AI-Driven Portfolio Tracking and Measurement

Retail investors face significant challenges when managing and optimizing their investment portfolios due to the complexity of financial instruments, overwhelming market data, and time constraints. Current tools often lack personalization, actionable insights, and accessibility, leaving many investors under-equipped to achieve their financial goals. Most retail investors, regardless of their sophistication, lack integrated tools to accurately measure their portfolio performance over time.

Traditionally, investors track the price at which they bought an instrument, such as a stock, and evaluate performance based on the current stock price. While this approach provides a relative gain or loss on individual stocks, it fails to account for the crucial factor of the holding period. Some investors may use metrics like XIRR to measure performance, but even with more sophisticated methods that extend XIRR calculations across multiple transactions, this approach still falls short of delivering a truly accurate measure of portfolio performance.

A more accurate approach to measuring portfolio performance could be based on a system similar to how Mutual Fund Net Asset Values (NAVs) are calculated. NAV represents a portfolio's market value, adjusting for cash inflows and outflows (investments or withdrawals). NAV can be calculated daily, based on the closing prices of all securities in the portfolio, providing an accurate and real-time measure of the portfolio's market value. A time-series-based NAV chart would enable investors to track their portfolio’s performance over time, and establish a benchmark to measure relative performance.

However, many retail investors are unaware of NAV-based measures, and few have access to tools that can accurately generate NAVs. This issue is further complicated by corporate actions, such as dividends, stock splits, rights issues, mergers, buybacks, and bankruptcies, which impact portfolio tracking and performance evaluation.

Proposed Solution:

Using PortfolioAssistant’s advanced AI tools and frameworks, we will deliver an intelligent platform that:

• Implements an NAV-based approach to portfolio performance measurement, similar to how Mutual Funds calculate NAVs.

• Calculates daily NAVs based on the closing prices of all securities in the portfolio, providing an accurate, real-time measure of portfolio value.

• Adjusts for cash inflows and outflows, such as investments or withdrawals, by modifying the number of units in the portfolio accordingly.

• Generates time-series-based NAV charts, enabling investors to visualize their portfolio’s performance over time and track long-term growth or decline.

• Allows benchmarking by comparing portfolio performance against a selected reference index, providing a clear view of performance relative to the market.

• Incorporates corporate actions (e.g., dividends, stock splits, rights issues, mergers, buybacks, bankruptcies) into NAV calculations to ensure all factors influencing the portfolio are accurately accounted for.

• Simplifies complex portfolio tracking by consolidating performance metrics into a single, easy-to-understand measure, making it accessible even for investors without advanced financial knowledge.

How AI/AGI Would Be Utilized:

To power the PortfolioAssistant platform, AI and AGI technologies will be applied in the following ways:

• Development of a Vertical LLM (Finance LLM): A specialized Language Model tailored to finance, specifically designed as a “Portfolio Assistant” LLM. This model will have access to vast financial datasets, historical stock data, and corporate actions (such as dividends, stock splits, mergers, buybacks), allowing it to fully understand the lifecycle of financial instruments within a portfolio.

• Comprehensive Historical Knowledge: The Portfolio Assistant LLM will have the ability to “remember” and interpret the full history of a stock or financial instrument, including past performance, corporate actions, and significant events that may have affected its price or market value. This capability will enable the model to provide highly informed insights, enhancing the accuracy of NAV calculations and portfolio evaluations.

• NAV Calculation Support: By leveraging its comprehensive understanding of each asset’s history and corporate actions, the LLM will improve NAV calculations. The model will refine the portfolio’s NAV by factoring in variables like price movements, dividend payouts, stock splits, and other relevant events.

• Natural Language Processing (NLP): The LLM will utilize NLP to automatically extract valuable insights from financial reports, earnings calls, news articles, and market trends. This will enable the LLM to translate complex financial data into actionable insights, further enhancing NAV accuracy and portfolio optimization.

• Interactive Chatbot for Financial Guidance: Using the LLM’s capabilities, an intelligent, conversational chatbot will be available to provide personalized financial advice, assist with portfolio tracking, and explain NAV-based performance metrics in clear, accessible language.

• Continual Learning: The LLM will continuously evolve by processing new financial data, adapting its models, and improving the accuracy of its insights. It will stay up-to-date with the latest market events, trends, and corporate actions, ensuring that its advice and NAV calculations reflect the most current information.

• Integration with Big Data and AI Tools: The LLM will work in tandem with other AI technologies, and other LLMs, specifically Finance Vertical LLMs, to analyze large-scale financial datasets, process complex documents, and refine the accuracy of NAV-based performance metrics.

Value Proposition:

• Empowering Retail Investors: Provide individuals with easy-to-understand insights and tools to make informed decisions, reduce risks, and achieve their financial goals.

• Enhancing Financial Literacy: Offer tailored educational content to help users gain confidence in managing their investments and understanding financial markets.