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Add the files for the take-home exam

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Alexander Hess 2022-08-05 00:08:32 +02:00
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# -*- coding: utf-8 -*-
"""
Created on Sat Jul 30 15:20:32 2022
@author: Alexander Hillert, Goethe University
"""
# import packages
import re
# define working directory
# adjust it to your computer
directory = "YOUR DIRECTORY"
# =============================================================================
# Part A: Creating an Overview File on the Call Participants
# =============================================================================
# Create output file
output_csv_file=open(directory+'Problem_1_Overview_Calls.csv','w',encoding="utf-8")
# Write variable names to the first line of the output file
# 1) Call-ID
# 2) Filename
# 3) Fiscal Quarter
# 4) Fiscal Year
# 5) Date of the call in the format YYYYMMDD
# 6) Time of the call, e.g., 05:00 PM GMT
# 7) number of non-corporate call participants
# 8) the names of all corporate participants and their positions -> each item
# should be written in a seperate column
output_csv_file.write('ID;Filename;Fiscal_Quarter;Fiscal_Year;Date;Time;\
#Analysts')
# There can be up to 4 corporate particiapnts
for i in range(1,5):
output_csv_file.write(';Name_'+str(i)+';Position_'+str(i))
output_csv_file.write('\n')
# Open the overfiew file "Overview_File_Problem_1.csv" to call the earnings calls
overview_file=open(directory+'Overview_File_Problem_1.csv','r',encoding="utf-8")
overview_text=overview_file.read()
list_earnings_calls=overview_text.split("\n")
# The last line is empty -> drop it
while list_earnings_calls.count("")>0:
list_earnings_calls.remove("")
# iterate all earnings conference calls
for i in range(1, len(list_earnings_calls)):
# reset the variables
fiscal_quarter=""
fiscal_year=""
date=""
time=""
# we split the entire transcripts into three parts
# its header
header_text=""
# the list of non-corporate participants
participants_text=""
# the list of corporate participants
corporates_text=""
# the number of analysts joining the call
number_analysts=0
# variables for manager name and position
manager_name=""
manager_position=""
manager_position_edited=""
# a list of manager names for part b)
manager_name_list=[]
# get the filename of each earnings call
call_information=list_earnings_calls[i].split(";")
call_id=call_information[0]
filename=call_information[1]
# open the call transcript
call_file=open(directory+'Problem_1_Sample/'+filename,'r',encoding="utf-8")
call_text=call_file.read()
# Get information on the call
# FOr example:
# Q1 2013 Bank of America Corporation Earnings Conference Call
# 04/17/2013 08:30 AM GMT
# the header ends where the list of corporate particpants starts
match_corporates=re.search(TO BE COMPLETED,call_text)
if match_corporates:
header_text=call_text[TO BE COMPLETED]
# get the fiscal quarter and year from the header text
match_fiscal_quarter=re.search(TO BE COMPLETED,header_text)
if match_fiscal_quarter:
fiscal_quarter=match_fiscal_quarter.group(0)
match_fiscal_year=re.search(TO BE COMPLETED,header_text)
if match_fiscal_year:
fiscal_year=match_fiscal_year.group(0)
# get date and time of the call
# date
match_date=re.search(TO BE COMPLETED,header_text)
if match_date:
date=match_date.group(0)
# the date in the output file should be formatted as YYYYMMDD
# so, you need to rearrange the date text
year=date[TO BE COMPLETED]
month=date[TO BE COMPLETED]
day=date[TO BE COMPLETED]
date_formatted=year+month+day
# time
match_time=re.search(TO BE COMPLETED,header_text)
if match_time:
time=match_time.group(0)
# count the number of analysts
# the relevant text part starts with, for example,
# ================================================================================
# Conference Call Participiants
# ================================================================================
#
# * Chris Mutascio
# Keefe, Bruyette & Woods - Analyst
# * Thomas Laturneau
# FBR - Analyst
# and ends with the beginning of the presentation
# ================================================================================
# Presentation
# --------------------------------------------------------------------------------
match_participants=re.search(TO BE COMPLETED,call_text)
match_presentation=re.search(TO BE COMPLETED,call_text)
# if you find both boundaries
if match_participants and match_presentation:
# get the text in between
participants_text=call_text[TO BE COMPLETED]
