Individual Investors Long-Only Momentum NYSE Systematic Trading Transaction Costs

Momentum Trading for Individual Investors: What the Research Shows

Summary

A 2015 study of 19 years of NYSE data quantifies when long-only momentum strategies are profitable after realistic transaction costs, and how rebalancing frequency should scale with account size.

February 11, 2026 · 6 min read

Original paper Profitable momentum trading strategies for individual investors

Bryan Foltice, Thomas Langer

Financial Markets and Portfolio Management 2015 DOI: 10.1007/s11408-015-0246-4 ↗

Key findings

Long-only momentum is profitable for individual investors at minimum starting capital of approximately $5,000 after realistic transaction costs on NYSE data from 1991 to 2010
Optimal rebalancing frequency scales with account size: bi-yearly at $5,000, quarterly to monthly at $25,000, monthly or more frequent at $50,000 and above
Overlapping momentum strategies improve risk-adjusted returns up to an optimal frequency, after which transaction costs dominate and performance declines
Transaction costs are the primary determinant of strategy viability, more significant than the choice of ranking metric or holding period
Standard academic momentum results (short selling, hundreds of positions, no costs) are not replicable by individual investors and require simplified long-only versions to be practically applicable

Most academic momentum research is not directly applicable to individual investors. The standard methodology goes long recent winners, short recent losers, holds hundreds of positions simultaneously, and omits transaction costs. The resulting return figures look compelling on paper but are inaccessible in practice: short selling is unavailable or impractical for most retail accounts, the capital requirements to hold hundreds of positions are substantial, and cost-free assumptions overstate real returns significantly.

A 2015 paper by Bryan Foltice (Butler University) and Thomas Langer (University of Munster) addresses this directly. Published in Financial Markets and Portfolio Management (vol. 29, pp. 85-113), the paper tests a simplified, long-only momentum strategy with realistic constraints on NYSE data from July 1991 to December 2010. The full paper is available at doi.org/10.1007/s11408-015-0246-4, with an open-access version at Butler University Digital Commons.

Methodology

The study uses NYSE data across a 19.5-year window covering multiple full market cycles, including the 2000-2002 drawdown and the 2008 financial crisis. The strategy is long-only, concentrated in a small number of top-ranked momentum stocks, and evaluated with realistic transaction costs included in all performance calculations.

The ranking metric follows the standard academic approach: past return over a formation period, excluding the most recent month to avoid short-term reversal contamination. What distinguishes this paper is the constraint set applied before evaluating performance:

No short positions
Small position count (practical for individual accounts)
Per-share commissions and bid-ask spread modeled explicitly
Minimum investment amounts tested across a range

The paper also tests what it calls an overlapping momentum strategy: rather than a single full rebalance at the end of each holding period, positions are entered more frequently on a rolling basis using a subset of capital. This creates time-diversification across entry points while keeping position counts manageable.

Results

The main finding: the simplified long-only strategy is profitable after transaction costs for accounts starting at approximately $5,000. Below that threshold, commissions as a percentage of capital become large enough to eliminate the edge.

The second finding concerns rebalancing frequency. Increasing frequency in the overlapping strategy initially improves risk-adjusted returns as the strategy captures more momentum signals and diversifies entry timing. Beyond an optimal point, transaction costs dominate and returns decline. The optimal frequency scales with account size:

Around $5,000: bi-yearly rebalancing (every 6 months)
Around $25,000: quarterly to monthly
Around $50,000 and above: monthly or more frequent

This relationship between account size and optimal rebalancing frequency is the most practically useful output of the paper. It gives individual traders a concrete framework for calibrating a real implementation rather than applying academic parameters that were never designed for retail constraints.

Transaction Costs as the Primary Variable

The paper is explicit that transaction costs, not the ranking metric or holding period choice, are the dominant variable in determining whether a momentum strategy is viable for a given account. A $10 commission on a $500 position is 2% per trade before any market movement. Compounded across multiple rebalances annually, the drag is sufficient to eliminate otherwise positive strategies.

This is one reason why backtesting platforms that model friction accurately matter for strategy evaluation. RealTest allows direct configuration of per-share commissions, slippage, and limit order buffers in strategy code. Running a backtest without these parameters produces numbers that have no bearing on live trading performance. The Foltice-Langer paper makes the same argument from an academic direction: cost modeling is not optional.

Background: The Momentum Effect

Momentum as a systematic phenomenon was documented by Jegadeesh and Titman in their 1993 paper "Returns to Buying Winners and Selling Losers". They found that stocks with strong returns over a 3-12 month formation period tended to continue outperforming over the following 3-12 months. The finding has been replicated across asset classes and markets over the subsequent 30 years.

