Ge empirical analysis

Running head: General Electric p.1

General Electric:

An Empirical Analysis of GE’s Implementation of Modern Business Analytics

Contributors: Janice Ang-Smith, Bipasha Basu Shah , Tim CallahanKhadijah Miah , Jessica Schmitke

Robert Morris University


TABLE OF CONTENTS

Section One PAGE

Executive Summary 3-4

Literature Review 4-5

Introduction 5-7

Discussion 7-16

Section Two

Model Specification 18-19

Estimate Parameter for Best Fitted Model 19-21

Goodness of Fit 22-25

ACF and PACF 25-27

Forecasting 27-29

UCL and LCL 29-31

Conclusion 32-33

Appendix 34

References 35-37

Executive Summary


The research of this paper focuses on the business of General Electric. Specifically, how

the business uses predictive analytics in order to thrive in the market place. GE uses analytics in

a variety of data solution products that it offers as products to its customers. They focus on

integrated data collection and visualization solutions in order to collect archive and distribute

tremendous volumes of real-time information at extremely high speeds. The goal of these

products is process improvement, higher efficiency and lower long-term costs.

The next part of our research involves the creation of a predictive model using General

Electric stock indicators to describe the variation in 20 variables from the Federal Deposit

Insurance Corporation (FDIC). Our data sources included 20 quarterly macroeconomic variables

from the FDIC and 7 General Electric stock indicator variables for the years 2001 through 2014.

Our model would be used to predict what would happen to GE’s stock indicators in the coming

three years (2015-2017) according to three scenarios, a base scenario, adverse scenario and a

severe adverse scenario. Base scenario represents stable economic conditions through time,

adverse scenario indicates more variation in economic conditions over time and severe adverse

indicates the most variation in economic conditions over time.

The goal of our model was to determine if the selected variables varied in time together

in order to identify a relationship. If a connection or significant relationship existed amongst

identified variables, then we could use a model to predict the General Electric stock variables for

the future. The intention of this research was to answer the following question: Which two

General Electric stock market variables have the highest significance to the econometric

variables? It was hypothesized that the closing price of the stock and the daily trading “high”

would be positively correlated with the predetermined macroeconomic variables, deeming them

the most reliable dependent variables among the seven that were tested. The hypothesis was


proven to be inaccurate due to the fact the adjusted close and stock low were the most significant

relevant variables correlated with the econometric variables used. Quarterly forecasts for both

the adjusted close and stock low were done through the year 2017.

The predictive model created can be used for insights to make strategic company

decisions, like when to explore new markets, acquisitions, and retentions; find upselling and

cross-selling opportunities; and discovering areas that can improve security and fraud detection.

The model can indicate not only what to do, but also how and when to do it, and to explain what-

if scenarios.

Literature Review

Matthew Richardson and James Stock conducted a time series statistical analysis to

determine the influence of mean reversion on stock returns. They were employed by the National

Bureau of Economic research when their paper titled “Drawing Inferences from Statistics Based

on Multi-Year Asset Returns” was published in 1990. The peer reviewed scholarly research

paper explored the “possibility of mean reversion in stock prices” through the use of asymptotic

distribution theories that provide coverage probabilities. The research concluded that mean

reversions are most pronounced in stock prices when a short period of time is considered. “An

example is the regression of one-month returns. . .” (Richardson, 1990). Richardson and Stock

also concluded, “The extension of this approach to multivariate statistics, such as the regression

of multi-year returns on lagged dividend-price ratios is a topic of ongoing research” (Richardson,

1990). This research paper has been reviewed because our current research related to General

Electric is also time series dependent and influenced by econometric variables. Richardson and

Stock’s study has been taken into consideration because it creates a good foundation for our


research. The main difference between the past research and our current study is the time frame

associated with the analyzed data. Their research used data from monthly outputs; our current

research has been scaled quarterly to match predetermined econometric variables.

Introduction

General Electric is an American corporation publicly traded on the New York Stock

Exchange (NYSE) as GE. The organization was founded in Schenectady, New York in 1892 by

Thomas Edison, Charles Cofin, Elihu Thomson and Edwin Houston. Jeffrey Immelt is currently

the Chief Executive Officer (CEO). The firm does business worldwide in the following

industries: electrical distribution, electric motors, energy, finance, gas, healthcare, lighting,

locomotives, oil, software and water. As of 2013 the company’s revenue was approximately

$146 billion with an operating income of $27 billion. In 2013 GE’s net income was $13 billion

and they owned $656 billion assets. GE had a total equity of $137 billion and over 300 thousand

employees in 2013. GE is compartmentalized into 9 subsidiaries: aviation, capital, energy, global

research, healthcare, home & business solutions, oil & gas, power & water and transportation.

