In 1993, two academics from the University of Illinois — Narasimhan Jegadeesh and Sheridan Titman — published a paper in the Journal of Finance that changed the way the market thinks about stock selection. The title was dry: Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The discovery, not: stocks that rose the most over the last 3 to 12 months tend to continue rising over the next 3 to 12 months. And the effect was large enough to deliver statistically significant alpha even after discounting transaction costs.
Thirty-three years later, the momentum factor continues to survive in global data — including on B3. This post explains what exactly momentum is, why it works despite being known for decades, and how VORTEX QSP uses the factor within a multifactorial architecture.
What It Is, Mechanically
The canonical formulation is simple. At each rebalance moment (monthly, in the case of VORTEX QSP), you calculate for each stock in the universe the cumulative return over the last L months, excluding the most recent month (to avoid the short-term reversal effect, which operates over 1-4 week horizons). This L is called the lookback period. Typical values fall between 6 and 12 months.
Next, you rank the universe from highest to lowest cumulative return. Stocks at the top of the ranking enter a long portfolio. In the original paper, Jegadeesh-Titman also built the short leg with the worst performers; in Brazilian commercial practice, most investors ignore the short due to operational restrictions and still capture the bulk of the premium.
Why It Works
There are three mainstream explanations — two behavioral, one based on risk.
1. Under-reaction to News
Investors take time to fully incorporate good news. When a company surprises with earnings, the price goes up on the day, but typically continues rising in the following months as analysts revise estimates, institutional funds rebuild positions, and the market processes the new reality. The same holds in the opposite direction — stocks that disappoint continue falling. Bernard and Thomas (1989) documented this post-earnings announcement drift in detail and it explains much of momentum.
2. Over-reaction on Longer Horizons
Investors who enter late extrapolate the recent past as the future. "Rose 40% in 6 months, it'll keep going." Demand feeds the price. The effect eventually reverses — momentum reverses over 3-5 year horizons — but over the 3-12 month interval the trend persists.
3. Risk Nobody Measured Right
Defenders of the Efficient Markets Hypothesis argue that momentum is not alpha but rather compensation for some type of risk that the CAPM doesn't capture — perhaps crash risk (momentum breaks violently in sudden reversals, like April/2009 globally or November/2020 in Brazil). Daniel and Moskowitz (2016) showed that the factor has extreme negative returns during market reversals, so part of the premium might indeed be compensation for tail risk.
The three explanations are not mutually exclusive. Probably momentum exists because of a mixture of all three — and that's why it's so robust.
Momentum on B3: The Empirical Evidence
Brazilian studies confirm the factor in our market. Mussa, Rogers and Securato (2009) analyzed data from 1995 to 2008 and found statistically significant momentum in 6 and 12 month windows. Heineberg and Procianoy (2003) had already observed the same pattern in earlier data. More recently, work with the IBrX-100 universe shows average annual returns of 4 to 8 percentage points above the benchmark for the decile portfolio of top momentum (before costs).
The good news: the factor is strong in Brazil. The bad news: it's also more volatile here than in developed markets — B3 has fewer liquid stocks, and momentum crashes tend to be more acute. That's why momentum alone is risky. Combined with other factors (such as low volatility and quality), it delivers risk-adjusted returns that are much superior.
Common Mistakes When Applying Momentum
Lookback Too Short
Looking at just the last 1-3 months captures noise, not trend. Worse: it captures precisely the short-term reversal — stocks that rose a lot in the last month often fall the next month. The empirical sweet spot falls between 6 and 12 months, and excluding the most recent month is part of the method.
Forgetting About Costs
Momentum requires frequent rebalancing. Each rebalance generates turnover — and turnover generates brokerage costs, bid-ask spreads, and market impact. In academic backtests these costs are underestimated (or ignored). In practice, a poorly calibrated momentum strategy can have excellent gross returns and negative net returns. VORTEX QSP uses hysteresis bands — a stock in the top-15 only leaves the portfolio when it falls outside the top-25 — precisely to control this.
Pure Momentum Without Regime Hedge
In sudden reversals, momentum bleeds. Anyone who ran pure momentum in March/2020 saw acute drawdown. The solution is not to abandon the factor, but to combine it with others that have low or negative correlation in crises — typically low volatility and quality, which defend in risk-off moments.
How We Apply It at VORTEX QSP
Momentum is one of five pillars of the composite score. The calculation is the standard Jegadeesh-Titman (lookback 12-1), normalized cross-section by z-score, aggregated with equal weight to the other factors. The combination reduces the volatility of the isolated factor and improves the Sharpe ratio by around 30-50% relative to pure momentum, in B3 backtests.
The full explanation of the architecture is in Technology. The empirical results from 7.3 years walk-forward — including month-by-month decomposition — are in Performance.
To Close
Momentum is not the only factor. It's not even the best in isolation (low volatility has higher Sharpe). But it's perhaps the most well-documented factor in modern financial literature, and it captures something real about how information diffuses through the market. Decades of attempts to arbitrage it away haven't made it disappear — it just became more sophisticated to exploit.
For a retail investor in Brazil, trying to implement momentum manually is hard: it requires clean data, monthly calculation, discipline to rebalance even when the ranking says counter-intuitive things, and controlled costs. It's precisely this gap that VORTEX QSP solves — systematizing the method with discipline and honest disclosure.
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