#!/usr/bin/env python

# coding: utf-8

# # Financial Analysis of the Impact of COVID-19 on the Banking Sector

#

#

# In this project, we'll be using the stock returns of various banks as a metric for the Financial Health of the Banking Sector during the COVID-19 pandemic.

# ___

# *Since the banking industry is primarily an oligopoly, the stock performance of the following major companies will be used to determine the overall health of the industry:*

#

# * Bank of America

# * Citigroup

# * Goldman Sachs

# * JP Morgan Chase

# * Morgan Stanley

# * Wells Fargo

# * PNC Financial Services

# * The Charles Schwab Corporation

# * U.S. Bancorp

# * Truist Financial Corporation

#

# *These Banking and Financial Services Corporations were chosen primarily because they showed the most change in recent times*

# First, we import our data visualization libraries

# In[26]:

from pandas_datareader import data, wb

import pandas as pd

import numpy as np

import seaborn as sns

import datetime

import matplotlib.pyplot as plt

get_ipython().run_line_magic('matplotlib', 'inline')

import plotly

import cufflinks as cf

cf.go_offline()

# # Importing and Organizing the Data

#

# **We'll be looking at the stock performance of these 10 companies for this year, with primary focus on the impact of the major spreading of the pandemic during the month of March, 2020 from which we'll get from Yahoo Finance**

# *Note: This notebook was last updated on July 12th, 2020*

# In[3]:

start = datetime.datetime(2020,1,1)

end = datetime.datetime(2020,7,12)

# In[5]:

#Bank of America

BAC = data.DataReader("BAC", 'yahoo', start, end)

#Citigroup

C = data.DataReader("C", 'yahoo', start, end)

#Goldman Sachs

GS = data.DataReader("GS", 'yahoo', start, end)

#JP Morgan Chase

JPM = data.DataReader("JPM", 'yahoo', start, end)

#Morgan Stanley

MS = data.DataReader("MS", 'yahoo', start, end)

#Wells Fargo

WFC = data.DataReader("WFC", 'yahoo', start, end)

#PNC

PNC = data.DataReader("PNC", 'yahoo', start, end)

#Charles Schwab

SCHW = data.DataReader("SCHW", 'yahoo', start, end)

#U.S. Bancorp

USB = data.DataReader("USB", 'yahoo', start, end)

#Truist

TFC = data.DataReader("TFC", 'yahoo', start, end)

# With our data, we'll combine each of the individual stock prices into one big DataFrame called bank_stocks, with each company having their own Ticker and Stock Information

# In[8]:

tickers = ['BAC','C','GS','JPM','MS','WFC','PNC','SCHW','USB','TFC']

bank_stocks = pd.concat([BAC,C,GS,JPM,MS,WFC,PNC,SCHW,USB,TFC], axis = 1, keys=tickers)

bank_stocks.columns.names = ['Bank Ticker','Stock Info']

#Previewing our data to make sure everything is correct

bank_stocks.head()

# # Visualizing Price Change

#

# Now that we have our stock data, let's visualize it to get a better idea of how well these companies, and as a result, most of sector is doing.

#

# First, we graph the closing price of the stocks of each of the 10 companies using an interactive graph.

#

# ___

# On March 11th, the World Health Organization (WHO) officially declared COVID-19 as a **Global Pandemic**. And as a result, if you look at the prices from the end of Febuary to April, there's an overal decline which makes sense as everyone started to really panick at this new global pandemic.

#

# However, we can also see that from April to July, the prices seem to slightly increase at a very small rate. This is likely because, after the initial shock of the Pandemic as well as most countries being locked down in quarantine for a couple months, everyone began adjusting to working from home and having online meetings.

#

# ___

# The majority of the companies' stock index remained fairly constant after April, except for Goldman Sachs (**GS**) having a slight increase.

# In[42]:

bank_stocks.xs(key='Close', axis=1, level='Stock Info').iplot(title="Stock Price over Time in 2020")

# And we can look at the increase in Goldman Sachs' stock price by comparing it to a 15-Day Moving Average (Which is just an average of the stock prices for consecutive 15 day periods).