Each subsidiary has the letters GE in front of the particular line of business, an example is GE

healthcare. We chose to analyze GE because the company is worldwide and has a long track

record of being an influential corporation in many industries. Many of the firm’s products are

considered household brands and it is likely that every American has at least one product

manufactured by GE in their home at all times.

The following topics and ideas will be thoroughly addressed throughout section one of

this document. Types of analytics implemented by GE will be explored. GE’s use of analytics

will be compared to how competitors are using similar information from internal and external


databases to benefit their companies. The firm’s overall approach on data mining will be

discussed. Methods used by GE to implement the findings from predictive analytics for

marketing purposes and potential improvements will be elucidated. GE’s use of digital analytics

and digital media to increase business and improve their public corporate image will be

expounded upon. What the company is doing to minimize risk and accurately model potential

risk and threats to the success of the business will be explained.

The second section of this research paper is focused on the econometric model generated

from the selected variables and economic influences. Section two of this document is focused on

the econometric model generated from a collection of economic influencers and significant

dependent variables. An analysis of the dependent variables has been conducted. Three datasets

were used to conduct the analysis. Econometric variables were provided by upper level

management to represent base,severe, and adverse economic scenarios Dividends were

extrapolated from quarterly data located on Yahoo Finance. Stock variables/ performance

indicators were calculated from raw monthly data collected from Yahoo Finance and

recalculated to yield quarterly outputs. The purpose of the research is to answer the following

question. Which two stock related variables are the most affected by economic variables?

Answering the aforementioned research question will provide insight regarding what should be

monitored closely to determine and predict the financial health and stability of GE’s stock. It is

hypothesized that out of the seven initial dependent variables, the closing price of the stock and

the daily trading “high” would have the highest significance to the macroeconomic variables.In

depth research and statistical analysis proved the hypotheses to be incorrect.

Discussion


As one of the world's largest companies, GE is a major manufacturer of systems in aviation,

rail, mining, energy, healthcare, and more. When it comes to utilizing business analytics, General

Electric uses business analytics in two ways. One is what could be referred to as data mining.

An example of how GE uses this is in detecting when rotating machinery might fail by

combining a lot of sensor data with software and analytics (as GE does with its Proficy

software). The second way GE uses analytics is how to take the available data and drive it

toward commercial objectives intended to provide solutions that improve its performance.

GE announced it has added 14 new industry modules to its Predictivity line of software,

adding to the 10 it had to manage and analyze industrial data. It sells Predix, a data management

platform designed to store and use machine-generated data. Another one of GE’s predictive

analytics software products is called Proficy SmartSignal. It identifies impending equipment

problems early across all process industries and helps avoid unexpected shutdowns and failure.

The software identifies what is going to fail, what is the cause of the failure, and what is the

priority of the impending failure. (Murphy)

One of GE’s most successful analytic software products that it offers clients is the ability to

analyze data from sensors on equipment to predict when the equipment needs to receive

maintenance.GE uses the warning and error alert data it has to identify patterns of behavior that

lead to more precise scheduled maintenance.

In aviation, the company's GEnx jet engine has 5,000 data points analyzed per second to

optimize flight times. For rail, GE and its customers will collect and analyze data from sensors to

determine when wheels need to be changed. Also, they intend to add cameras to record video of

the tracks that can be analyzed in real time. GE plans to enrich the data it collects with data from


other sources. For example, electrical grid maps will be enriched with satellite data so that

utilities know where to cut vegetation to reduce likelihood of power outages during storms.

These software analytic products came about after GE recognized the importance of big data

and launched a new initiative called the “industrial internet,” which aims to help customers

increase efficiency and to create new revenue opportunities for GE through analytics. The

industrial Internet is GE’s spin on the “the internet of things,” where internet connected sensors

collect vast quantities of data for analysis. Sensors are being embedded in “intelligent machines”

manufactured by GE including jet engines, power turbines, medical devices and so on. (Lampitt)

GE is actively using business analytics to maintain competitiveness with comparable

brands. In 2010 GE introduced a new campaign called Industrial Internet, it is similar to Internet

of things which is networking and connecting computers and other smart devices, but Industrial

Internet pertains to connecting big machines such as jet engines, power plant wind turbines, oil

& gas drills that GE manufactures. CEO Jeff Immelt explained that the reason behind Industrial

Internet was because GE was experiencing a gap in running and managing their machines, and

realized the opportunity in competing in the software industry (Betts, 2014).

Being a company with one of the largest global market share in different industries such

as aviation, locomotive, energy, health care, banking, GE remains not known to be a software

company, but is known to build giant machines. Today GE wants to build not just machines but

machines that are smart. To do this, GE invested in software platform such as Hadoop—a

software that can manage data growing in tremendous amount without requiring to grow number

of servers or data storage, thus saving money for companies (Hadoop, n.d.).


