A stock market is where buyers and sellers trade shares of a company, and is one of the most popular ways for individuals and companies to invest money. The size of the world stock market is now estimated to be in the trillions. The largest stock market in the world is the New York Stock Exchange (NYSE), located in New York City. About 2,800 companies were listed on the NSYE at the time this data was extracted. In this problem, we'll look at the monthly stock prices of five of these companies: IBM, General Electric (GE), Procter and Gamble, Coca Cola, and Boeing, and create some insights and studies out of it with analysis and visualization
- Load, observe, summarise the data sets
- Analyse the stock price of each Company on a plot chart.
- Observe trends of stock prices from 1970 to 2010.
- Comparison of one company's stock price to the other.
- Impact of 2000 Global Technology bubble in the stock price of each of five ccompanies
- Impact of 1997 Asian Economy Mini Crash Ton the stock price of each of five ccompanies
Installing all required libraries to be used in our analysis and visulization.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inlineLaoding data set of each comapny to start our Analaysis.
IBM = pd.read_csv("IBMStock.csv")
GE = pd.read_csv("GEStock.csv")
PG = pd.read_csv("ProcterGambleStock.csv")
CocaCola = pd.read_csv("CocaColaStock.csv")
Boeing = pd.read_csv("BoeingStock.csv")Lets find the the the maximun, minimum, average and standard deviation of different data set for our understanding.
#Maximum stock price of CocaCola
round(float(CocaCola.StockPrice.max()),2)
#Minmum stock price of PG
round(float(PG.StockPrice.min()),2)
#Mean Stock Price of Boeing
round(float(Boeing.StockPrice.mean()),2)
#Median Stock Price of IBM
IBM.StockPrice.median()
#Standard deviation of stock price of Boeing
round(float(Boeing.StockPrice.std()),2)stock price of CocaCola on the plot chart to observe the trend of its Stock proce over the from 1970 to 2010.
plt.plot(CocaCola.Year, CocaCola.StockPrice,"o")
plt.xlabel("Year")
plt.ylabel("Stock Price")
plt.title ("Stock Price of CocaCola")
plt.show()
PG["Date"] = pd.to_datetime(PG["Date"])
PG["Year"] = PG["Date"].dt.year
Line1, = plt.plot(PG.Year, PG.StockPrice)
Line2, = plt.plot(CocaCola.Year, CocaCola.StockPrice)
plt.xlabel("Year")
plt.ylabel("Stock Price")
plt.title ("Stock Price of CocaCola and PG")
plt.legend([Line1,Line2],["PG","CocaCola"])
plt.show()
In Technogly bubble of 2000, Mnay comapny Stock prices started to crashed and Maany started to rise. We Will see this Impact all five companies.
plt.plot(CocaCola.Year[301:432], CocaCola.StockPrice[301:432],label = "CocaCola")
plt.plot(PG.Year[301:432], PG.StockPrice[301:432],label = "PG")
plt.plot(Boeing.Year[301:432], Boeing.StockPrice[301:432],label = "Boeing")
plt.plot(GE.Year[301:432], GE.StockPrice[301:432],label = "GE")
plt.plot(IBM.Year[301:432], IBM.StockPrice[301:432],label = "IBM")
plt.title ("Stock Price of all comanies during Mini Asian Crash from Sep to Nov in 1997")
plt.xlabel("Month")
plt.ylabel("Stock Price")
plt.legend()
plt.show()
In October of 1997, there was a global stock market crash that was caused by an economic crisis in Asia. Comparing September 1997 to November 1997, Lets observe which companies saw a decreasing trend in their stock price?
plt.plot(CocaCola.Month[332:335], CocaCola.StockPrice[332:335],label = "CocaCola")
plt.plot(PG.Month[332:335], PG.StockPrice[332:335],label = "PG")
plt.plot(Boeing.Month[332:335], Boeing.StockPrice[332:335],label = "Boeing")
plt.plot(GE.Month[332:335], GE.StockPrice[332:335],label = "GE")
plt.plot(IBM.Month[332:335], IBM.StockPrice[332:335],label = "IBM")
plt.title ("Stock Price of all comanies during Mini Asian Crash from Sep to Nov in 1997")
plt.xlabel("Month")
plt.ylabel("Stock Price")
plt.legend()
plt.show()