1
Analyzing Highs and Lows of Aerospace Companies
to Predict Future Stock Prices
IBIS Personal Code: hsr292
Candidate Session Number: 002203-0167
Date: February 16, 2021
, 2
Introduction
The stock market, notorious for continuous fluctuations in stock prices, is dependent on stockholders'
sentiments which shift between the fear of losses and greed for profits. While investing in the stock market
carries risk, well-informed trades allow individuals to gain profits on their stocks. A stock or share is a financial
asset representing partial ownership of a company held by individuals and auctioned for a value higher, equal or
less than the original buying price (Hayes, 2020). A stock's value can be influenced by comprehensible
determinants such as economic growth, legislation and national policy changes, and abstract factors such as the
confidence of investors and the bandwagon effect. However, what if equations that model and extrapolate stock
prices could be calculated? Modelling the long-term stock prices would allow investors to deduce
generalizations for particular companies and industries, allowing an understanding of how certain factors
affected the companies' stock value and investor's behaviours. Applying concepts of functions, statistics and
calculus, polynomial and linear regressions can be derived to model fluctuating stocks to obtain an extrapolation
for future values, with some limitations to their reliability.
The stock prices of two competing companies, Boeing and Airbus, are modelled and extrapolated in
this investigation. Founded in Washington, The Boeing Company is the second-largest global aerospace
manufacturer, whose stock is included in the Dow Jones Industrial Average (Wikipedia contributors, 2021a).
Airbus SE is a European corporation that designs, manufactures and sells civil and military aerospace products
and, as of 2019, is the world's largest airline manufacturer (Wikipedia contributors, 2021b). Comparing these
competing stocks, which dominate the aerospace industry, will allow an analysis of the interconnectedness
between their stock values and deduce a general understanding of the aerospace sector's value.
Rationale
Having been introduced to the prevalence of the stock market by my father, I had always been intrigued
to understand if stock prices for coming days could be predicted based on previous trends or any public
announcement made by the company. Investing in the stock market also served as my families' side hustle. Since
we are an immigrant family, these hustles allow my family to build our savings for my university and tuition
costs. Hence, business channels and news are played on TV every night, and by watching these and having
discussions with my father, I understood the basics of stock markets and investing. Thus, I started recording my
father's trades with our mutual funds, consisting of Microsoft and Google’s stocks, and other stocks which
belonged to the airline and energy sectors. With the mutual fund stocks remaining at an approximate constant
price, while the airline sector's stocks fluctuated unexpectedly, I understood that public sentiment performed a
part in certain stocks' highly volatile nature. Thus, I intend to explore the relationship between public opinion
and stock value by modelling and analyzing Boeing and Airbus' stock prices. In doing so, I will be able to find
some investing strategies for myself and also predict stock prices for the next couple of days.
Aim
This investigation aims to derive a model that satisfies the fluctuations in Boeing and Airbus' stock
prices through the application of functions and statistics with the use of online graphing and modelling tools
such as Desmos and GDC to understand the relationship between public sentiment and stock prices, extrapolate
stock prices for six business days and determine some investing strategies.
, 3
Exploration
An effective interpretation of a stock's growth trend depends on the amount of raw data and sampling
method. The last five years' sample data will allow for a comprehensive understanding of the companies' stock
trends over an extended period and how they were affected by factors such as national policy changes and
positive business reports. Hence, the previous five years' stock prices, from February 2016 to January 2021, for
The Boeing Company and Airbus SE are considered. Rather than dealing with all business days of the last five
years, a monthly stock price is collected using a sampling method.
The sampling method includes:
1) Recording the closing stock price of the first day of each month, from February 2016 to January 2021, from
yahoo finance.
2) If the first day of the month was not a working day, then the previous day's closing stock value was recorded.
The Boeing Stock
A section of the raw data is shown below:
Date Number of months from February 2016 Stock Price ($ USD)
February 2016 1 118.18
March 2016 2 126.94
... ... ...