# In[56]:

plt.figure(figsize=(8,4))

GS['Close'].loc['2020-01-01':'2020-07-12'].rolling(window=15).mean().plot(label='15-Day Moving Average')

GS['Close'].loc['2020-01-01':'2020-07-12'].plot(label='GS Close')

plt.legend()

plt.title("Stock Index and Moving Average of Goldman Sachs")

plt.ylabel("Stock Price")

plt.show()

# Out of the 10 companies, it's had the highest increase in stock prices since the begining of April.

# # Analyzing Returns

#

# Now that we've analyzed total amounts of Stock Prices, we now want to analyze the change in prices, or in other words, the returns you would get if you invested in a certain company which are a good measure of how that company is performing.

#

# The $Return$ on any stock at time $t$ is defined as the percent change of the initial $Price_{0}$ plus any Dividend $D$

# by the final $Price_{t}$

#

# $$R_t = \frac{P_t - P_{0} + D}{P_{0}}$$

#

#

# We'll be creating a new DataFrame which we'll use to graph the returns using the Closing Price. For now, we'll ignore Dividends which shouldn't change the overall shape of our data.

# In[60]:

returns = pd.DataFrame()

for tick in tickers:

#Using pct_change to take the percent change of the closing price for the stock of each of the 10 companies

returns[tick+' Returns'] = bank_stocks[tick]['Close'].pct_change()

# And using our returns data, we can figure out the dates when each company had the lowest returns:

# In[61]:

returns.idxmin()

# As you can see, from March 9th-March 16th was when these major banking companies had their **worst stock performances of the year** which makes sense, because March 11th was when the Coronavirus was labled as a **Global Pandemic**. Also, on March 13th, COVID-19 was offically became a **national emergency** in the United States, which is where a lot of these companies are based in and only added to global panic.

#

# ___

#

# We can also look at how risky these stocks have been throughout the year, and through March to April. For this, we use

# **Standard Deviation** which tells us by how much do the prices deviate from the average. In other words, a higher standard deviation means a **higher volatility** and thus more risky

# In[62]:

#For the entire year, so far, Citigroup has the highest volatility

returns.std()

# In[72]:

#From March to April, Citigroup also has the highest Volatility, which increased from %5.45-->%8.07

returns.loc['2020-03-10':'2020-04-30'].std()

# As we can see, during the time period when panic arised from COVID-19, the volatility of all of the stocks increased, which tells us that they became more risky to trade with. The change in volatility isn't as noticable as in say the 2008 Financial Crisis, but it's still important nonetheless.

# ___

#

# To better understand this idea, here's two distributions of Citigroup's stock price volatility for the entire 2020 year so far compared to during the couple months we just looked at:

# In[104]:

sns.distplot(returns.loc['2020-01-01':'2020-07-12']['C Returns'], color='green', bins=50).set_title('Volatility of Citigroup Stock in 2020')

# In[105]:

sns.distplot(returns.loc['2020-03-10':'2020-04-12']['C Returns'], color='red', bins=50).set_title("Volatility of Citigroup Stock when COVID-19 was declared a Global Pandemic (March-April 2020)")

# As you can see above, when COVID-19 was declared a pandemic, the volatility(and therefore riskiness) of Citigroup stock had a range that was 0.1 greater on both positive and negative returns. This further proves the uncertainty and panic of the time.

# ___

#

# As we saw in the very first graph, Citigroup's stock price has been increasing at a very minimal rate since April, while the other 9 companies we looked at had stock prices that stayed relatively the same ($\pm$ $3-4)

# In[108]:

bank_stocks.xs(key='Close', axis=1, level='Stock Info').plot(title="Stock Price over Time in 2020").legend(loc=2)

# # Conclusion

#

# From the sharp decline in stock index prices when COVID-19 was officially labled a **Global Pandemic** to the higher volatility of returns during the introduction of the Pandemic, we can conclude the Coronavirus has had an overall negative impact on not only the Physical health of hundreds of millions of people, but also of the Financial Health of the Banking Sector