paper-with-me

홈 › Papers

StockBabble: A Conversational Financial Agent to support Stock Market Investors

2021-06-15 · Suraj Sharma, Joseph Brennan, Jason R. C. Nurse

We introduce StockBabble, a conversational agent designed to support understanding and engagement with the stock market. StockBabble's value and novelty is in its ability to empower retail investors -- many of which may be new to investing -- and supplement their informational needs using a user-friendly agent. Users have the ability to query information on companies to retrieve a general and financial overview of a stock, including accessing the latest news and trading recommendations. They can also request charts which contain live prices and technical investment indicators, and add shares to a personal portfolio to allow performance monitoring over time. To evaluate our agent's potential, we conducted a user study with 15 participants. In total, 73% (11/15) of respondents said that they felt more confident in investing after using StockBabble, and all 15 would consider recommending it to others. These results are encouraging and suggest a wider appeal for such agents. Moreover, we believe this research can help to inform the design and development of future intelligent, financial personal assistants.

📄 PDF Abstract BibTeX arXiv:2106.08298

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

FinSphere: A Conversational Stock Analysis Agent Equipped with Quantitative Tools based on Real-Time Database

2025-01-08 · Shijie Han, Changhai Zhou, Yiqing Shen, Tianning Sun 외

Current financial Large Language Models (LLMs) struggle with two critical limitations: a lack of depth in stock analysis, which impedes their ability to generate professional-grade insights, and the absence of objective …

AI Agent

StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?

2025-10-02 · Yanxu Chen, Zijun Yao, Yantao Liu, Amy Xin 외 arxiv

Large language models (LLMs) demonstrate strong potential as autonomous agents, with promising capabilities in reasoning, tool use, and sequential decision-making. While prior benchmarks have evaluated LLM agents in vari…

MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents

2025-02-01 · George Fatouros, Kostas Metaxas, John Soldatos, Manos Karathanassis

MarketSenseAI is a novel framework for holistic stock analysis which leverages Large Language Models (LLMs) to process financial news, historical prices, company fundamentals and the macroeconomic environment to support …

Decision MakingFinancial AnalysisRetrieval-augmented Generation

Multilingual Conversational AI for Financial Assistance: Bridging Language Barriers in Indian FinTech

2025-12-01 · Bharatdeep Hazarika, Arya Suneesh, Prasanna Devadiga, Pawan Kumar Rajpoot 외 arxiv

India's linguistic diversity presents both opportunities and challenges for fintech platforms. While the country has 31 major languages and over 100 minor ones, only 10\% of the population understands English, creating b…

Response Generation

Behavioral Consistency Validation for LLM Agents: An Analysis of Trading-Style Switching through Stock-Market Simulation

2026-02-02 · Zeping Li, Guancheng Wan, Keyang Chen, Yu Chen 외 arxiv

Recent works have increasingly applied Large Language Models (LLMs) as agents in financial stock market simulations to test if micro-level behaviors aggregate into macro-level phenomena. However, a crucial question arise…