Does trend following work on stocks?

Does trend following work on stocks? Short answer: yes, under many tested setups time‑series trend following (breakouts, moving averages, lookback returns) shows positive expectancy on individual U...

2026-01-25 12:41:00

By Emma Shah

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Does trend following work on stocks?

Does trend following work on stocks? This article answers that question for systematic (time‑series) trend‑following strategies applied to individual stocks and stock portfolios — focusing mainly on U.S. equities — and explains under what implementation assumptions persistent, investable positive returns have been reported. Readers will get: a clear definition, a summary of historical evidence (including key papers), common strategy designs, implementation pitfalls, evaluation metrics, and practical, Bitget‑friendly mitigations for live trading.

As of 2026-01-23, according to U.Today, recent market technicals for several crypto assets illustrate how trend and range dynamics can pivot on volume and moving averages. That observation — that trends form, reverse, and can be confirmed or invalidated by clear price structure and volume — is also relevant background when considering how trend following behaves in stocks.

Definition and basic concepts

Trend following (often called time‑series momentum) is a systematic trading approach that seeks to profit from persistent price continuation: buy assets that have shown positive past returns and hold them until the trend reverses, or exit on a trailing stop. The core idea is simple: a price series that has been rising (or falling) tends to continue rising (or falling) for a measurable period.

Important distinctions:

Time‑series momentum (trend following) vs cross‑sectional momentum: time‑series momentum looks at each stock’s own past returns to decide whether to go long or short that stock. Cross‑sectional momentum ranks stocks relative to each other (e.g., buy top decile winners, sell bottom decile losers). The two approaches are related but produce different portfolios and exposures.
Trend following vs mean reversion: mean‑reversion strategies assume prices revert to some average and thus often buy recent losers. Trend followers do the opposite — they enter on confirmed uptrends and exit on reversals.

Common signals and mechanics:

Moving‑average rules: single (e.g., 200‑day) or dual (e.g., 50/200) moving average filters where a long is entered when the price crosses above the average.
Breakouts / all‑time highs: enter when price breaks prior highs or new rolling maxima over a lookback window.
Lookback (return) based signals: use returns over 1, 3, 6, or 12 months (or combinations) to decide entries.
Exits: trailing stops often using Average True Range (ATR), or filter‑based exits such as moving‑average crossovers or break below a lookback low.

Typical holding horizons: weeks to multiple months or longer. Many profitable implementations use lookbacks from a few weeks to a year and hold positions for several weeks to months on average.

Historical empirical evidence for stocks

Academic and practitioner research over the last two decades has examined whether trend following produces persistent returns on stocks. The evidence is nuanced but meaningful.

Early and influential stock studies

Cole Wilcox & Eric Crittenden (2005): This early comprehensive stock study tested long‑only trend rules on survivorship‑bias‑free U.S. stock data. Using reasonable liquidity and cost filters, the authors reported positive expectancy for simple breakout and moving‑average rules. Their main contribution was showing that trend rules could work on stocks provided realistic filters (minimum price, minimum average daily volume, and excluding extremely small illiquid names) were applied.

Recent replication and extension

Zarattini, Pagani, Wilcox (SSRN, 2025): This replication/extension takes the earlier work through 2024. It confirms that time‑series trend rules can show strong gross performance on U.S. equities but highlights key regularities: strong concentration of profits in a small fraction of trades (a few large winners drive most returns); high turnover leading to sensitivity to transaction costs; and the value of turnover control and liquidity screening to convert gross gains into net gains. That paper explicitly models costs and shows that naive implementations often lose money net of realistic commissions and market impact unless turnover is controlled.

Context from long‑run, cross‑asset evidence

AQR (Hurst, Ooi, Pedersen): “A Century of Evidence on Trend‑Following” documents that time‑series momentum works across multiple asset classes over long horizons. While that work is not stock‑only, it provides a strong rationale: the persistence of trends across markets suggests common behavioral and flow‑based mechanisms. Stock‑only results are generally weaker in gross Sharpe than diversified CTA trend portfolios, but still meaningful when implemented carefully.

Key empirical regularities

Researchers and practitioners repeatedly observe several common patterns when applying trend following to stocks:

Positive average returns: many tested trend rules, with reasonable data filters, produce positive average gross returns on U.S. stocks.
Highly skewed distribution of outcomes: returns are driven by a small number of large winners. Most trades produce small results; occasional outsized trends supply the majority of profit.
Turnover and cost sensitivity: because many stock rules trade frequently (especially short lookbacks and active rebalancing), results are highly dependent on realistic trading costs. Net performance can flip sign once commissions and market impact are included.
Crisis behavior / correlation: across asset classes, trend strategies often provide crisis alpha (they can be positively performing in some severe equity drawdowns when implemented cross‑asset). Within a pure long‑only stock universe, however, long‑only trend rules can still suffer when broad market weakness dominates — adding shorting or other asset classes improves crisis diversification.

