Using AI for Equities Research: How Singapore Traders Are Getting an Edge

Every morning, thousands of Singapore traders sit down with the same problem: too much information, not enough time. Earnings reports, analyst notes, CNA articles, Straits Times business pages, SGX announcements, global macro data — it’s a firehose. The ones who process it fastest get an edge.

AI agents are changing how that processing happens. Not by replacing human judgment — the best traders are clear about that — but by compressing hours of reading into minutes, surfacing patterns humans miss, and monitoring markets around the clock.

Here’s how Singapore traders are actually using AI for equities research, and how you can build similar capabilities yourself.

What AI Can (and Can’t) Do for Equities Research

Let’s set expectations clearly before diving into tools and workflows.

What AI Does Well

• Summarize earnings reports — extract key metrics, compare to estimates, flag surprises
• Monitor news sentiment — scan dozens of sources, classify articles as bullish/bearish/neutral
• Screen stocks — filter SGX-listed companies by financial criteria at natural language speed
• Generate research drafts — produce initial analysis that humans can review and refine
• Track price alerts and patterns — monitor watchlists continuously without fatigue

What AI Doesn’t Do Well

• Predict stock prices — no AI reliably predicts short-term price movements. If someone tells you otherwise, they’re selling something.
• Replace human judgment — market context, geopolitical reading, and intuition still matter enormously
• Handle novel situations — AI reasons from historical patterns. Black swan events break those patterns.
• Guarantee compliance — MAS regulations require human accountability for investment decisions

The traders getting the most value from AI treat it like a very fast, tireless research analyst — not an oracle.

Building an AI Research Agent for SGX Stocks

Here’s a practical workflow you can implement. We’ll break it into layers, from simple to advanced.

Layer 1: Automated Earnings Analysis

The most immediately useful application. When a company like DBS, Singtel, or CapitaLand reports earnings, your AI agent can:

1. Pull the earnings data from financial APIs
2. Compare actuals to consensus estimates
3. Summarize the key takeaways in plain English
4. Flag anything unusual — margin changes, guidance shifts, one-off items

Tools needed:

• Financial data API: Yahoo Finance (free), Alpha Vantage (free tier available), or Financial Modeling Prep
• LLM: Claude or GPT-4 for analysis and summarization
• Python: for orchestration and data processing

Here’s a simplified workflow:

# Pseudo-code for earnings analysis agent
import yfinance as yf

# Pull DBS financials
dbs = yf.Ticker("D05.SI")
earnings = dbs.quarterly_financials
balance_sheet = dbs.quarterly_balance_sheet

# Send to LLM for analysis
prompt = f"""
Analyze DBS Group's latest quarterly results:
{earnings.to_string()}

Compare to previous quarters. Flag:
1. Revenue growth trend
2. Net interest margin changes  
3. Any unusual items
4. Key risks going forward

Write as a concise research note for a retail investor.
"""

analysis = llm.generate(prompt)

The output is a structured research note generated in seconds, covering what would take 30-60 minutes of manual reading.

Layer 2: News Sentiment Monitoring

Stock prices don’t move on fundamentals alone. News sentiment drives short-term movements, especially for mid-cap SGX stocks where analyst coverage is thin.

Your AI agent can monitor:

• Straits Times Business — company-specific news, policy changes
• CNA Business — economic indicators, government announcements
• SGX announcements — regulatory filings, material disclosures
• Global sources — Reuters, Bloomberg, for international exposure of SG companies
• Social media — Reddit (r/singaporefi), StocksCafe forums, Telegram groups

How sentiment analysis works:

# Simplified sentiment monitoring
articles = fetch_latest_news(query="DBS Group Singapore", sources=["CNA", "ST"])

for article in articles:
    summary = llm.summarize(article.content)
    sentiment = llm.classify(
        article.content, 
        categories=["bullish", "bearish", "neutral"]
    )
    
    if sentiment == "bearish" and article.source_credibility == "high":
        send_alert(f"⚠️ Bearish signal on DBS: {summary}")

Real-world example: In late 2025, several traders using AI monitors caught early Straits Times reporting about MAS reviewing bank capital requirements — days before analyst notes appeared. The AI flagged the articles as “potentially bearish for bank stocks” and alerted its human operators.

The humans then decided whether to act. The AI just made sure they didn’t miss the signal.

