]. (Ref. Excel Stock.Report Table

of Stock Records)

We determined that our ending date for data collection would be March 14th, 2017. By ending the

data collection on this date, we would be able to specifically focus on the initial impact of the release of the

Google Home. The data that we used to create our forecasting methods ranged from October 3rd, 2016, (first

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trading day in October) to March 14th, 2017. After organizing the data, we created a chart to visualize the

patterns of Google’s stock over this time range [Figure 5: Linear Trend Line]. Upon the completion of this

chart, we observed that despite some notable decreases, the values were increasing in a linear nature

following the release of the speaker. Due to the observed linear nature of the values, we created a hypothesis

that our linear trend line forecasting method would provide the more accurate forecast predictions.

6 Forecasting Methodology

We began with creating a Naïve Forecast for our collected data. Our forecasting equation for the

Naïve category was:

Forecastt = 𝐴𝑐𝑡𝑢𝑎𝑙𝑡−1

After creating the forecast for the stocks, we calculated the errors between the actual data and the

forecasts. We then analyzed our forecast errors using the MAD, MSE, and MAPE methods of forecasting

accuracy. We then continued to create the remaining of the forecasting methods of moving average,

weighted moving average, exponential smoothing, and linear trend line.

When creating our forecast for the simple moving average, we began by using three periods.

The equation for this category was:

Forecastt =𝐴𝑐𝑡𝑢𝑎𝑙𝑡−1 +𝐴𝑐𝑡𝑢𝑎𝑙𝑡−2 + 𝐴𝑐𝑡𝑢𝑎𝑙𝑡−3

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After creating the forecast using three periods, we also wanted to be able to analyze how the

forecasts and their accuracies would change when using a different number of periods in the formula. We

decided to also include forecasts for simple moving averages using both four periods and five periods. The

equations are as followed:

Forecastt =𝐴𝑐𝑡𝑢𝑎𝑙𝑡−1 +𝐴𝑐𝑡𝑢𝑎𝑙𝑡−2 + 𝐴𝑐𝑡𝑢𝑎𝑙𝑡−3 + 𝐴𝑐𝑡𝑢𝑎𝑙𝑡−4

4

Forecastt =𝐴𝑐𝑡𝑢𝑎𝑙𝑡−1 +𝐴𝑐𝑡𝑢𝑎𝑙𝑡−2 + 𝐴𝑐𝑡𝑢𝑎𝑙𝑡−3 + 𝐴𝑐𝑡𝑢𝑎𝑙𝑡−4 + 𝐴𝑐𝑡𝑢𝑎𝑙𝑡−5

5

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For the category of weighted moving average, we used a three-period weight distribution, with the

most weight on the most recent data. The equation used is as followed:

Forecastt = 1/2(𝐴𝑐𝑡𝑢𝑎𝑙𝑡−1) +1/3(𝐴𝑐𝑡𝑢𝑎𝑙𝑡−2) + 1/6(𝐴𝑐𝑡𝑢𝑎𝑙𝑡−3)

In the exponential smoothing category, we used the following equation. We decided to perform

forecasts for exponential smoothing using alphas of: 0.1, 0.2, 0.3, 0.4, 0.5 and 0.9.

𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑡 = ((1 − 𝛼)𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑡−1)) + (𝛼(𝐴𝑐𝑡𝑢𝑎𝑙𝑡−1))

For our linear trend equation, we first calculated a and b to find the equation of our linear trend

line.

𝑏 = 𝑛 ∑ 𝑡𝑦 − ∑ 𝑡 ∑ 𝑦

𝑛(∑ 𝑡2) − ( ∑ 𝑡)2

𝑎 = ∑ 𝑦 − 𝑏(∑ 𝑡)

𝑛

Once the factors for a and b were found, we formed our linear trend equation. (Ref. Figure 6)

𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑡 = 0.5679𝑡 + 763.66

When all forecasting methods were completed, and related forecasting accuracy tests were

performed, we organized the results into Figure 2. We were now able to analyze which forecasting method

produced the highest level of accuracy.

