How to use Google Gemini 2.0 for Finance and FP&A
Short Summary:
This video demonstrates how to leverage Google Gemini 2.0's new models for financial analysis and FP&A tasks. Key applications include stock analysis (using Python code generation and visualization via YFinance and Google Colab), industry research (leveraging Gemini's access to YouTube and search), skill acquisition (finding and summarizing relevant YouTube tutorials), and analyzing personal datasets (generating Python code for cohort analysis and other visualizations). The video emphasizes practical application through specific prompts and examples, showcasing Gemini's ability to generate code, summarize information, and create visualizations. The process involves prompting Gemini with specific instructions, utilizing generated Python code in Google Colab, and interpreting the results.
Detailed Summary:
The video is structured around several use cases demonstrating Gemini 2.0's capabilities for finance professionals:
Section 1: Stock Analysis: The presenter shows how to use the Gemini "Flash" model to generate Python code using the YFinance library to analyze stocks like Tesla, Microsoft, Google, and Amazon. The generated code creates data visualizations. The code is then executed in Google Colab, a Google Docs-like environment for programming. This section highlights the ease of generating code and visualizations for financial data analysis.
Section 2: Industry Research: Utilizing the "Gemini 2.0 Flash Thinking experimental with apps" model, the presenter demonstrates researching industries like quantum computing in finance. Gemini can suggest relevant YouTube videos, summarize their content, and even assist in acquiring new skills by searching for and summarizing tutorials on topics like building FP&A dashboards in Python and Excel. This section showcases Gemini's ability to access and process information from external sources.
Section 3: Analyzing Personal Datasets: This section focuses on analyzing a personal Excel dataset containing financial data (dates, customer IDs, products, invoices). The presenter instructs Gemini, acting as a "finance PhD and data science expert," to generate Python code for cohort analysis and other visualizations. The process involves describing the data structure and location to Gemini, which then generates the necessary code. The code is executed in Google Colab after uploading the dataset. The generated visualizations (e.g., cohort retention heatmap) are then interpreted, with Gemini providing summaries tailored to specific audiences (e.g., a startup's head of sales). The presenter also demonstrates using image uploads to help Gemini understand the visualizations.
Section 4: Report Generation: The presenter shows how to generate reports on specific industries in particular countries (e.g., healthcare in Australia, financial services in France). Gemini provides a structured report, showing its thought process step-by-step.
Section 5: Additional Google Colab Examples: The final section provides further examples of using Gemini to generate Python code for more advanced financial analysis within Google Colab, including Monte Carlo simulations and interactive visualizations (e.g., sales maps and heatmaps correlating sales with other factors).
Throughout the video, the presenter emphasizes the speed and ease of using Gemini 2.0 for various financial tasks, highlighting the practical applications and benefits for FP&A professionals. The use of specific prompts and step-by-step demonstrations makes the video highly practical and instructional. Notably, the presenter encourages viewers to use Google Colab for executing the generated Python code and mentions troubleshooting errors with Gemini itself.