COURSE

AI Agents for Financial Research and Quantitative Investing

A hands-on 6-hour workshop where participants build their first finance AI agent through real-world scenarios — no coding or finance background required.

Course dates: Please fill the form below to enquire
Course time: 9am to 5pm
Course fees: SGD500 (before GST)

Course dates: Please fill the form below to enquire
Course time: 9am to 5pm
Course fees: SGD500 (before GST)

Course dates: Please fill the form below to enquire
Course time: 9am to 5pm
Course fees: SGD500 (before GST)

COURSE OBJECTIVE
COURSE OBJECTIVE
Overview
Overview

Artificial Intelligence (AI) agents are rapidly transforming the financial industry by automating research, analyzing reports, building financial models, and assisting in quantitative investment research.

This highly practical 6-hour workshop introduces participants to AI agents through three real-world finance scenarios. Learners will discover how AI agents differ from chatbots, understand how agents perform tasks autonomously, and learn how to evaluate whether AI-generated outputs can be trusted.

Through guided demonstrations and hands-on exercises, participants will learn how AI agents can assist with reading financial reports, building financial models, and conducting quantitative research. No prior knowledge of coding, mathematics, trading, or finance is required. By the end of the workshop, participants will have built and evaluated their first finance-focused AI agent and gained a practical framework for applying AI in financial research and decision support.

Artificial Intelligence (AI) agents are rapidly transforming the financial industry by automating research, analyzing reports, building financial models, and assisting in quantitative investment research.

This highly practical 6-hour workshop introduces participants to AI agents through three real-world finance scenarios. Learners will discover how AI agents differ from chatbots, understand how agents perform tasks autonomously, and learn how to evaluate whether AI-generated outputs can be trusted.

Through guided demonstrations and hands-on exercises, participants will learn how AI agents can assist with reading financial reports, building financial models, and conducting quantitative research. No prior knowledge of coding, mathematics, trading, or finance is required. By the end of the workshop, participants will have built and evaluated their first finance-focused AI agent and gained a practical framework for applying AI in financial research and decision support.

Course Content

AI Agent

Goal

Data

Work

Evidence → Decision

Module 1

Introduction to AI Agents in Finance

  • Understand the difference between chatbots and AI agents

  • Learn how AI agents operate using goal-driven workflows

  • Explore the AI agent loop: Goal → Data → Work → Evidence → Decision

  • Understand the role of AI agents in financial research and analysis

Annual Report

Extract

Verify

Insight

Module 2

Reading Financial Reports with AI Agents

  • Extract key financial information from annual reports and financial statements

  • Summarize lengthy reports into concise insights

  • Cross-check financial data and identify inconsistencies

  • Use AI agents to answer financial research questions

  • Learn best practices for verifying AI-generated outputs

Valuation Model

Best

Base

Worst

Module 3

Financial Modeling with AI Agents

  • Understand the fundamentals of financial modeling

  • Build simple revenue and valuation models using AI assistance

  • Create and analyze best-case, base-case, and worst-case scenarios

  • Perform sensitivity analysis by adjusting model assumptions

  • Interpret model outputs and identify key drivers

Signals

Factor

Signal

Test

Module 4

Quantitative Investing in Plain English

  • Understand the quantitative research process

  • Learn the concept of factors, signals, strategies, and backtesting

  • Explore time-series and cross-sectional investment approaches

  • Understand how quantitative strategies are developed and evaluated

AI Agent

Task Definition

Memory Review

Data Binding

Factor Generation

Evaluation

Module 5

Building and Evaluating a Quant Research Agent

  • Learn the five-step quant research workflow:

    • Task Definition

    • Memory Review

    • Data Binding

    • Factor Generation

    • Evaluation

  • Generate and test investment factors using AI agents

  • Interpret quantitative performance metrics

  • Assess research outputs using evidence-based evaluation techniques

Deployment

Report

Exercise

Roadmap

Module 6

Trust, Limitations and AI Agent Deployment

  • Understand the limitations of AI-generated outputs

  • Learn how to validate and inspect AI-produced research

  • Apply AI agents across reporting, modeling, and quantitative research workflows

  • Complete a capstone exercise by assigning and evaluating a real-world finance task

    using an AI agent

  • Develop a roadmap for progressing from research automation to decision support

    and AI-assisted workflows

AI tools including but not limited to, ChatGPT, Claude, Gemini, Quandora, etc, will be used.

OUTCOMES
OUTCOMES
What You Will Achieve
What You Will Achieve

Upon successful completion of this course, participants will be able to:

Upon successful completion of this course, participants will be able to:

Explain the differences between AI chatbots and AI agents.

Explain the differences between AI chatbots and AI agents.

Understand how AI agents can be applied to financial research and analysis.

Understand how AI agents can be applied to financial research and analysis.

Use AI agents to extract, summarize, and verify information from financial reports.

Use AI agents to extract, summarize, and verify information from financial reports.

Build and stress-test simple financial models using AI assistance.

Build and stress-test simple financial models using AI assistance.

Apply scenario analysis techniques to evaluate different business assumptions.

Apply scenario analysis techniques to evaluate different business assumptions.

Understand the foundations of quantitative investing and systematic research.

Understand the foundations of quantitative investing and systematic research.

Differentiate between factors, signals, strategies, and backtesting concepts.

Differentiate between factors, signals, strategies, and backtesting concepts.

Build and evaluate a simple AI-powered quant research workflow.

Build and evaluate a simple AI-powered quant research workflow.

Interpret key quantitative performance metrics such as return, Sharpe ratio, turnover, fitness score, and drawdown.

Interpret key quantitative performance metrics such as return, Sharpe ratio, turnover, fitness score, and drawdown.

Assess the reliability of AI-generated outputs using evidence-based validation techniques.

Assess the reliability of AI-generated outputs using evidence-based validation techniques.

Design and deploy a basic AI agent workflow for financial research and decision support.

Design and deploy a basic AI agent workflow for financial research and decision support.

Apply a structured framework of Goal → Data → Work → Evidence → Decision when working with AI agents.

Apply a structured framework of Goal → Data → Work → Evidence → Decision when working with AI agents.

Register Interest

Fill in the form with your details and we will reach out to you.

Register Interest

Fill in the form with your details and we will reach out to you.

Register Interest

Fill in the form with your details and we will reach out to you.

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