December 2020 59 214.06
January 2021 60 206.79
With Desmos Graphing Calculator, the complete raw data shown in Appendix A, is plotted below
Graph 1: Boeing Stock Value (Raw Data Based) from February 2016 to January 2021
{𝑚 | 𝑚 > 0, 𝑚 ϵ 𝑍} 𝑚 − 𝑎𝑥𝑖𝑠
While the data points in the domain 24 ≤ 𝑚 ≤ 45 and 54 ≤ 𝑚 ≤ 58 , where 𝑚 denotes the number
of months from February 2016, show tremendous variation, a distinguishable trend of the graph increasing to a
maximum, decreasing to a minimum and then gradually increasing again is observed. The two turning points of
Analyzing Highs and Lows of Aerospace Companies
to Predict Future Stock Prices
IBIS Personal Code: hsr292
Candidate Session Number: 002203-0167
Date: February 16, 2021
, 2
Introduction
The stock market, notorious for continuous fluctuations in stock prices, is dependent on stockholders'
sentiments which shift between the fear of losses and greed for profits. While investing in the stock market
carries risk, well-informed trades allow individuals to gain profits on their stocks. A stock or share is a financial
asset representing partial ownership of a company held by individuals and auctioned for a value higher, equal or
less than the original buying price (Hayes, 2020). A stock's value can be influenced by comprehensible
determinants such as economic growth, legislation and national policy changes, and abstract factors such as the
confidence of investors and the bandwagon effect. However, what if equations that model and extrapolate stock
prices could be calculated? Modelling the long-term stock prices would allow investors to deduce
generalizations for particular companies and industries, allowing an understanding of how certain factors
affected the companies' stock value and investor's behaviours. Applying concepts of functions, statistics and
calculus, polynomial and linear regressions can be derived to model fluctuating stocks to obtain an extrapolation
for future values, with some limitations to their reliability.
The stock prices of two competing companies, Boeing and Airbus, are modelled and extrapolated in
this investigation. Founded in Washington, The Boeing Company is the second-largest global aerospace
manufacturer, whose stock is included in the Dow Jones Industrial Average (Wikipedia contributors, 2021a).
Airbus SE is a European corporation that designs, manufactures and sells civil and military aerospace products
and, as of 2019, is the world's largest airline manufacturer (Wikipedia contributors, 2021b). Comparing these
competing stocks, which dominate the aerospace industry, will allow an analysis of the interconnectedness
between their stock values and deduce a general understanding of the aerospace sector's value.
Rationale
Having been introduced to the prevalence of the stock market by my father, I had always been intrigued
to understand if stock prices for coming days could be predicted based on previous trends or any public
announcement made by the company. Investing in the stock market also served as my families' side hustle. Since
we are an immigrant family, these hustles allow my family to build our savings for my university and tuition
costs. Hence, business channels and news are played on TV every night, and by watching these and having
discussions with my father, I understood the basics of stock markets and investing. Thus, I started recording my
father's trades with our mutual funds, consisting of Microsoft and Google’s stocks, and other stocks which
belonged to the airline and energy sectors. With the mutual fund stocks remaining at an approximate constant
price, while the airline sector's stocks fluctuated unexpectedly, I understood that public sentiment performed a
part in certain stocks' highly volatile nature. Thus, I intend to explore the relationship between public opinion
and stock value by modelling and analyzing Boeing and Airbus' stock prices. In doing so, I will be able to find
some investing strategies for myself and also predict stock prices for the next couple of days.
Aim
This investigation aims to derive a model that satisfies the fluctuations in Boeing and Airbus' stock
prices through the application of functions and statistics with the use of online graphing and modelling tools
such as Desmos and GDC to understand the relationship between public sentiment and stock prices, extrapolate
stock prices for six business days and determine some investing strategies.
, 3
Exploration
An effective interpretation of a stock's growth trend depends on the amount of raw data and sampling
method. The last five years' sample data will allow for a comprehensive understanding of the companies' stock
trends over an extended period and how they were affected by factors such as national policy changes and
positive business reports. Hence, the previous five years' stock prices, from February 2016 to January 2021, for
The Boeing Company and Airbus SE are considered. Rather than dealing with all business days of the last five
years, a monthly stock price is collected using a sampling method.
The sampling method includes:
1) Recording the closing stock price of the first day of each month, from February 2016 to January 2021, from
yahoo finance.
2) If the first day of the month was not a working day, then the previous day's closing stock value was recorded.
The Boeing Stock
A section of the raw data is shown below:
Date Number of months from February 2016 Stock Price ($ USD)
February 2016 1 118.18
March 2016 2 126.94
... ... ...
December 2020 59 214.06
January 2021 60 206.79
With Desmos Graphing Calculator, the complete raw data shown in Appendix A, is plotted below
Graph 1: Boeing Stock Value (Raw Data Based) from February 2016 to January 2021
{𝑚 | 𝑚 > 0, 𝑚 ϵ 𝑍} 𝑚 − 𝑎𝑥𝑖𝑠
While the data points in the domain 24 ≤ 𝑚 ≤ 45 and 54 ≤ 𝑚 ≤ 58 , where 𝑚 denotes the number
of months from February 2016, show tremendous variation, a distinguishable trend of the graph increasing to a
maximum, decreasing to a minimum and then gradually increasing again is observed. The two turning points of