7 Forecasting Results

As shown in Figure 2 below, the highlights in blue were the results in their respective category that

produced the least accurate results when compared to all twelve forecasting methods. The yellow highlights

were the most accurate results in their category when compared to the rest. The linear trend forecasting

method produced the least accurate results, while the naïve forecasting produced the most accurate results.

We examined the exponential smoothing categories by their respective alphas, and found that as the alpha

was increased, the results became more accurate.

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To ensure that our results were correct, we used an exponential smoothing forecast with an alpha

of one and ran the three error accuracy tests. By using an alpha of one, the exponential smoothing forecast

provides the same calculations as the naïve forecast. This pattern can be explained by the fact that using a

large alpha in this method will give greater weight to more recent changes in data, while using smaller

alphas will weigh out recent and past values, respectively. To visualize just how accurate the naïve

forecasting method was, we created a graph to show the close relation between the forecast and the actual

stock values [Figure 7: Naïve Forecast vs. Actual].

8 Control Chart

We created our control chart for our naïve forecast errors by using z=3. Our process included first

taking the MSE from the naïve forecast and finding the square root. We then found our upper control limit

by multiplying z by the square root of the MSE. To find our lower control limit, we multiplied negative z

by the square root of the MSE.

MAD MSE MAPE MAD MSE MAPE MAD MSE MAPE7.98 108.36 1.01 11.74 204.43 1.47 7.02 83.10 0.89




Moving Average 3

Moving Average 4

Moving Average 5

Weighted Moving Average

Exp. Smoothing (alpha = 0.9)

Linear Trend Line

Naïve






Figure 2: Forecasting Results

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9 Conclusion

After completing our forecasting calculations, we were surprised that the naïve forecast had

provided the most accurate results. Our predictions of the linear trend line being the most accurate method

were incorrect, and in fact this method turned out to be the least accurate forecasting technique. After

additional research, we found explanations of why the naïve method can in many cases, produce the most

accurate results to predict future stock values.

The naïve method’s concepts can be traced back to an eighteenth century mathematical theory

called the Martingale probability theory. This theory states that given all the past information, the best

expected forecast for tomorrow, will be today’s value [10]. Despite the observable trend of Google’s stock

Figure 3: Control Chart – Naïve Forecast

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values to be increasing, they still suffered from decreases and proved susceptible to market influences. Our

research proves that even with the strong dominance Google has, the market norms still apply to them.

The naïve theory is commonly used by investors and traders when analyzing stock values. This

theory has been proven throughout history to provide more accurate results compared to the methods that

are more complex in nature. The volatile nature of the stock market explains why complex forecasting

methods that consider past value patterns, cannot accurately predict future values. The research conducted

in the project provides exciting confirmation that the least labor intensive forecasting method can in most

cases, produce the most accurate results for stock predictions.

We concluded that the release of the Google Home has helped contribute to the company’s stock

value increase. Due to the simplistic nature of the Naïve forecast, we were unable to predict qualitative

predictions for future stock values. Our quantitative prediction is that the Naïve method will continue to

produce the most accurate predictions for this company’s future stock values.

Acknowledgments

This project was completed in partial fulfillment of the requirements of the SCM 371 course at the School

of Management, The University of Michigan-Flint in Winter 2017; we thank our group members Rania

Kattoula and Vernell Shock for their contribution to the preliminary part of this research.

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Additional Tables & Figures

Figure 4: Google Stock History

y = 0.5679x + 763.66R² = 0.5576

680

700

720

740

760

780

800

820

840

860

1 4 71

01

31

61

92

22

52

83

13

43

74

04

34

64

95

25

55

86

16

46

77

07

37

67

98

28

58

89

19

49

71

00

10

31

06

10

91

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Linear Trend Line

Figure 5: Linear Trend Line

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Figure 6: Alphabet’s Structure

Source: CNN.com

Figure 7: Naïve Forecast vs. Actual

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[13] Pett, David. (2017) "Why Analysts Are Gaga About Google Inc's Restructuring Under Alphabet

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