发表机构
Tufts University; New York University; Thapar Institute of Engineering and Technology（塔夫茨大学; 纽约大学; 塔帕尔工程技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
InvestorNerd 是一个基于用户画像的在线平台，利用生成式AI和开放金融数据提供股票洞察、问卷工具及个性化理财建议，旨在普及金融教育并帮助新手投资者做出明智决策。
AI 中文摘要
InvestorNerd 是一个基于网络的平台（此 http URL），旨在通过提供针对潜在用户画像的、可访问的、由人工智能驱动的投资和个人理财见解，来教育和普及金融理解。该系统解决了帮助普通个人（尤其是那些没有接受过正规金融教育的人）理解投资选择和个人财务决策这一关键挑战。该平台提供三个交互式工具：股票洞察——允许用户输入任何股票、共同基金或交易所交易基金（ETF）的代码，以接收相关新闻情绪和量化指标的摘要。股票洞察问卷——在股票洞察的基础上，让用户指定偏好的风险承受能力和行业兴趣。它根据波动性和行业返回分类的投资表格，可按股息收益率、回报百分比等进行排序。通用洞察问卷——根据年龄-收入-支出-储蓄问卷的答案，为用户提供个性化见解。输出包括有关储蓄策略、账户类型（例如，罗斯个人退休账户、UTMA）和潜在贷款选项（例如，FHA）的可能行动。输出旨在清晰、公正且具有教育意义，使新手投资者能够获得实用的见解。本文详细介绍了 InvestorNerd 的设计、实现和评估，展示了如何将生成式人工智能和开放的金融数据整合起来，以创建可扩展的、洞察丰富的金融素养工具。
英文摘要
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.
