How to Automate Mergers and Acquisitions Reports: A Guide for Developers
If you’re a developer, especially for an automated platform company, this guide is for you. It walks you through a script we created to generate mergers and acquisitions reports automatically. Creating these reports manually takes a lot of time and energy — something few developers have. However, you can automate the report generation process with AI and some scripting. You can find links to download the script file and related materials at the bottom of this guide.
What you’ll need to run the script
News Data — You should obtain news data from a reliable source. For this guide, we’re getting the data from the Webz.io News API. It provides structured news data feeds in 170+ languages from millions of news sites.
You need an API key to use the Webz.io News API, and you can get one by contacting Webz.io.
- OpenAI API — You’ll use OpenAI’s API to leverage the GPT-4 and DALL·E models. GPT-4 analyzes and summarizes the text from customer reviews, while DALL·E generates a main image for the report.
- You also need an API key for the OpenAI API. Create an account or sign in at OpenAI to get a key. OpenAI uses pay-per-use pricing for its language and image models. You can see the price points on the OpenAI website.
- Python — We’re using Python to automate the report creation process. You’ll need to ensure you can run Python code on your machine.
Automating mergers and acquisitions reports: script breakdown
The script fetches news articles from the Webz.io News API. Next, the script calls the OpenAI API, using its text model to analyze the articles for content related to mergers and acquisitions. It then creates a structured report for each article, outputting its findings into a Word document. The script uses the OpenAI image model to generate a main image for the report. The script combines all these elements into a completed report contained in the Word document.
Here is the detailed breakdown of the script:
Import packages and modules
First, the script imports the Python packages and modules, the OpenAI Python API library, and other necessary components.
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import docx import requests from openai import OpenAI import os import openai from Levenshtein import ratio from docx.shared import Pt from bs4 import BeautifulSoup import io from docx.oxml.shared import OxmlElement, qn from docx.shared import Pt from docx.enum.text import WD_ALIGN_PARAGRAPH |
Set global variable and access API keys
Next the script accesses the API keys (Webz.io and OpenAI) through the development environment. It also includes a global variable where you can set the number of articles the script will generate a report on.
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WEBZ_API_KEY = os.getenv("WEBZ_API_KEY") openai.api_key = os.getenv("OPENAI_API_KEY") NUM_OF_REPORTS = 5 # Set the number of articles the script will generate a report on. client = OpenAI() |
Orchestrate entire process (main)
Towards the end of the script, you’ll see the “main” function. It orchestrates the entire process — from reading the news articles and generating reports for each one to creating a main report image to finalizing the Word document.
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def main(): image_url = generate_article_image() filtered_articles = get_unique_posts_from_webz("""category:"Economy, Business and Finance" num_chars:>1000 language:english published:>now-7d social.facebook.likes:>0 title:acquire""") reports = generate_reports(filtered_articles) intro = generate_intro(reports) title_text = generate_title(intro) create_word_doc("M&A digest.docx", title_text, image_url, intro, reports) if __name__ == "__main__": main() |
Define functions
Now we define the different functions of our script:
Fetch news articles from Webz.io (def fetch_articles)
Makes a call to the Webz.io News API to get articles with content related to mergers and acquisitions.
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# Function to get news articles from Webz.io API def fetch_articles(query, api_key, total): endpoint = f"https://api.webz.io/filterWebContent?token={api_key}&format=json&q={query}&size=100&ts=0" all_posts = [] while total > 0: response = requests.get(endpoint) data = response.json() posts = data["posts"] if len(posts) == 0: break all_posts.extend(posts) total -= len(posts) if total > 0 and "next" in data: endpoint = f"https://api.webz.io{data['next']}" else: break articles = [] for article in all_posts: article = {'title': article["title"], 'text': trim_string(trim_title(article["title"]) + "\n\n" + article["text"], 10000), 'link': article['url'], 'published': article['published']} articles.append(article) return articles |
Remove duplicate posts (get_unique_posts_from_webz)
Removes duplicate posts in case they exist.
