Quant & Multi-Factor
Remove human emotion from the equation. Learn the data-driven approaches used by the world's top hedge funds to consistently beat the market.
The Case for Quant
Traditional investing often relies on narratives, intuition, and human judgment. The problem? Humans are inherently emotional and suffer from cognitive biases. We tend to panic during crashes and become overly exuberant during bubbles.
Quantitative investing uses mathematical models, historical data backtesting, and rigid rule sets to make buy and sell decisions. When you trade a system, you don't guess — you execute.
Executing the Algorithm
A good rule-based system tells you exactly WHAT to buy, WHEN to buy it, and crucially, exactly WHEN to sell it.
The Four Major Factors
Academic research has proven that certain "factors" historically outperform the broader market over long periods.
1. Value Factor
Buying stocks that are cheap relative to their fundamental metrics (P/E, P/B). The market often overreacts to bad news, undervaluing totally fine companies.
2. Momentum Factor
"What goes up, tends to keep going up." Buying stocks that have shown strong price performance in the past 6 to 12 months. Riding the wave.
3. Quality Factor
Filtering for companies with high profitability, strong balance sheets, stable earnings, and low debt. High Return on Equity (ROE) businesses.
4. Low Volatility
Surprisingly, boring, less volatile stocks often generate better risk-adjusted returns over time than high-flying, highly volatile growth stocks.
The Multi-Factor Approach
No single factor works all the time. Value can underperform for a decade, while Momentum crashes during market reversals.
By building a multi-factor portfolio (e.g., combining Value and Momentum), you smooth out the returns. You buy stocks that are mathematically cheap BUT are also starting to show an upward trend.
- Screening: Using tools to filter 5,000 stocks down to the top 30 based on factor scores.
- Rebalancing: Systematically selling losers and buying new winners every quarter.
- Backtesting: Verifying rule sets against 20+ years of historical market data.
Rank & Sort
Quant systems rank the entire universe of stocks. The algorithm doesn't care about the company's story, only its data rank.
This content is purely for educational purposes. Backtested results do not guarantee future performance. Systems can fail during unprecedented "Black Swan" events. We are not SEBI registered advisors.