Within Hadoop is GE’s software called “Predix” providing a standard asset and

optimization tool called “Predictivity” used for industrial sized machines. These tools together

connect the big machines, the analytical capability and the people or the users. The software

collects the data from the machines, breaks down the data, and analyzes it. Analyzing data allows

for example to determine if a machine will require new parts soon, enabling companies to make

the purchase before the machine breaks. It helps prevent or predict machine failure before it

happens, which reduces down time and saves company money (GE Introduces Software Suite

that Utilizes Predix Industrial Internet Platform, 2014).

GE had figured out how to turn itself into a software company by building state of art

labs that study the software, the machines, and testing every aspect of analytics in order to

compete in the software industry. Today GE sells Predix and Predictivity to other companies

serving as data analytic tools, and according to VP William Ruh their target market is “any

business with a piece of equipment you are trying to optimize” (Clancy, 2014).

GE with its newly found expertise now offers to other companies analytical tools. Some

of the areas that GE is now competing in software expertise are architecture, advance research,

data science, development, user experience, cyber security, cloud services and commercial

strategy. Even though GE has ventured out in software it is still a fairly new division competing

with some of the largest companies that clearly had been in the software business longer. Few of

its competitors are IBM, Microsoft, Cisco, Oracle, SAP and SAS (Sallam, 2013). However,

regardless of the massive market influence of the known other companies, GE is not backing

down and is determined to gain a share in the software market. CEO James Immelt knows that

existing large software companies might have an advantage and are more advanced, but GE’s

advantage is that it builds the machines and now its building, creating, managing, and monitoring


its own products in industrial scale. A promising update on its new business is its partnership

with Softbank, a financial company in Japan that should allow GE to get into software markets in

developing countries (Hardy, 2014). So far, GE had expected a gain of $1 billion in 2014 and

expecting that to increase to $200 billion in the years to come (GE's Planned Industrial (Sallam,

2013).

GE is also currently using data mining and analysis to create strategic business

advantages through the integration of real time hardware status updates. The firm is collecting

performance data from hardware transmitters to better service their products and affiliated

brands. According to Saran of Computer Weekly, “GE manufactures jet engines, turbines and

medical scanners. It is using operational data from sensors on its machinery and engines for

pattern analysis.” The information collected is used to provide product specific servicing

recommendations that will directly benefit the productivity and up-time of the monitored

machines. “GE is using the analysis to provide services tied to its products, designed to minimize

downtime caused by parts failures. Real-time analytics also enables machines to adapt

continually and improve efficiency” (Saran, 2013). The data mining and analysis approach is

expected to directly decrease operational expenditures over time and increase the useful life of

the machines which will indirectly decrease capital expenditures.

Another example of the company’s software is titled Movement Planner; it assists train

companies save fuel. “Movement Planner is a cruise control system for train drivers. The

technology assesses the terrain and the location of the train to calculate the optimal speed to run

the locomotive for fuel economy” (Saran, 2013). There is a large amount of growth potential

within the company’s business analysis and data mining area. Considering the interconnectivity

of devices and the “internet of things”, there are vast amounts of data from internal and external


database available for GE to analyze. The company may want to consider analyzing how the

products benefit end-users and the variables that affect recurring usage. Specifying the

aforementioned will allow the company to continue to expand and satisfy more customers

simultaneously.

By use of predictive analysis, a sophisticated mathematical model can be created which

can be used as the basis of predictions. This model can be used in association with the pieces of

collected data to effectively boost sales and marketing to give insights into the characteristics and

behaviors to marketers. Companies like GE use predictive analysis to outpace it competitors

through twice as much incremental sales lift from a marketing campaign and its average click-

through rate from marketing campaigns is 76% higher than that of non-users. Customer behavior

data can be used to segment, target and personalize. By breaking customers into different

segments, loyal customers can be differentiated from ones who are not. In marketing

applications, predictive analytics is used for analysis of behavioral data and make marketing

decisions based on them. Data gathered over the years are used for analytical information

gathering initiatives on cross-selling, upselling, customer acquisition, new product introduction,

etc.

GE uses data as a long term strategic asset as opposed to a source of quick hits. They

collect and analyze a wide range of data about customers, partners, marketplace, competitors,

etc. They use analytics to spur customer offerings, to deliver customized products and services

and design distinctive capabilities to stand out from their customers. The organization uses data-

centric mind-set for growth initiatives and a more creative, sophisticated analysis processes.


GE emphasizes on creating value through data, thus making it both accessible and useful.