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def get_unique_posts_from_webz(query): print("Fetch posts from Webz.io") articles = fetch_articles(query, WEBZ_API_KEY, 100) filtered_articles = remove_similar_strings(articles) return filtered_articles |
Look for similar strings (are_similar)
Checks if two strings are similar based on the Levenshtein ratio.
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def are_similar(str1, str2, threshold=1): """ Check if two strings are similar based on Levenshtein ratio. """ return ratio(str1, str2) > threshold |
Remove similar articles (remove_similar_strings)
Eliminates duplicate content by removing articles based on similar strings.
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def remove_similar_strings(articles): unique_articles = [] for article in articles: if not any(are_similar(article['text'], existing['text'], 0.7) for existing in unique_articles): unique_articles.append(article) return unique_articles |
Trim string (trim_string)
Makes sure a string isn’t longer than max_length .
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def trim_string(string, max_length): if len(string) > max_length: return string[:max_length] else: return string |
Trim article title (trim_title)
Removes irrelevant text from the title. For example, if the real title includes “- CNN News” the script will remove this phrase.
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def trim_title(input_string): words = input_string.split() if "|" in input_string: return input_string.split("|")[0] last_dash_index = input_string.rfind("-") if last_dash_index != -1: right_of_dash = input_string[last_dash_index + 1:] right_words = right_of_dash.split() if len(right_words) <= 3 and len(words) > 10: return input_string[:last_dash_index] return input_string |
Send prompt (call_gpt_completion)
Sends a prompt to the GPT-4 model and receives a response.
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def call_gpt_completion(prompt): return client.chat.completions.create( model="gpt-4-1106-preview", max_tokens=4096, messages=[ {"role": "user", "content": prompt}, ] ) |
Review articles and generate reports (generate_reports)
Goes through articles pulled from the Webz.io News API to determine if any contain content related to mergers and acquisitions. If an article does mention a merger or acquisition, the script generates a report for it.
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def generate_reports(filtered_articles): print("Generating Reports") reports = [] for article in filtered_articles: print(f"Creating report about: {article['title']}") prompt = f"""Carefully review the following news article between the [] brackets and determine if there is an explicit discussion about an M&A deal emerging from its content. The article is as follows: [ {article['text']} ] If the article explicitly mentions or clearly discuss an M&A (merger & acquisition) deal, generate a detailed report in HTML format. Use <B> tags to highlight the titles of each section and <UL> and <LI> tags for listing items. The report should include the following sections: <HTML> <B>Executive Summary</B> <UL><LI>Summarize the main points of the M&A deal: companies involved, deal size, key dates, high-level analysis of the deal's rationale and expected outcomes.</LI></UL> <B>Introduction</B> <UL><LI>Provide a background information about the companies involved, industry context and market conditions leading up to the M&A.</LI></UL> <B>Details of the Deal</B> <UL><LI>Provide a description of the M&A transaction: type, structure, financial terms, timeline, information on deal financing.</LI></UL> <B>Strategic Rationale</B> <UL><LI>Provide a strategic reasons behind the M&A, expected benefits for both companies.</LI></UL> <B>Market Reaction and Analysis</B> <UL><LI>Discuss the market reaction to the announcement, comparison with similar industry deals.</LI></UL> <B>Regulatory and Legal Considerations</B> <UL><LI>Mention any regulatory approvals and antitrust concerns, legal implications and compliance requirements.</LI></UL> <B>Risk Analysis</B> <UL><LI>Discuss any potential risks associated with the deal, risk mitigation strategies.</LI></UL> <B>Financial Analysis</B> <UL><LI>Review the financial metrics and performance indicators, impact on financial statements.</LI></UL> <B>Industry and Competitor Impact</B> <UL><LI>Discuss the effect on the broader industry and market dynamics, impact on competitors.</LI></UL> <B>Conclusion and Recommendations</B> <UL><LI>Provide a summary of key findings and insights, recommendations for stakeholders.</LI></UL> </HTML> If the article does not explicitly mention or discuss an M&A deal, please respond with: can't produce report. """ try: response = call_gpt_completion(prompt) report = {'text': ''} for choice in response.choices: report['text'] += choice.message.content if "Executive Summary" in report['text']: report['link'] = article['link'] report['title'] = article['title'] report['published'] = article['published'] reports.append(report) print(f"Created a report about: {article['title']}") else: print(f"Can't product report for: {article['title']}") if len(reports) == NUM_OF_REPORTS: break except Exception as e: print("An error occurred:", str(e)) return reports |
Generate title (generate_title)
Creates a title for the mergers and acquisitions report.