Layer 3: Watchlist Intelligence

Instead of manually checking your portfolio every day, an AI agent can:

• Track 20-50 stocks simultaneously across SGX and global markets
• Generate daily briefings summarizing price movements, volume anomalies, and relevant news
• Compare performance against benchmarks (STI, sector indices)
• Flag technical signals — volume spikes, price breakouts, unusual options activity

Sample daily briefing output:

📊 Daily Watchlist — 21 Feb 2026

OUTPERFORMERS:
• Sea Ltd (SE) — +3.2%, strong volume. Q4 earnings beat, 
  Shopee GMV up 18% YoY
• Wilmar (F34.SI) — +1.8%, palm oil futures rising

UNDERPERFORMERS:  
• SingTel (Z74.SI) — -1.5%, concerns about Optus regulatory 
  fine (AUD $12M)

NEWS TO WATCH:
• MAS to announce updated fintech regulations (expected this week)
• China PMI data tomorrow — impacts Yangzijiang, BYD exposure

UNUSUAL ACTIVITY:
• Keppel Corp (BN4.SI) — volume 3.2x average, no clear 
  news catalyst. Worth monitoring.

This briefing is generated automatically at 7 AM, before markets open. It takes the AI about 90 seconds and costs roughly S$0.05 in API calls.

Layer 4: Comparative Analysis and Screening

AI excels at comparative work that’s tedious for humans. Ask your agent:

• “Compare all SGX-listed REITs by distribution yield, gearing ratio, and NAV discount. Which three look most undervalued?”
• “Which Singapore tech stocks have the fastest revenue growth over the past 4 quarters?”
• “Show me SGX-listed companies with market cap under S$500M, positive free cash flow, and dividend yields above 4%.”

These queries, which would take hours of manual spreadsheet work, get answered in minutes when your agent has access to financial data APIs.

The Tech Stack

For Singapore traders looking to build their own AI research tools, here’s a practical tech stack:

Data Sources

Source	What It Provides	Cost
Yahoo Finance API	Price data, financials, estimates	Free
Alpha Vantage	Fundamental data, technical indicators	Free (5 calls/min)
SGX APIs	Official announcements, corporate actions	Free
Brave Search API	News monitoring, web search	Free tier available
Financial Modeling Prep	Detailed financials, DCF data	From US$14/month

LLM APIs

Provider	Best For	Cost Per Analysis
Claude (Anthropic)	Nuanced reasoning, long documents	~S$0.02-0.08
GPT-4 (OpenAI)	General analysis, tool integration	~S$0.03-0.10
Gemini (Google)	Large context windows for long reports	~S$0.01-0.05

Infrastructure

• Python — primary language for financial data processing
• Pandas — data manipulation and analysis
• Cron jobs / schedulers — run monitoring on a schedule
• Telegram Bot API — send alerts to your phone
• PostgreSQL or SQLite — store historical analysis for comparison

Total cost for a comprehensive setup: S$30-80/month, depending on how many stocks you monitor and how frequently you run analysis.

What the Smart Traders Are Doing Differently

The traders getting the most value from AI aren’t using it as a magic prediction machine. They’re using it to:

1. Reduce information overload — AI reads everything, humans focus on what matters
2. Enforce discipline — agents check the same criteria consistently, without emotional bias
3. Compound knowledge — every analysis is stored, creating a searchable history of market insights
4. Move faster — when a material announcement drops, the AI has a summary ready in seconds

One Singapore-based trader we spoke with described it well: “My AI doesn’t tell me what to buy. It makes sure I’ve read everything I should read before I make a decision. It’s like having a research assistant who works 20 hours a day and never gets bored.”

Important Caveats

Regulatory considerations: MAS requires that investment decisions be made by qualified humans. AI can assist research but cannot make autonomous trading decisions for regulated activities. If you’re managing other people’s money, consult a compliance professional.

Data quality matters: Garbage in, garbage out. Free financial APIs sometimes have delayed or inaccurate data. Cross-reference important figures with official SGX filings.

Backtesting isn’t prediction: Just because your AI-generated trading signals would have worked historically doesn’t mean they’ll work going forward. Markets adapt.

Cost control: API costs can spiral if you’re not careful. Set hard spending limits. Monitor usage weekly. A well-designed agent checks only what’s necessary — it doesn’t re-analyze unchanged data.

Going Deeper: AI Academy’s Finance Track

The workflow outlined here is a starting point. At AI Academy, our advanced courses cover the complete pipeline — from building your first stock screener to deploying multi-agent research systems that monitor entire sectors.

You’ll learn to build AI Employees specifically configured for financial research, including real-time monitoring, portfolio analytics, and automated reporting. We also cover the compliance and ethical considerations that Singapore-based investors need to know.

For corporate teams at investment firms, our corporate training programmes can be customized to your specific asset classes and research workflows.

Ready to build your own AI research agent? Join AI Academy’s next cohort →