An example is their mobile application SmartChart. They have been collecting data from their

customers and were highly successful to attract new customers because they want to access this

data. They also collect continuous feedback from customers, and prototype ideas and put them in

front of customers for feedback. GE has a social intelligence command center for social listening

both internally and externally. They also use brand and reputation analytics. They gather and

analyze data to better understand the sentiments and fast moving trends. GE’s marketing

innovation is strongly customer centric and prediction based. GE regularly conducts discovery

sessions with its customers, maps the customer journey and conducts observational research.

GE also currently uses predictive modeling for risk purposes. Analytics is changing

expectations and business strategies. A decade ago, the company was in the mode of “if the

product breaks we fix it”. Today, GE has more than $100 billion in revenues tied to data drive

contracts where it gets paid based on a product being in service (Kalakota, 2012). It needs

predictive analytics to help customers avoid downtime, predict safety issues, and make contracts

profitable. Predictive models are in operation frequently in mission critical transactional systems

and drive decision and action in real time to minimize risk. A number of methodologies are

applications of both linear and nonlinear mathematical programming algorithms, in which one

objective is optimized within a set of constraints. Secondly, advanced “neural” systems, which

learn complex patterns from large data sets to predict the probability that a new individual will

have certain behaviors of interest in the business. Lastly, statistical techniques for analysis and

pattern detection are used with large data sets (Kalakota, 2012). Having access to all this data for

predictive analytics purposes allows the customers to be much more predictive about investment

outcomes and understand the value of the company and customer bond.


Outcome based business models create new dependencies and risk as well as revenue

opportunities. GE will depend on the ability of customers to operate successfully and be sensitive

to the same economic trends and potential shocks that affect them (Iansati and Lakhani, 2014).

GE is going to absorb a lot of business risk for its customers, but they have the financial

understanding and capabilities to manage that risk. They will have to reach out to the financial

sector for some carefully considered ways to cope with potential downsides and risks. The digital

technology and connectivity will have implications for the economy at large. No opportunity

comes without risks, which are best handled with awareness and transparency. Individual

investors, companies, and institutions should work to understand new assets, new connections,

and new dependencies. Institutions should ensure that connections are transparent and that the

powerful are held accountable for the impact of their decisions. If risks and potential downsides

are managed well, there will be short and long term rewards in the future.

GE made most of its revenue by selling industrial hardware and repair services. In recent

years, it was at a risk of incremental amounts to lose its top customers to competitors; IBM and

SAP and big data start-ups. These competitors utilized new efficiencies and other benefits

through advance analytics and algorithms based on the data generated by the equipment. GE

began to add digital sensors to its machines, connecting them to cloud-based software platform,

investing in modern software development capabilities, building advance analytics capabilities

and embracing crowd sourced product development (Iansati and Lakhani, 2014). With all this

being set in place, the company transformed its business model. For example, revenue from its

jet engines is not only tied to simple sales transactions but to performance improvements.

Digitally enabled outcomes helped the company generate more than $800 million in incremental


income in 2013. The company expects to reach at $1 billion in 2014 and again in 2015 (Iansati

and Lakhani, 2014).

The implementation of digital sensors is extending digitization and connectivity to

previously analog task, processes, and machine and service operations. Digital connectivity is

now essential to competitiveness in industries and companies. The company creates value by

extracting useful data from the sensors on its turbines and other wind energy equipment and

using that information to optimize equipment performance, utilization, and maintenance (Iansati

and Lakhani, 2014). It focuses on providing data synthesis and analysis and designing real-time

and predictive solutions to enhance the operations of its customers. Connecting the hundreds of

thousands of GE devices to one another and supplying them with sensors was a reasonable

extension of maintenance and operation driven business model, and one that would extend GE’s

strategic advantages.

Being a leader in efficiency, productivity, and innovation, they still haven’t been known

for the responsiveness and strategic awareness of its software development process. Developer

talent was a concern as GE’s engineers had experience with technologies that were last

generation such as the reliability of outside vendors for development. The CEO set out to create

a software platform that would work across the entire enterprise. Allowing new application

development to be more efficient and allowing rapid cross country innovation. The first step the

CEO chose was to enforce that all its members work together at GE Software headquarters in

San Ramon, California. New things are able to be created in a team based environment. The

team rolled out its first set of solutions under the Predictivity brand running on Predix, GE’s

common software platform. This platform dramatically streamlined monitoring and maintenance

of the company’s industrial technologies. Predix combines distributed computing and big data


analytics, asset management, machine-to-machine communication, security, and mobility.

Predictivity will eventually connect GE machines to the cloud allowing them to talk to one

another, learn from historical data, and provide predictive information to help eliminate

unplanned downtime and improve efficiency.

