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