Introduction
In today’s fast-moving markets, investors face a deluge of financial disclosures: balance sheets, income statements, cash-flow reports and footnotes. Manually parsing these documents to spot trends, risks and opportunities can be laborious, error-prone and time consuming. Luckily, powerful web-based financial data services and AI language models can radically streamline this work. In this article, we show how to combine Financial Modeling Prep (FMP) and OpenAI’s GPT to turn raw financial statements into concise, investor-ready summaries—complete with key ratios, narrative insights and risk flags—in just a few lines of code.
1. Why Automate Financial Analysis?
• Volume and Complexity: Public companies file lengthy 10-Ks and 10-Qs quarterly or annually. Sifting through hundreds of line items can take hours.
• Consistency and Objectivity: Manual write-ups may vary in tone and depth depending on the analyst. Automated generation ensures uniform quality.
• Speed to Insight: Rapid summaries help portfolio managers react swiftly to earnings releases, macro shifts or competitor news.
• Customization: Different investors focus on cash flow, margins or leverage. Scriptable prompts let you tailor the output to your strategy.
2. Introducing Financial Modeling Prep (FMP)
FMP is an online API platform that provides real-time and historical financial data on stocks, ETFs, mutual funds and cryptocurrencies. Key features include:
• Financial Statements Endpoints: Instantly fetch income statements, balance sheets and cash-flow statements in JSON.
• Ratios and Metrics: Access precomputed ratios—P/E, ROE, current ratio—so you don’t have to calculate them manually.
• Market Data: Pull real-time quotes, historical prices and company profiles.
To get started, sign up at financialmodelingprep.com, generate a free or paid API key, then include it in your HTTP requests.
3. Leveraging GPT for Natural-Language Summaries
OpenAI’s GPT models excel at transforming structured data into fluent, context-aware narratives. By feeding a JSON payload along with a clear prompt, you can instruct the model to highlight revenue trends, margin pressures, debt levels and free-cash-flow developments, even flagging items that warrant closer scrutiny.
4. Step-by-Step Integration Guide
Below is an outline for building your own “financial-summary bot.” Adjust code snippets to your preferred language or framework.
Step 1: Install Dependencies
– HTTP client (e.g., axios or requests)
– OpenAI SDK or simple HTTP wrapper for the GPT endpoint
Step 2: Configure API Keys
– FMP_KEY = “your_fmp_api_key”
– OPENAI_KEY = “your_openai_api_key”
Step 3: Fetch Financial Statements
Request the latest annual or quarterly statements for your ticker (e.g., AAPL):
GET https://financialmodelingprep.com/api/v3/income-statement/AAPL?apikey=FMP_KEY&limit=1
GET https://financialmodelingprep.com/api/v3/balance-sheet-statement/AAPL?apikey=FMP_KEY&limit=1
GET https://financialmodelingprep.com/api/v3/cash-flow-statement/AAPL?apikey=FMP_KEY&limit=1
Step 4: Consolidate and Preprocess Data
– Extract key items: revenue, cost of revenue, gross profit, operating expenses, net income, total assets, total liabilities, operating cash flow, capital expenditures.
– Compute derived metrics if needed (e.g., EBITDA, free cash flow).
– Structure JSON to include both raw values and percentage changes year-over-year or quarter-over-quarter.
Step 5: Craft the Prompt
Compose a system or user prompt that frames the task clearly. For example:
“You are a professional equity analyst. Given the following financial statement data for [Company] in JSON format, write a concise 200-word summary highlighting revenue trends, profitability, cash-flow health and any credit risks. Use bullet points for key ratios.”
Step 6: Call the GPT API
POST https://api.openai.com/v1/chat/completions
Headers: Authorization: Bearer OPENAI_KEY
Payload:
{
“model”: “gpt-4o”,
“messages”: [
{ “role”: “system”, “content”: “You are an expert financial analyst.” },
{ “role”: “user”, “content”: “” }
],
“temperature”: 0.3,
“max_tokens”: 300
}
Step 7: Post-Processing
– Review for accuracy: Spot-check numbers against the raw data.
– Refine prompt: If the summary is too generic or misses an item, iterate on your instructions (e.g., ask explicitly for working-capital trends).
– Automate delivery: Integrate with Slack, email or your investment dashboard so summaries arrive on earnings days.
5. Best Practices and Tips
• Keep Prompts Focused: Specify word limits, tone (“professional,” “concise”), and format (paragraphs vs. bullet points).
• Control Randomness: Lower the temperature to reduce hallucinations and ensure factual consistency.
• Handle Rate Limits: Cache previous summaries for static periods; batch requests when analyzing a portfolio of tickers.
• Validate Critical Metrics: Always cross-verify debt levels, cash balance and revenue, since minor API changes can shift JSON structures.
• Layer Insights: Combine GPT narratives with charting libraries to visualize trends alongside text.
6. Conclusion
By marrying FMP’s robust financial data API with GPT’s natural-language generation capabilities, investors and analysts can automate the extraction of actionable insights from dense financial reports. This approach slashes manual effort, enforces consistency and accelerates decision-making—freeing you to focus on strategy rather than spreadsheets.
Key Takeaways
• Streamline Analysis: Automate data retrieval and narrative generation to produce consistent, high-quality financial summaries in minutes.
• Customizable Prompts: Tailor summaries to highlight the metrics and narrative style that matter most to your investment process.
• Verify and Iterate: Use low temperature settings for accuracy, validate key numbers, and refine prompts until your summaries meet your standards.
Frequently Asked Questions
1. How accurate are GPT-generated financial summaries?
GPT provides fluent, context-aware narratives, but you should always cross-check critical figures and ratios against the source data. Use a low temperature setting and explicit prompts to minimize hallucinations.
2. What are the main costs involved in this workflow?
Costs include your FMP API subscription (free tier available, paid tiers start at around $19/month) and your OpenAI usage fees (based on tokens processed; e.g., GPT-4 models may cost $0.03–$0.12 per 1,000 tokens). Optimize efficiency by caching results and batching requests.
3. Can this system handle multiple companies at once?
Yes. Implement batching and asynchronous requests to pull statements for dozens or hundreds of tickers. Combine summaries into single reports or dashboards, but monitor API rate limits and implement retry logic for robustness.