GE had a dramatic approach, abandoning the traditional mentality in favor of solution-

based sales that focused on the pain points and knew exactly how to use them to enhance

customer’s operating performance. The challenge was tricky, but is experimenting with different

types of partnerships and joint ventures. GE is also relying more on crowdsourcing for

innovation. The company invested in Quirky to propose, refine, select, fund, and build new

products. GE Aviation partnered with Alaska Airlines to present Flight Quest, making two

months worth of flight stats data available on an open platform. Outsiders were able to come up

with algorithms that could better predict flight arrival times, with a total of $250,000 awarded to

the top five entries. The winner was able to develop an algorithm that predicted arrival times

40% more accurately than the existing technology. It has also partnered with Intel for sensor

technology, Cisco for network hardware, Accenture for service delivery, and Amazon web

services for Cloud delivery (Iansati and Lakhani, 2014). GE is just one company being reshaped

by digital technology. Over time, digital technology and the internet of things will transform

every sector and every business.

There’s a need to focus efforts and measure results through different metrics, and there’s

a need to view the overlap between digital technologies such as mobile, clouds, social media, and

big data in order to identify opportunities that didn’t exist a few years ago. GE developed an

application called SmartChart. It’s a mobile app that uses big data algorithm collecting data from

25,000 customers. It has been highly successful in attracting new customers because they want


access to the data. With this GE developed an advisory board to discuss problems and

challenges. This allows continuous engagement with customers. It also uses brand and reputation

analytics. This helps to analyze all the data to better understand fast moving trends (Greengard,

2014). As a result, they are able to identify market opportunities customers might miss while also

helping them cut costs. Today’s data is the center of every company flourishing presently and

futuristically.

GE Capital, the division of GE focused on the financing aspect of the business recently

lost a lawsuit for approximately $169 million. The lawsuit involved claims that Spanish speaking

clients were discriminated against. It is believed that GE Capital mailed special debt reduction

program applications to over 400 thousand consumers but omitted customers who had an address

in Puerto Rico and that, “. . . indicated a preference for communications in Spanish (Douglas,

2014).” The class action lawsuit was brought against GE by the Consumer Financial Protection

Bureau and is considered the largest discrimination settlement related to credit card offers in U.S.

history. From a data perspective, it is fair to assume that the database had a language and

location designation and the two variables were part of a selection algorithm. The use of

language or ethnicity within a database intended to segment consumers is considered unethical

and illegal. There seems to be a “gray area” regarding the legalities of including indirect race

identifiers in an internal database used to segment clients. For example, language, favorite ethnic

food and origin can all be considered indirect race identifiers. The lawsuit was brought against

GE Capital because the actions committed were illegal and out of compliance with governmental

trade regulations. Potentially the firm can face additional legal troubles if they continue to

perform market segmentation while using race, language or anything related to a person’s

ethnicity as an independent variable. GE has not experienced any recent publically


acknowledged data breaches. The firm must continue to maintain a high level of security to

protect company specific information and sensitive client information that is stored on massive

internal databases.

Section 2: Model Specification

The cross- sectional data set retrieved from FDIC (independent variables) and GE’s stock

price indicators (dependent variables) is used to provide an analysis on their relationship and

predict stock price through the year of 2017.

Data Variables:

● 4 Quarters per year= 2001-2014

● FDIC Base Scenario= variability in time of continuation of normal activities

● FDIC Adverse Scenario= variability in time of adverse condition

● FDIC Severe Scenario= variability in time of severe conditions

● Open= GE’s price at which a security first trades upon the opening of an exchange for the

quarter.

● High= GE’s highest prices that a stock has traded for the quarter

● Low= GE’s lowest prices that a stock has traded for the quarter

● Close= GE’s final price at which a security is traded for the quarter.


● Volume= GE’s number of shares or contracts traded in a security or an entire market

during a quarter.

● Adjusted close= GE’s stock's closing price on any given day of trading that has been

amended to include any distributions and corporate actions that occurred at any time prior

to the next day's open for a quarter.

● Dividends= GE’s payment made in the form of additional shares, rather than a cash

payout per quarter.

● FDIC Dow Jones total stock market index (y1_13 base condition, y2_13 adverse

condition, y3_13 severe condition) = provide broad-based coverage of the U.S. stock

market including most stocks except the very smallest and least-liquid U.S. stocks

Quite commonly, a security's opening price will not be identical to its closing price. This

is due to after-hours trading and to changes in investor valuations or expectations of the security

occurring outside of trading hours. A security's opening price is an important marker for that

day's trading activity, especially for those interested in measuring short-term results, such as day

traders. When trading is done for the day, all stocks are priced at close. The price that is quoted

at the end of the trading day is the price of the last lot of stock that was traded for the day. This is

called a stock's closing price. The final stock price that is quoted can be used by investors to

compare a stock's performance over a period of time. This period is usually from one trading day

to another.

