Investigating the effect of Company Announcements on their Share Price following COVID-19 (using the S&P 500)

    	
            # The ' from ... import ' structure here allows us to only import the module ' python_version ' from the library ' platform ': from platform import python_version print("This code runs on Python version " + python_version())
        
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            This code runs on Python version 3.7.7
        
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import eikon as ek
 
# The key is placed in a text file so that it may be used in this code without showing it itself:
eikon_key = open("eikon.txt","r")
ek.set_app_key(str(eikon_key.read()))
# It is best to close the files we opened in order to make sure that we don't stop any other services/programs from accessing them if they need to:
eikon_key.close()

        
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# datetime will allow us to manipulate Western World dates
import datetime
 
# dateutil will allow us to manipulate dates in equations
import dateutil

        
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import numpy
print("The numpy library imported in this code is version: " + numpy.__version__)

        
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            The numpy library imported in this code is version: 1.18.2
        
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import pandas
# This line will ensure that all columns of our dataframes are always shown:
pandas.set_option('display.max_columns', None)
print("The pandas library imported in this code is version: " + pandas.__version__)

        
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            The pandas library imported in this code is version: 1.0.3
        
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import matplotlib
# the use of ' as ... ' (specifically here: ' as plt ') allows us to create a shorthand for a module (here: ' matplotlib.pyplot ')
import matplotlib.pyplot as plt
print("The matplotlib library imported in this code is version: " + matplotlib.__version__)

        
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            The matplotlib library imported in this code is version: 3.2.1
        
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def plot1ax(dataset, ylabel = "", title = "", xlabel = "Year",
            datasubset = [0], # datasubset needs to be a list of the number of each column within the dtaset that needs to be labelled on the left
            datarange = False, # If wanting to plot graph from and to a specific point, make datarange a list of start and end date
            linescolor = False, # This needs to be a list of the color of each vector to be plotted, in order they are shown in their dataframe from left to right
            figuresize = (12,4), # This can be changed to give graphs of different proportions. It is defaulted to a 12 by 4 (ratioed) graph
            facecolor="0.25",# This allows the user to change the background color as needed
            grid = True,  # This allows us to decide whether or not to include a grid in our graphs
            time_index = [], time_index_step = 48, # These two variables allow us to dictate the frequency of the ticks on the x-axis of our graph
            legend = True):

        
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# Defining the ' daily_df ' variable before the ' Get_Daily_Close ' function
daily_df = pandas.DataFrame()
 
def Get_Daily_Close(instrument, # Name of the instrument in a list.
                    days_back, # Number of days from which to collect the data.
                    plot_title = False, # If ' = True ', then a graph of the data will be shown.
                    plot_time_index_step = 30 * 3, # This line dictates the index frequency on the graph/plot's x axis.
                    col = ""): # This can be changed to name the column of the merged dataframe.

        
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            def Get_Announcement_For_Index(index_instrument, periods_back, show_df = False, show_list = False):
        
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COVID_start_date = datetime.datetime.strptime("2020-02-20", '%Y-%m-%d').date()
days_since_COVID = (datetime.date.today() - COVID_start_date).days

        
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            index_Announcement_df, index_Announcement_list = Get_Announcement_For_Index(index_instrument = ["0#.SPX"],periods_back = 3, show_df = False,show_list = False)
        
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Announcement_COVID_date = []
for k in (1,2,3):
    index_Instruments_COVID_date = []
    index_Announcement_post_COVID_list = []
    for i in range(len(index_Announcement_list)):
        index_Instrument_COVID_date = []
        for j in reversed(index_Announcement_list[i].iloc[:,1]):
            try: # Note that ' if (index_Announcement_list[i].iloc[1,1] - COVID_start_date).days >= 0: ' would not work
                if (datetime.datetime.strptime(index_Announcement_list[i].iloc[:,1].iloc[-1], '%Y-%m-%d').date() - COVID_start_date).days >= 0:
                    while len(index_Instrument_COVID_date) == 0:
                        if (datetime.datetime.strptime(j, '%Y-%m-%d').date() - datetime.datetime.strptime("2020-02-20", '%Y-%m-%d').date()).days >= 0:
                            index_Instrument_COVID_date.append(j)
                else:
                    index_Instrument_COVID_date.append("NaT")
            except:
                index_Instrument_COVID_date.append("NaT")
        index_Instruments_COVID_date.append(index_Instrument_COVID_date[0])
    Instruments_Announcement_COVID_date = pandas.DataFrame(index_Instruments_COVID_date, index = index_Announcement_df.Instrument.unique(), columns = ["Date"])
    Instruments_Announcement_COVID_date.Date = pandas.to_datetime(Instruments_Announcement_COVID_date.Date)
    Announcement_COVID_date.append(Instruments_Announcement_COVID_date)