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def generate_title(intro): print("Creating a title") prompt = "Create a title using the following text as a context:\n" + intro title_text = "" try: response = call_gpt_completion(prompt) for choice in response.choices: title_text += choice.message.content except Exception as e: print("An error occurred:", str(e)) title_text = title_text.strip(" ").strip('\"') if title_text.startswith("Title:"): # Sometimes ChatGPT prefix the title with Title: return title_text[len("Title:"):] return title_text |
Generate introduction (generate_intro)
Generates an introductory paragraph for the report.
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def generate_intro(reports): print("Generate post intro") prompt = """ Write a paragraph introducing a digest that contains M&A reports about the following titles, don't elaborate on these titles: [] The reports are created automatically by using Webz.io news api and ChatGPT. The reports are generated by calling the Webz.io news API for news articles categorized as "Economy, Business and Finance" related to M&A deals. The matching news articles are then run through a ChatGPT prompt to analyze if the article is indeed about an M&A deal. If so it create a structured report. """ prompt = insert_titles_in_text(prompt, reports) intro = "" try: response = call_gpt_completion(prompt) for choice in response.choices: intro += choice.message.content except Exception as e: print("An error occurred:", str(e)) return intro |
HTML to formatted text (html_to_word)
Converts HTML content to formatted text in a Word document. It handles bold text and bullet lists.
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def html_to_word(doc, html_content): soup = BeautifulSoup(html_content, 'html.parser') for element in soup.find_all(['b', 'ul']): if element.name == 'b': # Add bold text as a heading doc.add_paragraph(element.get_text(), style='Heading 2') elif element.name == 'ul': for item in element.find_all('li'): # Add list items doc.add_paragraph(item.get_text(), style='List Bullet') |
Add hyperlink (add_hyperlink)
Inserts a hyperlink into a Word document paragraph.
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def add_hyperlink(paragraph, url, text): """ Add a hyperlink to a paragraph. """ part = paragraph.part r_id = part.relate_to(url, docx.opc.constants.RELATIONSHIP_TYPE.HYPERLINK, is_external=True) hyperlink = OxmlElement('w:hyperlink') hyperlink.set(qn('r:id'), r_id, ) new_run = OxmlElement('w:r') rPr = OxmlElement('w:rPr') u = OxmlElement('w:u') u.set(qn('w:val'), 'single') rPr.append(u) u = OxmlElement('w:u') u.set(qn('w:val'), 'single') rPr.append(u) new_run.append(rPr) new_run.text = text hyperlink.append(new_run) paragraph._p.append(hyperlink) return hyperlink |
Add title placeholder (insert_titles_in_text)
Adds a placeholder for inserting the titles.
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def insert_titles_in_text(text, reports): # Placeholder for inserting the titles placeholder = "[]" # Extracting the titles from the reports and formatting them with new lines titles = "\n".join([report['title'] for report in reports]) # Replacing the placeholder with the titles updated_text = text.replace(placeholder, titles) return updated_text |
Generate image (generate_article_image)
Generates a main image for the report using OpenAI’s DALL-E model with a specific prompt.
