Are you tired of spending hours analyzing complex financial statements? Well, fret no more! In this article, we will explore a groundbreaking use case of Language Models (LLMs) in finance – using GPT-4 to summarize and analyze financial statements.
Unleashing the Power of GPT-4
GPT-4 is the latest addition to the family of LLMs. In our previous tutorials on GPT-3 and GPT-4, we discovered various innovative applications in finance, such as earnings call summaries, stock screener assistants, and research assistants. However, today, our focus will be on how GPT-4 can revolutionize the way we digest financial statements.
Imagine being able to effortlessly summarize and analyze financial statements with just a few clicks. It’s now possible! We have integrated an updated version of this financial statements AI analyst into the MLQ app. Sign up here to experience the power of GPT-4 firsthand.
Building an Interactive Streamlit App
Let’s walk through the steps of building a simple Streamlit app that allows users to analyze financial statements easily:
- Select the desired financial statement (income statement, balance sheet, or cash flow).
- Choose the statement period (annual or quarterly).
- Select the number of past statements to analyze.
- Input a stock ticker and click run.
The Streamlit app provides users with the raw DataFrame of financial statements to verify the numbers. It then generates a summary of key financial metrics in a user-friendly manner. Finally, the app performs a comprehensive analysis of the statements.
This application, though simple, has immense potential in the field of finance. By leveraging LLMs, we can summarize raw financial data, provide basic financial analysis, highlight key takeaways, and uncover interesting insights that may elude the less-trained financial eye.
Let’s Dive Into the Code
To get started, make sure you have the necessary libraries installed using pip:
pip install streamlit openai requests
Next, import the required libraries and set your OpenAI and Financial Modeling Prep API keys.
import streamlit as st import openai import requests import os from apikey import OPENAI_API_KEY, FMP_API_KEY openai.api_key = OPENAI_API_KEY
In the following steps, we’ll retrieve financial statements and generate summaries using GPT-4. This will enable us to analyze the data more efficiently and provide valuable insights.
# Step 1: Retrieving Financial Statements def get_financial_statements(ticker, limit, period, statement_type): # Retrieve financial statements from the Financial Modeling Prep API ... return financial_statements # Step 2: Generate Financial Statements Summary with GPT-4 def generate_financial_summary(financial_statements, statement_type): # Generate a summary of financial statements using GPT-4 ... return financial_summary # Step 3: Building the Streamlit App def financial_statements(): # Build the Streamlit app for analyzing financial statements ...
Finally, we create the main function and run the Streamlit app:
def main(): # Create the sidebar and select the assistant ... if __name__ == '__main__': main()
With a few simple steps, you can run the app locally using
streamlit run app.py.
Impressive, right? GPT-4 takes financial analysis to a whole new level.
The Future of Financial Analysis
In summary, GPT-4 provides a unique opportunity to make raw financial data more human-readable and accessible. While some financial experts may enjoy diving into spreadsheets, most people prefer an “AI-assisted” approach. They want summaries, insightful analysis, and key metrics that may have been overlooked.
This is just the beginning of what an AI analyst can offer. In future articles, we will explore other fascinating applications of LLMs in financial analysis. There is a massive opportunity to bring financial data to life, enabling users to have interactive conversations with their data. Imagine being able to ask follow-up questions and truly chat with financial data. Stay tuned!
Remember, GPT-4 is the game-changer that simplifies financial statements analysis. Sign up for the MLQ app and witness the power of AI in action. Your financial analysis experience will never be the same again.