During the course of a trading day, many things can happen to affect a stock's price. Along

with good and bad news relating to the operations of a company, any sort of distribution that is

made to investors will also affect stock price. These distributions can include cash dividends,

stock dividends and stock splits. When distributions are made, the adjusted closing price can be

calculated. It represents a more accurate reflection of a stock's value.


High or low stock price are important factors in determining a stock's current value and

predicting future price movement. Security prices at market close, or closing prices, are more

important than intra-day prices for performance measurement.

B: Estimated Parameter for Best Fitted Model

Seven variables were chosen to be tested as dependent variables for GE’s stock price, out

of those seven variables, two were statistically significant—Low and Adj. Close stock price. In

Graph 1 variable Low shows as non-stationary due to a decreasing price trend, and Graph 2 the

Adj. Close is also non-stationary with an upward stock price trend. Notice how the “Observed”

lines of the dependent variables are well suited and followed closely by the “Fit” (predictor) or

the independent variable, it means the independent variable for the dependent variable is a good

predictor.

Graph 1: GE Quarterly Low Stock Price

Graph 2: GE Quarterly Adj. Stock Price


Using SPSS, the Expert modeler has been applied to predict GE’s Low and Adj. Close

stock prices. The expert modeler resulting ARIMA table shows that independent variable Y1_13

FDIC Dow Jones total stock market index variable was significantly influencing dependent

variables Low and Adj. Close stock price (see Figure 1 & 2). The significance value for Low was

.000 and .007 for Adj. Close; both values were less than the critical value .05, which means there

is a statistically significant relationship between the Dow Jones total stock market index and the

two dependent variables Low and Adj. Close stock prices of GE.

The model description table result from expert modeler shows the estimated ARIMA

model type using the standard notation of ARIMA (p,d,q) (P,D,Q). In Figure 1 it shows that the

model is ARIMA (0,1,0)(0,0,0), it means for the non-seasonal part of ARIMA there is 0 order of

autoregression, 1 degree of differencing, 0 order of moving average process, and 0 for its

seasonal counterparts (P,D,Q).

On the ARIMA model parameters table in Figure 1, the independent variable has a lag 0,

which means if for example the FDIC Dow Jones increases by 1000 points in a particular quarter

then the Low stock price will increase by 2 points. The lag 1 result means if FDIC Dow Jones


total stock market index increases by 1000 points this affects low stock price the following

quarter by decreasing one point. In Figure 2 the model description for the dependent variable

Adj. Close stock price result is similar to the model of Low stock price, which is ARIMA (0,1,0)

(0,0,0) with lag 1 and the estimate is also -.001, which would have the same explanation.

Figure 1: Low Stock Price

Figure 2: Adj. Close Stock Price

C: Goodness of the Fit

Stationary r-squared removes the trend making the percentage more accurate than regular

r-squared. Ljung-box statistic provides whether the model is correctly specified. A high number

in both stationary r-squared and Ljung-box statistic would prove to be a good model with

minimal trend or pattern and white noise insignificance. If the model presents a high percentage


in one area and a low in the other we would rather the stationary r-squared be higher, which

explains the observed variation in the series after accounting for the trend and stationary effects.

After running data of all independent variables of FDIC against the stock indicators variables it

was determined the best models are provided by two dependent variables of adjusted close and

low.

In Figure 3 shows variable Low stock price with 72.3% observed variation in the series is

accounting for the trend and stationary effect. The significance of the model is 96.4% which

indicates that the model is correctly specified. The significance test is listed at .000 which states

the probability of being wrong is .000 at both lags. This displays that the variable is well placed

in the model showing strong significance as the cutoff statistic is at 8% to which our significance

is less than 8%. The model forms equation:

Base condition: Low-Model_1-Low-Model_1-1 = -.893 +.002(y1_13-y1_13-1)+ -.001(y1_13-

y1_13-2)

Adverse condition: Low-Model_1-Low-Model_1-1 = -.893 +.002(y2_13-y2_13-1)+

-.001(y2_13-y2_13-2)

Severe condition: Low-Model_1-Low-Model_1-1 = -.893 +.002(y3_13-y3_13-1)+

-.001(y3_13-y3_13-2)

The model states: The difference in Dow Jones total stock market index (y1_13, y2_13,

y3_13) will increase the difference in GE’s low stock price by 2 points at lag 0, while the

difference in Dow Jones total stock market index will decrease the difference in GE’s low stock

price by 1 point at lag 1 in all conditions. For example, if the difference in Dow Jones stock

market index is at 1000, as the index rises at 1000, the difference in GE’s low stock prices will

increase by 2 points instantaneously while the difference in low stock price will decrease by 1

point the following quarter at lag 1.The model displays no transformation at both lags.