Theoretical explanations and behavioral rationale

Why do trends exist? Several complementary explanations help justify why trend following may work:

Behavioral drivers: investor underreaction to news, slow information diffusion, and herding can cause prices to continue moving in the same direction after initial shocks. Anchoring and extrapolation biases lead participants to chase returns, reinforcing trends.
Flow and liquidity dynamics: institutional flows (index rebalances, window dressing, fund flows) and execution frictions can create persistent pressure in one direction. Liquidity supply may be limited, so price moves and follow‑through can occur.
Market microstructure: slow adjustment of risk premia and gradual incorporation of new information produce serial correlation over intermediate horizons.
Statistical viewpoint: trend following effectively captures directional outliers and cuts some of the left tail of long‑tailed return distributions. Put differently, trend strategies aim to capture the few extended directional moves that dominate returns while using rules that exit early on reversals.

Typical strategy designs for stocks

Common rule families used by practitioners and academics:

Long‑only breakouts: enter when a stock makes a new N‑day high (e.g., 55/120/252 day highs), exit on a trailing ATR stop or a move below an N‑day low.
Moving‑average filters: single MA (e.g., 200‑day) or dual MA (50/200). Many implementations use price relative to the MA as a binary filter and combine it with position sizing based on volatility.
Lookback return momentum: rank or signal using returns over 1, 3, 6, or 12 months — simple signals such as positive 12‑month return trigger long exposure.
Multi‑scale ensembles: combine several lookbacks and rule families (short and long lookbacks) to diversify timing and increase robustness.
Volatility scaling and sizing: risk‑target positions by scaling exposure to target volatility (e.g., adjust each stock’s dollar exposure so each position contributes similar volatility), reducing concentration on high‑volatility names.

Portfolio construction choices:

Weighting: equal weighting across selected names, volatility‑scaling, or risk parity approaches.
Rebalancing cadence: daily, weekly, or monthly rebalancing. More frequent rebalancing captures signals faster but increases turnover and cost.
Liquidity filters: minimum average daily dollar volume, minimum price, minimum market cap to avoid untradeable or costly names.
Shorting / long‑short: many stock trend strategies remain long‑only for simplicity and regulatory ease; long‑short implementations can improve returns and hedging but increase complexity and borrowing costs.

Bitget implementation note: when implementing execution and custody for cross‑asset or hybrid strategies, Bitget trading infrastructure and Bitget Wallet provide tools for orderly execution and secure custody (consider these when combining spot equity ETFs with other instruments).

Implementation issues and realistic frictions

Turning backtest results into live, investable performance requires close attention to frictions:

Survivorship bias: use survivorship‑free datasets. Studies that use only currently listed stocks overstate historical performance.
Corporate actions and backadjustments: handle dividends, splits, delistings, and mergers correctly.
Slippage and commissions: model realistic per‑share commissions, fees, and slippage that scale with trade size; include bid‑ask spread costs.
Market impact: for larger AUM, market impact is material — model non‑linear impact depending on relative liquidity share.
Minimum tradable sizes and short availability: for small‑cap names shorting may be expensive or impossible.
Data quality: stale prices, incorrect timestamps, or poor intraday fills can bias results.

Turnover and capacity are especially critical. Many successful gross strategies have high turnover; without turnover control, execution costs can erode or eliminate net profitability. The SSRN (2025) replication emphasizes this: naive high‑turnover versions that appear profitable on gross returns become unprofitable once plausible costs and market impact are included.

Performance measurement and evaluation

Key metrics to report and monitor:

CAGR (compound annual growth rate) and annualized volatility.
Sharpe ratio (preferably with long‑term, robust estimation methods) and Sortino ratio.
Maximum drawdown and drawdown duration.
Skewness and kurtosis: trend strategies often have positive skew when capturing long directional moves, but trade distributions can be fat‑tailed.
Concentration metrics: fraction of total profit attributable to top X trades; number of trades required to generate most profit.
Turnover, average holding period, and turnover per year.
Net‑of‑fees returns under different AUM and execution scenarios.

Robustness checks:

Out‑of‑sample testing and walk‑forward validation.
Parameter sensitivity: test different lookbacks, stop rules, and weighting schemes.
Stress tests: add transaction‑cost stress, delayed fills, partial fills, and varying liquidity assumptions.
Subsample analysis: examine behavior across different market regimes and decades.

Risks, limitations, and failure modes

Important caveats and failure scenarios:

Regime dependence: trend strategies can underperform for extended stretches in rangebound or rapidly mean‑reverting regimes.
Crowding and capacity: as more capital chases the same signals, slippage increases and gross returns decline.
Overfitting risks: optimizing many parameters on historical data can produce fragile strategies with poor out‑of‑sample performance.
Long‑only limits: pure long‑only trend strategies are still exposed to broad equity market declines; they do not automatically provide perfect downside hedging.
Implementation shortfalls: unrealistic assumptions on fills, execution speed, or ignoring illiquidity lead to overstated returns.