        
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Instruments_Income_Statement_Announcement_COVID_date = Announcement_COVID_date[0]
Instruments_Income_Statement_Announcement_COVID_date.columns = ["Date of the First Income Statement Announced after COVID"]

        
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Instruments_Cash_Flow_Statement_Announcement_COVID_date = Announcement_COVID_date[1]
Instruments_Cash_Flow_Statement_Announcement_COVID_date.columns = ["Date of the First Cash Flow Statement Announced after COVID"]

        
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Instruments_Balance_Sheet_COVID_date = Announcement_COVID_date[2]
Instruments_Balance_Sheet_COVID_date.columns = ["Date of the First Balance Sheet Announced after COVID"]

        
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for i in index_Announcement_df.iloc[:,0].unique():
    Get_Daily_Close(i, days_back = days_since_COVID)

        
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removing = [i.split()[0]  + " Close Price" for i in daily_df.iloc[0,:][daily_df.iloc[0,:].isna() == True].index]
print("We will be removing " + str(removing) + " from our dataframe")

        
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            ['CARR.N Close Price', 'OTIS.N Close Price']
        
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# This line removes instruments that wera added midway to the index
daily_df_no_na = daily_df.drop(removing, axis = 1).dropna()

        
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daily_df_trend = pandas.DataFrame(columns = daily_df_no_na.columns)
for i in range(len(pandas.DataFrame.transpose(daily_df_no_na))):
    daily_df_trend.iloc[:,i] = daily_df_no_na.iloc[:,i] - daily_df_no_na.iloc[0,i]

        
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datasubset_list = []
for i in range(len(daily_df_no_na.columns)):
    datasubset_list.append(i)

        
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plot1ax(dataset = daily_df_no_na,
        ylabel = "Close Price",
        title = "Index Constituents' Close Prices",
        xlabel = "Date",
        legend = False,
        datasubset = datasubset_list)

        
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plot1ax(dataset = daily_df_trend, legend = False,
        ylabel = "Normalised Close Price",
        title = "Index Constituents' Change in Close Prices",
        datasubset = datasubset_list, xlabel = "Date")

        
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# pip install pickle-mixin
import pickle
 
pickle_out = open("SPX.pickle","wb")
pickl = (COVID_start_date, days_since_COVID,
         index_Announcement_df, index_Announcement_list,
         Announcement_COVID_date,
         Instruments_Income_Statement_Announcement_COVID_date,
         Instruments_Cash_Flow_Statement_Announcement_COVID_date,
         Instruments_Balance_Sheet_COVID_date,
         daily_df, daily_df_no_na,
         daily_df_trend, datasubset_list)
pickle.dump(pickl, pickle_out)
pickle_out.close()

        
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# pickle_in = open("pickl.pickle","rb")
# COVID_start_date, days_since_COVID, index_Announcement_df, index_Announcement_list, Announcement_COVID_date, Instruments_Income_Statement_Announcement_COVID_date, Instruments_Cash_Flow_Statement_Announcement_COVID_date, Instruments_Balance_Sheet_COVID_date, daily_df, daily_df_no_na, daily_df_trend, datasubset_list = pickle.load(pickle_in)

        
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# This is just to delimitate between the code before and after this point
daily_df2 = daily_df_no_na

        
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date_in_date_format = []
for k in range(len(daily_df2)):
    date_in_date_format.append(daily_df2.index[k].date())
daily_df2.index = date_in_date_format

        
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daily_df2_instruments = []
for i in daily_df2.columns:
    daily_df2_instruments.append(str.split(i)[0])

        
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daily_df2_post_COVID_announcement = pandas.DataFrame()
for i,j in zip(daily_df2.columns, daily_df2_instruments):
    daily_df2_post_COVID_announcement = pandas.merge(daily_df2_post_COVID_announcement,
                                                     daily_df2[i][daily_df2.index >= Instruments_Income_Statement_Announcement_COVID_date.loc[j].iloc[0].date()],
                                                     how = "outer", left_index = True, right_index = True) # Note that the following would not work: ' daily_df2_post_COVID_announcement[i] = daily_df2[i][daily_df2.index >= Instruments_Income_Statement_Announcement_COVID_date.loc[j].iloc[0].date()] '

        
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daily_df2_post_COVID_announcement_trend = pandas.DataFrame()
for i in daily_df2.columns:
    try:
        daily_df2_post_COVID_announcement_trend = pandas.merge(daily_df2_post_COVID_announcement_trend,
                                                               daily_df2_post_COVID_announcement.reset_index()[i].dropna().reset_index()[i] - daily_df2_post_COVID_announcement.reset_index()[i].dropna().iloc[0],
                                                               how = "outer", left_index = True, right_index = True)
    except:
        daily_df2_post_COVID_announcement_trend[i] = numpy.nan