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def generate_article_image(): print("Generating post image") image_url = "" try: response = client.images.generate( model="dall-e-3", prompt="Create an image for a cover of a Mergers and Acquisitions report. The scene is set in a modern, spacious corporate boardroom with a large glass table. On the table, neatly arranged, are documents, pens, and digital tablets showing graphs and financial data. In the foreground, two pairs of hands, one male and one female, are in the middle of a handshake, symbolizing a successful deal. In the background, through a large window, we see a city skyline with skyscrapers, implying a high-powered business environment. The color scheme should be a mix of cool blues and warm golds to give a sense of professionalism and success. Incorporate subtle M&A related icons like pie charts, upward arrows, and company logos in a tasteful manner. Ensure the image is suitable for a formal report, with a focus on clarity and corporate aesthetics.", n=1, size="1024x1024" ) image_url = response.data[0].url except Exception as e: print("An error occurred generating the image:", str(e)) return image_url |
Download image (add_image_from_base64)
Downloads an image from a URL and adds it to a Word document.
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def add_image_from_base64(doc, image_url): response = requests.get(image_url) # Check if the request was successful if response.status_code == 200: image_stream = io.BytesIO(response.content) doc.add_picture(image_stream, width=docx.shared.Inches(6)) else: print(f"Failed to download image. Status code: {response.status_code}") |
Create Word doc (create_word_doc)
Assembles the various components into a formatted Word document.
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def create_word_doc(file_name, title_text, image_url, intro, reports): print("Saving to word document") doc = docx.Document() # Add a title title = doc.add_paragraph() title.style = 'Title' title_run = title.add_run(title_text) title_run.font.size = Pt(24) # Set the font size title_run.font.name = 'Arial (Body)' # Set the font title.alignment = WD_ALIGN_PARAGRAPH.CENTER # Center align the title if len(image_url) > 0: add_image_from_base64(doc, image_url) doc.add_paragraph(intro) # Add each report for report in reports: p = doc.add_paragraph(style='Heading 1') add_hyperlink(p, report['link'], report['title']) doc.add_paragraph(f"Published on: {report['published']}") html_to_word(doc, report['text']) doc.add_paragraph(""" """) for paragraph in doc.paragraphs: for run in paragraph.runs: run.font.name = 'Arial (Body)' # Save the document doc.save(file_name) |
AI + Python = a powerful automation tool
This script demonstrates an advanced use case of integrating AI-powered analysis and content generation with document automation in Python. It’s a comprehensive example of how combining various Python libraries with AI models can produce a powerful automation tool.
Download the example code and report:
- The full Python script.
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import docx import requests from openai import OpenAI import os import openai from Levenshtein import ratio from docx.shared import Pt from bs4 import BeautifulSoup import io from docx.oxml.shared import OxmlElement, qn from docx.shared import Pt from docx.enum.text import WD_ALIGN_PARAGRAPH WEBZ_API_KEY = os.getenv("WEBZ_API_KEY") openai.api_key = os.getenv("OPENAI_API_KEY") NUM_OF_REPORTS = 5 client = OpenAI() def are_similar(str1, str2, threshold=1): """ Check if two strings are similar based on Levenshtein ratio. """ return ratio(str1, str2) > threshold def remove_similar_strings(articles): unique_articles = [] for article in articles: if not any(are_similar(article['text'], existing['text'], 0.7) for existing in unique_articles): unique_articles.append(article) return unique_articles def trim_string(string, max_length): if len(string) > max_length: return string[:max_length] else: return string # Function to get news articles from Webz.io API def fetch_articles(query, api_key, total): endpoint = f"https://api.webz.io/filterWebContent?token={api_key}&format=json&q={query}&size=100&ts=0" all_posts = [] while total > 0: response = requests.get(endpoint) data = response.json() posts = data["posts"] if len(posts) == 0: break all_posts.extend(posts) total -= len(posts) if total > 0 and "next" in data: endpoint = f"https://api.webz.io{data['next']}" else: break articles = [] for article in all_posts: article = {'title': article["title"], 'text': trim_string(trim_title(article["title"]) + "\n\n" + article["text"], 