A good model is formed overall with stationary r-squared of 72.3% and Ljung-box

statistic is 96.4% (see Figure 3). Stationary r-squared explains the total variation from 0% to

100%. Variables y1_13, y2_13, and Y3_13( FDIC Dow Jones total stock market index) is

statistically significant at both lag 0 and lag 1 at .000. The Ljung-Box test is based on the

autocorrelation plot, instead of testing randomness at each distinct lag, it tests the "overall"

randomness based on a number of lags. The null hypothesis indicates a correctly specified model

with a high r-squared indicating the data is random. The alternative hypothesis indicates pattern

left in the model which isn’t good for the model indicating the data is not random. As a result,

we fail to reject the null hypothesis of randomness as very little pattern is shown forming a good

model with high percentage of stationary r-squared distribution.

Figure 3: Low Stock price

In Figure 4 shows variable Adj. Close with 68.4% observed variation in the series is

accounting for the trend and stationary effect. The significance of the model is 87% which

indicates that the model is correctly specified.The significance test is listed at .000 which states

the probability of being wrong is .000 at both lags. This displays that the variable is well placed

in the model showing strong significance as the cutoff statistic is at 12% to which our

significance is less than 12%. The model forms equation:

Base condition: Adj Close-Model_1-Adj Close-Model_1-1 = -.501 +.002(y1_13-y1_13-1)+

-.001(y1_13-y1_13-2)

Adverse condition: Adj Close-Model_1-Adj Close-Model_1-1 =-.501 +.002(y2_13-y2_13-1)+

-.001(y2_13-y2_13-2)


Severe condition: Adj Close-Model_1-Adj Close-Model_1-1 = -.501+.002(y3_13-y3_13-1)+

-.001(y3_13-y3_13-2)

The model states: The difference in Dow Jones total stock market index (y1_13, y2_13,

y3_13) will increase the difference in GE’s adjusted close by 2 points at lag 0, while the

difference in Dow Jones total stock market index will decrease the difference in GE’s adjusted

close by 1 point at lag 1 in all conditions. For example, if the difference in Dow Jones stock

market index is at 2000, as the index rises at 2000, the difference in GE’s adjusted close will

increase by 2 points instantaneously while the difference in adjusted close will decrease by 1

point the following quarter at lag 1. The model displays no transformation at both lags.

A good model is formed overall with stationary r-squared of 68.4% and Ljung-box

statistic is 86% (see Figure 4). Stationary r-squared explains the total variation from 0% to

100%. Variables y1_13, y2_13, and Y3_13(FDIC Dow Jones total stock market index) are

statistically significant at both lag 0 and lag 1 at .000. The Ljung-Box test is based on the

autocorrelation plot, instead of testing randomness at each distinct lag, it tests the "overall"

randomness based on a number of lags. The null hypothesis indicates a correctly specified model

with a high r-squared indicating the data is random. The alternative hypothesis indicates pattern

left in the model which isn’t good for the model indicating the data is not random. The Ljung-

Box significance was 0.974 for the low variable and 0.865 for the adjusted close variable, which

are both very high and indicates we can be confident that our model is correctly specified. As a

result of the 0.974 and 0.865 significance level, we fail to reject the null hypothesis of

randomness as very little pattern is shown forming a good model with high percentage of

stationary r-squared distribution.

Figure 4: Adj. Close Stock Price


D. ACF and PACF

By viewing the autocorrelation function (ACF) and partial autocorrelation function

(PACF) of the residuals for both dependent variables, it can be seen that the residuals do not go

beyond the upper or lower control limits. This indicates that none of the residuals display

significant autocorrelation. There is also no pattern among the residuals (no lasting positive or

negative trend) so we can conclude that the residuals for both models display independence.

Figure 5: Low Stock Price ACF and PACF


Figure 6: Adj. Close Stock Price ACF and PACF

E. Forecast

The forecast for GE’s Low and Adj. Close stock price for year 2015-2017 are both shown

on Graph 3& 4. The Low stock price in the four quarters of 2014 presented a slight variation in

price in both quarter one and quarter four, the price seems to be very similar but has a n ever so

slight increase in quarter two, followed by a very slight decrease until it ends in quarter four at

about the same price it started in quarter one. If the future economy is similar to the economy of

2014 then the price is expected to be almost the same showing a flat line in Graph 3. However,

notice that in the case of an adverse economy the price deteriorates and drops so low it becomes

negative, similarly, in a severe economic event the stock price dips way beyond a dollar. In this

scenario GE’s stock is bearish, or is forecasted to experience extreme losses.

