This document summarizes the theory and practice behind the factors implemented in this repository: value, momentum, and quality. It is intended as educational context for the strategies in the course.
Idea: Stocks that are "cheap" relative to fundamentals (earnings, assets, sales, cash flow) tend to outperform over the long run.
Theoretical basis:
- Fama–French: value (e.g. high book-to-market) earns a risk premium.
- Behavioral: investors overreact to bad news, so value stocks are underpriced.
- Risk-based: value firms are riskier (distress, cyclicality), so higher expected return.
Common metrics (lower = cheaper = more value):
- P/E (price-to-earnings): price per dollar of earnings. Fails for negative earnings.
- P/B (price-to-book): market value vs book value. Less meaningful for intangibles-heavy firms.
- P/S (price-to-sales): useful when earnings are negative or volatile.
- EV/EBITDA: enterprise value to operating cash flow; good for cross-sector comparison.
- EV/GP (enterprise value to gross profit): less affected by leverage and accounting choices.
In this repo: The RV (Robust Value) score is the average of percentiles across P/E, P/B, P/S, EV/EBITDA, and EV/GP (lower raw metric → higher percentile → higher RV). We select the top 50 by RV for the value strategy.
Idea: Stocks that have performed well over the recent past (e.g. 6–12 months) tend to continue performing well in the short term.
Theoretical basis:
- Underreaction to information (earnings, news) so trends persist.
- Behavioral: herding and slow information diffusion.
- Risk: momentum as compensation for time-varying risk exposure.
Common implementations:
- Price momentum: past 2–12 month return (skip most recent month to avoid short-term reversal).
- High-quality momentum: require strength across multiple horizons (e.g. 1m, 3m, 6m, 12m) to avoid one-off spikes (e.g. FDA approval).
In this repo: HQM (High-Quality Momentum) uses 1m, 3m, 6m, and 1y price return percentiles and averages them. Top 50 by HQM form the momentum portfolio. Alternatives implemented: 52-week high proximity, risk-adjusted momentum (return/volatility).
Idea: Profitable, stable, low-leverage firms (high "quality") tend to have higher risk-adjusted returns.
Common metrics:
- ROE (return on equity): profitability.
- Earnings stability: low volatility of earnings over time.
- Low leverage: debt/equity or interest coverage.
In this repo: Quality is used as an overlay (filter or score) and in the multi-factor strategy (value + momentum + quality weights). Quality score can be based on ROE and optional earnings stability.
Combining value, momentum, and quality can improve diversification of signals and reduce regime dependence. In this repo, the multi-factor strategy uses configurable weights (e.g. 1/3 value, 1/3 momentum, 1/3 quality), ranks by composite score, and selects the top 50.
- Equal weight: simplest; each name has same weight.
- Risk parity / volatility weighting: weight inversely to volatility so each position contributes similarly to risk.
- Conviction: weight by score (e.g. higher HQM or lower RV → higher weight).
- Optimization: minimum variance (minimize portfolio vol) or max Sharpe (maximize risk-adjusted return) subject to constraints.
- Survivorship bias: using only current index members overstates historical returns; use point-in-time constituents when possible.
- Look-ahead bias: ensure all inputs (prices, fundamentals) are available at each rebalance date.
- Transaction costs: rebalancing and turnover reduce net returns; model explicitly for realism.
- Fama, E. F., & French, K. R. (1992). The cross‐section of expected stock returns. Journal of Finance.
- Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers. Journal of Finance.
- Asness, C. S., Frazzini, A., & Pedersen, L. H. (2019). Quality minus junk. Review of Accounting Studies.