Intro to Quantitative Strategies & Factor Investing
Explore the mathematical foundations of quantitative finance, algorithmic trading, and how institutional factor models evaluate market risk.
🎯 Key Learning Objectives
- ✓Quantitative finance uses mathematical models and algorithms to identify and exploit market inefficiencies.
- ✓Factor investing targets specific, empirically proven anomalies such as Value, Momentum, Quality, and Low Volatility.
- ✓Systematic backtesting is crucial to verify that a quantitative strategy has predictive power rather than random noise.
- ✓Survivorship bias and lookahead bias are major pitfalls in building quant models.
What is Quantitative Trading?
Quantitative finance is the application of mathematical models, statistical analysis, and computer algorithms to evaluate financial markets and execute trades. Rather than relying on subjective human judgment or qualitative stories about a company’s management team, quantitative analysts ("quants") search for mathematical patterns, anomalies, and statistical relationships in market data.
In this guide, we will explore the core concepts of factor investing, backtesting, and how modern algorithmic systems manage multi-million dollar portfolios.
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1. The Core Abstraction: Factor Investing
Factor investing is a systematic investment strategy that targets specific drivers of return across asset classes. Instead of viewing a stock as an indivisible asset, quants view stocks as a "bundle of characteristics" or factors.
The mathematical foundation rests on the Capital Asset Pricing Model (CAPM) and its extension, the Fama-French Three-Factor Model:
Where: - $R_i$: Return of the asset - $R_f$: Risk-free interest rate - $R_m - R_f$: Market risk premium - SMB (Small Minus Big): Size factor (historical premium of small-cap stocks over large-cap stocks) - HML (High Minus Low): Value factor (premium of value stocks with high book-to-market ratios over growth stocks) - $\\alpha$ (Alpha): Unexplained excess return generated by active management - $\\epsilon$: Random noise/error term
Over the decades, academic research has identified several major equity factors that consistently beat broad market indices over long time horizons:
| Factor | Description | Typical Metrics |
|---|---|---|
| Value | Cheap stocks outperform expensive stocks | Low P/E, low Price/Book, high FCF Yield |
| Momentum | Stocks that have performed well continue to perform well | 6-month and 12-month relative strength |
| Quality | Companies with strong, stable balance sheets outperform | High Return on Equity (ROE), low debt, stable earnings |
| Size | Smaller companies outperform larger ones over time | Market capitalization ranges |
| Low Volatility | Stocks with stable, low-beta price movements yield better risk-adjusted returns | Low standard deviation, low Beta |
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2. Systematic Backtesting: Separating Signal from Noise
A backtest is a historical simulation of how an algorithmic strategy would have performed in the past. It serves as a testing ground for quant models before real capital is deployed.
To build a reliable backtest, quants must control for several cognitive and statistical biases:
Survivorship Bias This occurs when a quant tests a strategy using only the companies currently listed in an index. For example, testing an S&P 500 strategy from 2010 to 2026 using the current 2026 list ignores all the companies that went bankrupt, merged, or were delisted during those 16 years. This leads to an artificial inflation of historical returns.
Lookahead Bias This occurs when a model uses information that was not actually available at the simulated point in time. For example, assuming a company's Q1 financial statements were available on March 31, when in reality the company did not file them with the SEC until April 25.
Overfitting (Data Snooping) Overfitting happens when a model is trained too closely on historical data. By adding hundreds of parameters, a model can perfectly match historical charts (achieving 100% simulated win rates) but perform terribly in real-time forward trading because it modeled noise rather than an actual economic signal.
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How MiroMint Builds Quantitative Scores
MiroMint’s backend engines run daily factor scoring pipelines. Our systems ingest: 1. Fundamental factor data: Balancing leverage, solvency, and profitability scores. 2. Technical factor data: Momentum indicators (RSI, moving average crossovers) and market capitalization parameters. 3. Alternative data: News sentiment vectors and macro inputs.
These factors are fed into our multi-factor scoring engine, which generates the 0-100 AI Score found across the terminal. This provides retail investors with institutional-grade quantitative screening capabilities at the click of a button.