The Adj. Close stock price in Graph 4 shows to be performing well in the market, also

referred to as bullish, in stock market terms. If the market continues to be the same in the future,

the stock will continue to do well and would increase in price, as forecasted. However, in case of


an adverse economic event the stock price drops from ~$20.00 down to ~$6.00. Shareholders

including GE will experience big losses if this happens. Notice that in the severe economic

condition, both Low and Adj. Close price becomes negative and start to pick up and rise again

towards the end of Q1 of 2016. One speculation of this is that the market starts to get better,

positively affecting the independent variables used to predict Low and Adj. Close stock price,

thus, GE’s stock price starts to climb back up.

Graph 3: Low Stock Price Forecast

Graph 4: Adj. Close Stock Price Forecast


F. UCL and LCL

LCL (lower control limit) and UCL (upper control limit) are important tools for statistical

process control or quality control. They are indicators of whether any variation in the process is

natural or caused by an abnormal event within time. The UCL shows the maximum value that is

statistically reasonable and the LCL indicates the minimum reasonable value. The term

“reasonable” can be best defined as most likely to occur given natural variation. Based upon the

historical analysis for variable Low (Graph 5), variations are seen in the years of 2003, 2006,

2007, and 2009-2014, yet the UCL and LCL follow a similar pattern to that of the observed

value. Adjusted Close (Graph 6) appears to have an identical analysis with the same indications

for UCL and LCL for the same years. Forecasted values show UCL and LCL are extremely

higher and lower than the expected forecasted values for the years of 2015-2017 for both

variables (Figure 7 & 8). These points do not fall within the predicted limits, showing that the

system may be unstable since it has changed significantly from the predictive model. However,

when dealing with UCL and LCL, caution must be taken when drawing conclusions of

something being wrong during that time. Overall, the variations in UCL and LCL for both

variables follow the same pattern as the observed values indicating usual probability distribution

until the forecasted values take place.


Graph 5: Low UCL and LCL

Figure 7: Low UCL and LCL

Graph 6: Adjusted Close UCL and LCL


Figure 8: Adjusted Close UCL and LCL

Conclusion


To conclude, General Electric is a large multifaceted, cross national company with strong

influence in multiple industries. The research conducted and explained throughout this paper was

partially inspired by Matthew Richardson’s and James Stock’s publication titled “Drawing

Inferences from Statistics Based on Multi-Year Asset Returns”. Richardson and Stock were

searching for empirical evidence that stock prices would revert back to the original mean over a

predetermined period of time. They were unable to prove their hypothesized outcome.

This research paper focused on GE, furthers past research by determining correlational

support by statistical significance between econometric variables and stock market performance

indicators. The intention of this research was to answer the following question. Which two stock

market variables have the highest significance to econometric variables? In essence this research

provides empirical evidence to support which aspects of a stock price should be monitored to

determine the long term potential effect of base, adverse and severe economic conditions. It was

hypothesized that the closing price of the stock and the daily trading “high” would be closely

positively correlated with the predetermined economic variables, deeming them the most reliable

dependent variables. The hypothesis was disapproved due to the fact the adjusted close and stock

low were the most significant relevant variables correlated with the econometric variables used.

The research showed how the adjusted close and stock low were effected in base, adverse and

severe economic conditions. As expected, the dependent variables displayed the best

performance during the base scenario and the worst during severe economic conditions. During

severe economic conditions (Graph 3& Graph 4) the variables are devalued beyond zero. The

negative number represents an absence of value gain related to the stock price and can be

correlated with the amount of time it takes for the company to adjust and recover from

undesirable economic conditions. The amount of time needed for independent variable values to


increase, recover or become stabilized are also affected by the actions taken by GE’s executive

board in response to undesirable economic influences.

There are several approaches that GE can take to minimize loss during severe economic

conditions. It is suggested that GE decreases capital expenditures, increases the interest rate

associated with credit extended to customers, decrease workforce, cross train employees and re-

segmented data to improve target marketing performance. This research could be expanded upon

by conducting an analysis to determine which one of the suggestions listed above would have the

highest return on invest for GE during severe economic conditions. It has been assumed that the

data provided for the three economic conditions could be used to accurately represent the effects

of the different market conditions. There has also been an assumption that all stock market data

collected about GE from reputable websites and information providers is accurate and true. This

research is limited by assumptions, it is believed that all assumptions are supported by factual

information.

Appendix A

Model Description

Model Type


Model ID Low Model_1 ARIMA(0,1,0)(0,0,0)

Forecast

Model

Q1

201

5

Q2

201

5

Q3

201

5

Q4

201

5

Q1

201

6

Q2

201

6

Low-Model_1 Forecast 24.

2

24.

1

24.

0

23.

9

23.

9

23.

8

UCL 27.

3

28.

4

29.

2

30.

0

30.

6

31.

3

LCL 21.

2

19.

8

18.

8

17.

9

17.

1

16.

4

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