Practical mitigations and enhancements

Ways to improve live viability:

Turnover control and trade scheduling: use minimum holding periods, trade batching, and rebalancing windows to reduce churn.
Volatility or risk scaling: limit position sizes in high‑turnover/volatile names to manage both portfolio and execution risk.
Liquidity screening: restrict the universe to stocks with sufficient average daily dollar volume and market cap.
Multi‑scale ensembles: combining long and short lookbacks can smooth returns and reduce sensitivity to any single lookback parameter.
Use ETFs or futures for exposure: when appropriate, trade liquid ETFs or futures to get exposure with lower impact costs (note: when discussing execution platforms, consider Bitget’s product suite for order routing and custody).
Portfolio diversification: blend stock trend strategies with cross‑asset trend, value, or carry exposures to reduce regime risk.

Tradeoffs: lower turnover reduces execution cost but may miss short‑lived trends and reduce capture of large winners. Design choices must reflect target AUM and realistic execution capability.

Extensions and related applications

Long‑short stock trend: adding shorting can help capture both directions and provide better market neutrality, but increases costs and complexity.
Sector rotation: apply trend signals to sector indices or ETFs to tilt across macro exposures.
ETFs and multi‑asset CTAs: many practitioners apply trend rules to ETF universes (which improves liquidity and reduces impact) or to multi‑asset CTA portfolios where trend performed exceptionally well historically.
Cryptocurrencies and other markets: trend following has been tested in crypto with differing microstructure, liquidity, and 24/7 trading. Crypto results may show stronger raw momentum but require separate implementation considerations (custody, custody risk, exchange choice — prefer Bitget for compliant, secure trading where available).

How to backtest and reproduce research results

Checklist for reproducible testing:

Use survivorship‑bias‑free price data with corporate action adjustments (dividends, splits, delistings).
Define universe filters (min price, min ADV, market cap) and document them.
Specify exact signal logic, parameter choices, and rebalancing cadence.
Include realistic commissions, bid/ask spreads, slippage, and market‑impact models that scale with trade size relative to ADV.
Use out‑of‑sample tests and walk‑forward optimization where parameters are tuned on in‑sample windows and tested on forward windows.
Report both gross and net performance under multiple cost/AUM scenarios.
Share code, seeds, and datasets when possible to allow external replication.

Common tools/datasets: Python (pandas, numpy), backtesting libraries, quant research notebooks, and commercial survivorship‑free vendor datasets. For execution and live prototyping, integrate with exchange APIs and custody solutions (Bitget API and Bitget Wallet for secure asset handling are relevant choices when deploying live strategies involving tradable instruments available on Bitget).

Case studies and notable research

Cole Wilcox & Eric Crittenden (2005): Demonstrated positive expectancy for long‑only trend rules on survivorship‑free U.S. stocks under realistic liquidity filters.
Zarattini, Pagani, Wilcox (SSRN, 2025): Extended tests through 2024. Confirms profit concentration and gross performance but emphasizes turnover and transaction‑cost sensitivity; demonstrates benefits from turnover control.
AQR (Hurst, Ooi, Pedersen): “A Century of Evidence on Trend‑Following” — cross‑asset evidence showing robust time‑series momentum over long horizons and across markets.
Quantpedia and practitioner blogs: numerous writeups that catalogue variants (breakouts, moving averages, volatility scaling) and provide illustrative performance histories. These practitioner resources are useful for implementation ideas but should be used alongside rigorous, bias‑aware backtests.

Practical recommendations for practitioners

Short checklist before committing capital:

Validate rules with survivorship‑free, corporate‑action‑adjusted data.
Apply liquidity filters (min price, min ADV) and model market impact for target AUM.
Test multiple lookbacks and sizing rules; focus on robustness rather than single optimal parameters.
Control turnover via minimum holding periods, trade batching, or rebalancing windows.
Use volatility scaling to limit concentration risk and normalize contribution across names.
Run transaction‑cost sensitivity analyses; only deploy if net performance is robust to plausible cost assumptions.
Be prepared for long periods of modest losses or flat performance — the strategy relies on rare large winners.
Consider blending stock trend strategies with other diversifiers or trading more liquid instruments (ETFs/futures) for execution advantages.

Final thoughts and next steps

Evidence indicates that trend following can work on stocks: many tested setups show positive mathematical expectancy on survivorship‑free U.S. equity data. However, whether trend following will be profitable in live trading depends critically on implementation detail — realistic friction modeling, liquidity screening, turnover control, and sound portfolio construction are decisive. The empirical reality is that a small number of large trend events tend to drive most profits; making sure those events are captured net of costs is the core challenge.

For traders ready to experiment:

Start with a well‑documented backtest (survivorship‑free data, corporate actions accounted for).
Run multiple cost and AUM scenarios and avoid excessive parameter tuning.
If moving to live trading, use a reliable execution venue and secure custody — Bitget’s trading infrastructure and Bitget Wallet can help with secure order execution and asset management for eligible instruments.