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Outline

Financial Analytical Tool using LLM

  • Introduction

    • Why is financial analytics important?
      • Multiple data sources are avaiable in market with no unified data tool to analyze the data
      • Publicly aviable data is not enough to make a good decision
      • Private data is not avaiable to the user
      • Limited time to analyze the data

  • Background

    • What is the current state of financial analytics?
      • Excel reports are used to analyze the data. The data is not live and the analysis is not automated.
    • What are the current challenges of financial analytics?
      • Limited time to analyze the data
      • Limited data availability
      • Limited data quality
      • Limited data sources

  • What are we trying to achieve

    Annual Report:

    1. Data Extraction: Extract the text from annual reports in a digital format. You can use Optical Character Recognition (OCR) tools if the reports are scanned documents.
    2. Preprocessing: Preprocess the text data to clean it up and prepare it for analysis. This may involve removing noise, punctuation, stopwords, and special characters.
    3. Topic Modeling: Use techniques such as Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF) to identify the main topics or themes present in the annual reports. This can help in understanding the focus areas of the company and any emerging trends or concerns.
    4. Sentiment Analysis: Apply sentiment analysis to gauge the overall sentiment expressed in the annual reports. This can involve determining whether the language used is positive, negative, or neutral, which can provide insights into the company's performance and outlook.
    5. Financial Analysis: Extract financial data such as revenue, expenses, profits, and other key metrics from the annual reports. You can then perform quantitative analysis on this data to assess the financial health and performance of the company over time.
    6. Comparative Analysis: Compare the textual and financial data across multiple annual reports to identify trends, patterns, and deviations. This can help in benchmarking the company's performance against its peers or industry standards.
    7. Risk Assessment: Identify any potential risks or challenges mentioned in the annual reports, such as regulatory issues, market volatility, or competitive pressures. Analyze the language used to assess the severity and potential impact of these risks on the company's operations and financial performance.
    8. Executive Summary Generation: Use the LLM to generate executive summaries of the annual reports, highlighting the most important findings, insights, and trends. This can save time for stakeholders who need to quickly grasp the key takeaways from the reports.
    9. Future Outlook Prediction: Based on the analysis of historical data from the annual reports, use the LLM to generate predictions or forecasts for future performance indicators such as revenue growth, profitability, and market share.
    10. Interactive Reporting: Develop interactive dashboards or reports that allow users to explore the analyzed data from the annual reports in a user-friendly manner. This can facilitate deeper insights and decision-making for stakeholders. By employing LLMs for analyzing annual reports, you can gain valuable insights into various aspects of a company's performance, financial health, and strategic direction, ultimately aiding in informed decision-making by stakeholders.

    Financial Data

    1. Data Extraction: Extract the textual data from financial documents in a digital format. Ensure that the data is accurately transcribed and structured for analysis.
    2. Preprocessing: Preprocess the extracted text data to clean it up and prepare it for analysis. This may involve removing noise, punctuation, stopwords, and special characters, and structuring the data into appropriate sections.
    3. Financial Analysis: a. Balance Sheet Analysis: Use the LLM to analyze the components of the balance sheet such as assets, liabilities, and equity. Identify trends in the company's financial position over time, assess liquidity, leverage, and solvency ratios, and evaluate the company's capital structure. b. Income Statement Analysis: Analyze the income statement using the LLM to assess the company's revenue, expenses, and profitability. Identify trends in revenue growth, gross margin, operating income, and net income, and evaluate key performance indicators such as Earnings Per Share (EPS) and Return on Equity (ROE). c. Cash Flow Statement Analysis: Utilize the LLM to analyze the cash flow statement to understand the company's cash inflows and outflows from operating, investing, and financing activities. Assess the company's liquidity position, cash flow adequacy, and ability to generate free cash flow. d. Stock History Analysis: Analyze the historical stock price data using the LLM to identify trends, patterns, and correlations. Assess factors influencing stock price movements such as company performance, market sentiment, macroeconomic indicators, and industry trends.
    4. Comparative Analysis: Compare the financial data across multiple periods or against industry benchmarks to identify performance trends and deviations. Use the LLM to generate insights into how the company's financial metrics stack up against its peers or industry standards.
    5. Risk Assessment: Analyze the financial data using the LLM to identify potential risks and challenges facing the company. Assess factors such as financial stability, debt levels, operating risks, and market volatility, and evaluate their potential impact on the company's future performance.
    6. Executive Summary Generation: Use the LLM to generate executive summaries of the financial analysis, highlighting key findings, insights, and recommendations. This can help stakeholders quickly grasp the key takeaways from the analysis. By leveraging LLMs for financial analysis, you can gain deeper insights into a company's financial performance, position, and market dynamics, enabling informed decision-making by investors, analysts, and other stakeholders.

  • Solution

    • Automated Data Pipleines to pull the data
    • Pipelines to clean the data with
    • analyze the data
    • visualize the data
    • generate the executive summary
    • LLM models to draw insights and find gaps in markets, opportunity to test.