        
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plot1ax(dataset = daily_df2_post_COVID_announcement_trend,
        ylabel = "Normalised Close Price",
        title = "Index Constituents' Trend In Close Prices From There First Income Statement Announcement Since COVID\n" + 
                "Only companies that announced an Income Statement since the start of COVID (i.e.:" + str(COVID_start_date) + ") will show",
        xlabel = "Days since first Post-COVID-Announcement",
        legend = False, # change to "underneath" to see list of all instruments and their respective colors as per this graph's legend.
        datasubset = datasubset_list)

        
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COVID_priced_in = [[],[],[]]
for i in daily_df2_post_COVID_announcement_trend.columns:
    if str(sum(daily_df2_post_COVID_announcement_trend[i].dropna())) != "nan":
        if numpy.mean(daily_df2_post_COVID_announcement_trend[i].dropna()) < 0:
            COVID_priced_in[0].append(str.split(i)[0])
        if numpy.mean(daily_df2_post_COVID_announcement_trend[i].dropna()) == 0:
            COVID_priced_in[1].append(str.split(i)[0])
        if numpy.mean(daily_df2_post_COVID_announcement_trend[i].dropna()) > 0:
            COVID_priced_in[2].append(str.split(i)[0])
COVID_priced_in = pandas.DataFrame(COVID_priced_in, index = ["Did not have the negative impact of COVID priced in enough",
                                                             "Had the effects of COVID priced in (or didn't have time to react to new company announcements)",
                                                             "Had a price that overcompensated the negative impact of COVID"])
COVID_priced_in

        
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ESector, err = ek.get_data(instruments = [i.split()[0] for i in daily_df2_post_COVID_announcement_trend.dropna(axis = "columns", how = "all").columns],
                           fields = ["TR.TRBCEconSectorCode",
                                     "TR.TRBCBusinessSectorCode",
                                     "TR.TRBCIndustryGroupCode",
                                     "TR.TRBCIndustryCode",
                                     "TR.TRBCActivityCode"])
 
ESector["TRBC Economic Sector"] = numpy.nan
ESector_list = [[],[],[],[],[],[],[],[],[],[]]
Sectors_list = ["Energy", "Basic Materials", "Industrials", "Consumer Cyclicals",
                "Consumer Non-Cyclicals", "Financials", "Healthcare",
                "Technology", "Telecommunication Services", "Utilities"]
 
for i in range(len(ESector["TRBC Economic Sector Code"])):
    for j,k in zip(range(0, 10), Sectors_list):
        if ESector.iloc[i,1] == (50 + j):
            ESector.iloc[i,6] = k
            ESector_list[j].append(ESector.iloc[i,0])
 
ESector_df = numpy.transpose(pandas.DataFrame(data = [ESector_list[i] for i in range(len(ESector_list))],
                                              index = Sectors_list))
 
ESector_df_by_Sector = []
for k in Sectors_list:
    ESector_df_by_Sector.append(numpy.average([numpy.average(daily_df2_post_COVID_announcement_trend[i + " Close Price"].dropna()) for i in [j for j in ESector_df[k].dropna()]]))
 
ESector_average = pandas.DataFrame(data = ESector_df_by_Sector,
                                   columns = ["Average of Close Prices Post COVID Announcement"],
                                   index = Sectors_list)

        
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            ESector_average
        
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Sector_Average = []
for k in ESector_average.index:
    Sector_Average1 = []
    for j in range(len(pandas.DataFrame([daily_df2_post_COVID_announcement_trend[i + " Close Price"].dropna() for i in ESector_df[k].dropna()]).columns)):
        Sector_Average1.append(numpy.average(pandas.DataFrame([daily_df2_post_COVID_announcement_trend[i + " Close Price"].dropna() for i in ESector_df[k].dropna()]).iloc[:,j].dropna()))
    Sector_Average.append(Sector_Average1)

        
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            Sector_Average = numpy.transpose(pandas.DataFrame(Sector_Average, index = ESector_average.index))
        
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plot1ax(dataset = Sector_Average, ylabel = "Price Movement",
        title = "Index Constituents' Trend In Close Prices From There First Income Statement Announcement Since COVID Sorted By Sector\n" + 
                "Only companies that announced an Income Statement since the start of COVID (i.e.:" + str(COVID_start_date) + ") will show",
        xlabel = "Trading Day", legend = "underneath",
        datasubset = [i for i in range(len(Sector_Average.columns))])

        
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