10000), 'link': article['url'], 'published': article['published']} articles.append(article) return articles def trim_title(input_string): words = input_string.split() if "|" in input_string: return input_string.split("|")[0] last_dash_index = input_string.rfind("-") if last_dash_index != -1: right_of_dash = input_string[last_dash_index + 1:] right_words = right_of_dash.split() if len(right_words) <= 3 and len(words) > 10: return input_string[:last_dash_index] return input_string def add_image_from_base64(doc, image_url): response = requests.get(image_url) # Check if the request was successful if response.status_code == 200: image_stream = io.BytesIO(response.content) doc.add_picture(image_stream, width=docx.shared.Inches(6)) else: print(f"Failed to download image. Status code: {response.status_code}") def html_to_word(doc, html_content): soup = BeautifulSoup(html_content, 'html.parser') for element in soup.find_all(['b', 'ul']): if element.name == 'b': # Add bold text as a heading doc.add_paragraph(element.get_text(), style='Heading 2') elif element.name == 'ul': for item in element.find_all('li'): # Add list items doc.add_paragraph(item.get_text(), style='List Bullet') def add_hyperlink(paragraph, url, text): """ Add a hyperlink to a paragraph. """ part = paragraph.part r_id = part.relate_to(url, docx.opc.constants.RELATIONSHIP_TYPE.HYPERLINK, is_external=True) hyperlink = OxmlElement('w:hyperlink') hyperlink.set(qn('r:id'), r_id,) new_run = OxmlElement('w:r') rPr = OxmlElement('w:rPr') u = OxmlElement('w:u') u.set(qn('w:val'), 'single') rPr.append(u) u = OxmlElement('w:u') u.set(qn('w:val'), 'single') rPr.append(u) new_run.append(rPr) new_run.text = text hyperlink.append(new_run) paragraph._p.append(hyperlink) return hyperlink def insert_titles_in_text(text, reports): # Placeholder for inserting the titles placeholder = "[]" # Extracting the titles from the reports and formatting them with new lines titles = "\n".join([report['title'] for report in reports]) # Replacing the placeholder with the titles updated_text = text.replace(placeholder, titles) return updated_text def generate_article_image(): print("Generating post image") image_url = "" try: response = client.images.generate( model="dall-e-3", prompt="Create an image for a cover of a Mergers and Acquisitions report. The scene is set in a modern, spacious corporate boardroom with a large glass table. On the table, neatly arranged, are documents, pens, and digital tablets showing graphs and financial data. In the foreground, two pairs of hands, one male and one female, are in the middle of a handshake, symbolizing a successful deal. In the background, through a large window, we see a city skyline with skyscrapers, implying a high-powered business environment. The color scheme should be a mix of cool blues and warm golds to give a sense of professionalism and success. Incorporate subtle M&A related icons like pie charts, upward arrows, and company logos in a tasteful manner. Ensure the image is suitable for a formal report, with a focus on clarity and corporate aesthetics.", n=1, size="1024x1024" ) image_url = response.data[0].url except Exception as e: print("An error occurred generating the image:", str(e)) return image_url def get_unique_posts_from_webz(query): print("Fetch posts from Webz.io") articles = fetch_articles(query, WEBZ_API_KEY, 100) filtered_articles = remove_similar_strings(articles) return filtered_articles def call_gpt_completion(prompt): return client.chat.completions.create( model="gpt-4-1106-preview", max_tokens=4096, messages=[ {"role": "user", "content": prompt}, ] ) def generate_reports(filtered_articles): print("Generating Reports") reports = [] for article in filtered_articles: print(f"Creating report about: {article['title']}") prompt = f"""Carefully review the following news article between the [] brackets and determine if there is an explicit discussion about an M&A deal emerging from its content. The article is as follows: [ {article['text']} ] If the article explicitly mentions or clearly discuss an M&A (merger & acquisition) deal, generate a detailed report in HTML format. Use <B> tags to highlight the titles of each section and <UL> and <LI> tags for listing items. The report should include the following sections: <HTML> <B>Executive Summary</B> <UL><LI>Summarize the main points of the M&A deal: companies involved, deal size, key dates, high-level analysis of the deal's rationale and expected outcomes.</LI></UL> <B>Introduction</B> <UL><LI>Provide a background information about the companies involved, industry context and market conditions leading up to the M&A.