# split the text of the participants that you have just identified
# in a way that each element refers to one analyst.
analyst_list=participants_text.split(TO BE COMPLETED)
# depending on how you split, you might need re.split()
# check whether you get empty elements and/or elements that do not refer
# to analysts -> remove them
while TO BE COMPLETED>0:
TO BE COMPLETED
# after these steps and checks, the number of analysts is the length of your analyst list
number_analysts=TO BE COMPLETED
# get the names of the corporate participants and their position
# remember that you already have the beginning of corporate participants
# see above at around line 90
# the corporate participants come before the list of non-corporate participants
corporates_text=call_text[TO BE COMPLETED]
# like before, split this text such that one element refers to one corporate participant
corporates_list=corporates_text.split(TO BE COMPLETED)
# check whether you get empty elements and/or elements that do not refer
# to corporate participants -> remove them
while TO BE COMPLETED>0:
TO BE COMPLETED
# write the call information to the output file
output_csv_file.write(str(call_id)+";"+filename+";"+fiscal_quarter+";"+fiscal_year+";"\
+date_formatted+";"+time+";"+str(number_analysts))
# now, we need to add the information on the corporate participants
# go over all corporate participants
for j in range(len(corporates_list)):
# depending on how you split the text of corporate participants,
# one element of your list could contain the name of the mangager in the first
# line and their position in the second line.
# ADJUST THE FOLLOWING COMMANDS IF YOU USED A DIFFERENT SPLIT.
# split each element of the list of corporate participants further
# into name and position
manager_entry=corporates_list[j]
manager_entry_parts=manager_entry.split(TO BE COMPLETED)
manager_name=manager_entry_parts[TO BE COMPLETED]
# for part b) of the problem it is helpful to have a list of all
# manager names. With this list, we can identify whether a statement
# comes from a managers (-> answer) or from an analyst (-> question)
manager_name_list.append(manager_name)
manager_position=manager_entry_parts[TO BE COMPLETED]
# Like before, the template assumes a very specific type of split here
# So depending on your approach, you might need to change the commands below.
# the position is just the text part after " - "
# For example
# Bank of America Corporation - CEO
# the position is "CEO"
manager_position_edited=re.TO BE COMPLETED
# write the manager names and positions to the output file
output_csv_file.write(";"+manager_name+";"+manager_position_edited)
output_csv_file.write("\n")
print("For earnings call "+str(i)+" part a) has been completed.")
# =========================================================================
# Part B: Extracting the Call Segments
# =========================================================================
# set variables
presentation_text=""
qanda_text=""
qanda_list=[]
question_text=""
answer_text=""
# identify the presentation
# the begin of the presentation has already been identified above
# see at around line 140
#
# the presentation ends where the Q and A part begins
# ================================================================================
# Questions and Answers
# --------------------------------------------------------------------------------
match_qanda=re.search(TO BE COMPLETED,call_text)
presentation_text=call_text[TO BE COMPLETED]
# drop operator statements
# search for the beginning of an operator statement
match_operator=re.search(TO BE COMPLETED,presentation_text)
while match_operator:
match_operator_start=match_operator.start()
# search for the end of the operator statement
# Hint: search only after the beginning of the operator statement
# Hint 2: remember to keep track of your coordinates (.start() and .end())
match_operator_end=re.search(TO BE COMPLETED,TO BE COMPLETED)
# keep the text before the operator statement and the text after
# the approach is similar to removing tables (see Problem 4 and 5 from class)
presentation_text=presentation_text[TO BE COMPLETED]
# check whether there is another match
match_operator=re.search(TO BE COMPLETED,presentation_text)
# sometimes there are technical remarks like "(inaudible)", "(corrected by company after the call)",
# or "(technical difficulty)" -> drop those
TO BE COMPLETED
# there are several ways to approach this editing step (e.g., re.sub())
# drop information on the speakers, e.g.,
# -------------------------------------------------------------------------
# Deborah Crawford, Facebook, Inc. - Director of IR [2]
# -------------------------------------------------------------------------
match_speaker=re.search(TO BE COMPLETED,presentation_text)
while match_speaker:
# the task is similar to the Operator statement but be careful
# to only remove the speaker name but NOT the text of the speaker.
presentation_text=presentation_text TO BE COMPLETED
# check whether there is another speaker name
match_speaker=re.search(TO BE COMPLETED,presentation_text)
# write the text of the presentation to an output file
# make sure that the folder "Problem_1_Conference_Call_Segments" exists.
output_file_presentation=open(directory+'Problem_1_Conference_Call_Segments/call_'+str(call_id)+'_presentation.txt',"w",encoding='utf-8')
output_file_presentation.write(presentation_text)
# Close file
output_file_presentation.close()
# -------------------------------------------------------------------------
# identify questions and answers
# -------------------------------------------------------------------------
# you already have the start of the Q&A section (see at around lines 235)
qanda_text=call_text[match_qanda.end():]
# the earnings call transcript ends with definitions
# remove these/keep the text before the definitions
match_definitions=re.search("\n-{1,}\nDefinitions\n-{1,}\n",qanda_text)
if match_definitions:
# keep the text before
qanda_text=qanda_text[TO BE COMPLETED]
# split the Q and A part by speaker
qanda_list=re.split(TO BE COMPLETED,qanda_text)
# variables to count the number of answers
answer_counter=1
# and questions
question_counter=1
# go over all speakers/statements that you obtained from the previous split
# you now have to decide whether the speaker is an analyst (-> question)
# or a corporate participant (-> answer)
for k in range(TO BE COMPLETED):
# identify the speaker name to check whether it is a corporate participant.
# For example
# --------------------------------------------------------------------------------
# Bruce Thompson, Bank of America Corporation - CFO [3]
# --------------------------------------------------------------------------------
#
speaker_text_part=qanda_list[k]
# split the text part of the kth speaker
# into his*her name and the rest
# NOTE: re.search() and re.sub() are also nice ways to accomplish the goal
speaker_text_sub_parts=re.split(TO BE COMPLETED,qanda_list[k])
# get the name of the speaker from the previous split
# in the example above, we need to get "Bruce Thompson"
speaker_name=speaker_text_sub_parts[TO BE COMPLETED]
# depending on your split, you might need some further editing to
# get onyl the name ("Bruce Thompson") without any additional information.
# the second part of speaker_text_sub_parts is (probably) the statement
# of the speaker (again, it depends on your split)
text=speaker_text_sub_parts[TO BE COMPLETED]
# sometimes there are technical remarks like "(inaudible)", "(corrected by company after the call)",
# or "(technical difficulty)" -> drop those
text=TO BE COMPLETED
# there are several ways to approach this editing step (e.g., re.sub())
# check whether the speaker name is in the manager list from part a) (see at around line 195)
if speaker_name in manager_name_list:
# the name of the speaker is in the list of corporate participants
# -> it is a management answer
answer_text=answer_text+"Answer_"+str(answer_counter)+":\n"+text+"\n"
answer_counter=answer_counter+1
else:
# it is either an analyst question or an operator statement
# be careful to check the condition below. depending on how your
# speaker names look like, you may need .count() and/or re.search() instead of .startswith()
if speaker_name.startswith("Operator") or TO BE COMPLETED:
pass
else:
# it is an analyst question
question_text=question_text+"Question_"+str(question_counter)+":\n"+text+"\n"
question_counter=question_counter+1
# write the texts to output files
# make sure that the subfolder exists.
output_file_answers=open(directory+'Problem_1_Conference_Call_Segments/call_'+str(call_id)+'_answers.txt',"w",encoding='utf-8')
output_file_questions=open(directory+'Problem_1_Conference_Call_Segments/call_'+str(call_id)+'_questions.txt',"w",encoding='utf-8')
output_file_answers.write(answer_text)
output_file_questions.write(question_text)
# Close files
output_file_answers.close()
output_file_questions.close()
call_file.close()
# Close files
overview_file.close()
output_csv_file.close()
print("Problem 1 completed.")