Problem
Research questions and friction points this paper is trying to address.
financial education
investment options
personal finance decisions
InvestorNerd: An Investment and Financial Insights System Based on User Profiles
🤖 AI Summary
该研究通过构建一个基于用户资料的AI驱动平台InvestorNerd，解决了普通个体特别是无金融教育背景者理解投资和个人财务决策难题。
📝 Abstract
InvestorNerd is a web-based platform (investornerd.org) designed to educate and democratize financial understanding by providing accessible, AI-powered investment and personal finance insights tailored to potential user profiles. The system addresses a key challenge in helping everyday individuals, especially those without formal financial education, make sense of investment options and personal financial decisions. The platform offers three interactive tools:
Stock Insights - Allows users to input any Stock, Mutual Fund, or Exchange Traded Fund (ETF) ticker to receive a summary of relevant news sentiment and quantitative metrics.
Stock Insights Questionnaire - Builds on stock insights by letting users specify a preferred risk tolerance and sector interest. It returns categorized investment tables based on volatility and sector, sortable by dividend yield, return percentages, and others.
General Insights Questionnaire - Provides users with personalized insights based on answers to an age-income-expenditure-savings questionnaire. Outputs include possible actions regarding savings strategies, account types (e.g., Roth IRA, UTMA), and potential loan options (e.g., FHA).
The output is designed to be clear, unbiased, and educational, empowering novice investors with practical insights. This paper details the design, implementation, and evaluation of InvestorNerd, demonstrating how generative AI and open financial data can be integrated to create scalable, insight-rich tools for financial literacy.
Innovation
Methods, ideas, or system contributions that make the work stand out.
generative AI
open financial data
personalized insights
financial literacy
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