</LI></UL> <B>Details of the Deal</B> <UL><LI>Provide a description of the M&A transaction: type, structure, financial terms, timeline, information on deal financing.</LI></UL> <B>Strategic Rationale</B> <UL><LI>Provide a strategic reasons behind the M&A, expected benefits for both companies.</LI></UL> <B>Market Reaction and Analysis</B> <UL><LI>Discuss the market reaction to the announcement, comparison with similar industry deals.</LI></UL> <B>Regulatory and Legal Considerations</B> <UL><LI>Mention any regulatory approvals and antitrust concerns, legal implications and compliance requirements.</LI></UL> <B>Risk Analysis</B> <UL><LI>Discuss any potential risks associated with the deal, risk mitigation strategies.</LI></UL> <B>Financial Analysis</B> <UL><LI>Review the financial metrics and performance indicators, impact on financial statements.</LI></UL> <B>Industry and Competitor Impact</B> <UL><LI>Discuss the effect on the broader industry and market dynamics, impact on competitors.</LI></UL> <B>Conclusion and Recommendations</B> <UL><LI>Provide a summary of key findings and insights, recommendations for stakeholders.</LI></UL> </HTML> If the article does not explicitly mention or discuss an M&A deal, please respond with: can't produce report. """ try: response = call_gpt_completion(prompt) report = {'text': ''} for choice in response.choices: report['text'] += choice.message.content if "Executive Summary" in report['text']: report['link'] = article['link'] report['title'] = article['title'] report['published'] = article['published'] reports.append(report) print(f"Created a report about: {article['title']}") else: print(f"Can't product report for: {article['title']}") if len(reports) == NUM_OF_REPORTS: break except Exception as e: print("An error occurred:", str(e)) return reports def generate_intro(reports): print("Generate post intro") prompt = """ Write a paragraph introducing a digest that contains M&A reports about the following titles, don't elaborate on these titles: [] The reports are created automatically by using Webz.io news api and ChatGPT. The reports are generated by calling the Webz.io news API for news articles categorized as "Economy, Business and Finance" related to M&A deals. The matching news articles are then run through a ChatGPT prompt to analyze if the article is indeed about an M&A deal. If so it create a structured report. """ prompt = insert_titles_in_text(prompt, reports) intro = "" try: response = call_gpt_completion(prompt) for choice in response.choices: intro += choice.message.content except Exception as e: print("An error occurred:", str(e)) return intro def generate_title(intro): print("Creating a title") prompt = "Create a title using the following text as a context:\n" + intro title_text = "" try: response = call_gpt_completion(prompt) for choice in response.choices: title_text += choice.message.content except Exception as e: print("An error occurred:", str(e)) title_text = title_text.strip(" ").strip('\"') if title_text.startswith("Title:"): # Sometimes ChatGPT prefix the title with Title: return title_text[len("Title:"):] return title_text def create_word_doc(file_name, title_text, image_url, intro, reports): print("Saving to word document") doc = docx.Document() # Add a title title = doc.add_paragraph() title.style = 'Title' title_run = title.add_run(title_text) title_run.font.size = Pt(24) # Set the font size title_run.font.name = 'Arial (Body)' # Set the font title.alignment = WD_ALIGN_PARAGRAPH.CENTER # Center align the title if len(image_url) > 0: add_image_from_base64(doc, image_url) doc.add_paragraph(intro) # Add each report for report in reports: p = doc.add_paragraph(style='Heading 1') add_hyperlink(p, report['link'], report['title']) doc.add_paragraph(f"Published on: {report['published']}") html_to_word(doc, report['text']) doc.add_paragraph(""" """) for paragraph in doc.paragraphs: for run in paragraph.runs: run.font.name = 'Arial (Body)' # Save the document doc.save(file_name) def main(): image_url = generate_article_image() filtered_articles = get_unique_posts_from_webz("""category:"Economy, Business and Finance" num_chars:>1000 language:english published:>now-7d social.facebook.likes:>0 title:acquire""") reports = generate_reports(filtered_articles) intro = generate_intro(reports) title_text = generate_title(intro) create_word_doc("M&A digest.docx", title_text, image_url, intro, reports) if __name__ == "__main__": main() |
- Example of auto-generated report in PDF format.
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