The behavioral explanation centers on investor under-reaction to new information: prices adjust more slowly than fundamentals warrant, creating persistent trends. This eventually reverses over longer horizons (1-3 years), which is why momentum strategies typically avoid holding positions for extended periods.

The challenge for implementation has always been translating the academic finding into a practical strategy that survives real costs. Foltice and Langer quantify where that threshold sits for individual investors.

Relevance to SetupAlpha Strategies

SetupAlpha strategies are backtested in RealTest with Interactive Brokers commission rates and realistic slippage specified directly in strategy code. The paper's finding: cost modeling determines viability, not the choice of ranking metric. That is the same criterion applied before anything is published here.

Two strategies in particular reflect the long-only momentum framework the paper describes. The Weekly Pullback Strategy uses weekly entry signals to balance momentum participation against transaction frequency. The Modern Breakout Strategy targets high-momentum breakout conditions with daily signals filtered to limit trade frequency. Both are tested in RealTest with Norgate Data, the same survivorship-bias-free data standard used in academic research.

The account-size-to-frequency relationship from the paper is relevant for position sizing decisions. Traders running SetupAlpha strategies on smaller accounts should be more conservative about rebalancing frequency and position count than traders with larger accounts, consistent with the paper's findings.

Replicating the Methodology in RealTest

For traders who want to test momentum parameters directly, the paper's framework translates to RealTest in the following steps:

Define the universe: NYSE or broader US equity set, using Norgate Data with point-in-time constituents
Set the ranking metric: total return over the past 12 months, excluding the most recent 20 trading days
Set holding period and rebalancing frequency based on account size, using the paper's thresholds as a starting point
Configure commissions and slippage to match your actual broker rates
Run across the full available history including drawdown periods, not just favorable windows

Strategies that survive this test with positive risk-adjusted returns and acceptable drawdowns have cleared the same bar the paper sets for viable individual investor momentum strategies.

Limitations

The paper does not address momentum crashes. Momentum strategies can experience sharp, rapid drawdowns during abrupt market reversals (the spring 2009 recovery is the standard example). The study period includes 2008 but the momentum-specific crash of 2009 is not the primary focus.

Tax treatment is not considered. More frequent rebalancing generates short-term capital gains, which for US investors in taxable accounts reduces after-tax returns relative to the pre-tax figures in the paper.

The results are specific to NYSE-listed equities. Performance on other exchanges, in smaller-cap universes, or in different market structure periods may differ.

These are scope limitations, not flaws. The paper answers a specific question for a specific context: whether simplified long-only momentum works for individual US equity investors after transaction costs. The answer it provides is well-supported and practically useful within that scope.

Citation: Foltice, B. and Langer, T. (2015). Profitable momentum trading strategies for individual investors. Financial Markets and Portfolio Management, 29(2), 85-113. https://doi.org/10.1007/s11408-015-0246-4

Key terms

Momentum: The empirical tendency of assets with strong recent performance to continue outperforming in the near term. Typically measured over a 3-12 month formation period, excluding the most recent month to avoid short-term reversal effects.
Long-Only Strategy: A strategy that only takes long positions (buys securities), without short selling. The standard constraint for individual investors who lack margin accounts or access to stock lending.
Risk-Adjusted Return: Return measured relative to the risk taken to achieve it. Common metrics include Sharpe ratio (excess return divided by total volatility) and Sortino ratio (which penalizes only downside volatility).
Overlapping Momentum Strategy: A momentum implementation where new positions are entered on a rolling schedule more frequently than the full holding period, creating time-diversification across entry points rather than a single periodic rebalance.
Rebalancing Frequency: How often portfolio positions are adjusted to reflect updated momentum rankings. Higher frequency captures signals faster but increases transaction costs. The Foltice-Langer paper shows the optimal frequency is a function of account size.
Survivorship Bias: A data construction error where the historical dataset only includes securities that survived to the end of the test period. This overstates backtest returns by excluding companies that were delisted, went bankrupt, or were acquired at a loss. Norgate Data provides point-in-time constituent data that avoids this issue.
Transaction Costs: Total costs of executing a trade: broker commissions, bid-ask spread, and market impact. For small accounts, these can represent 1-3% per round trip. The Foltice-Langer paper identifies this as the dominant variable in determining whether a momentum strategy is viable.

Frequently asked questions

What is momentum trading?

Momentum trading is a systematic strategy that buys stocks with strong recent performance, based on the empirical finding that recent winners tend to continue outperforming over the following 3-12 months. Documented by Jegadeesh and Titman in 1993, momentum is one of the most replicated return anomalies in financial research.

What minimum account size does this paper find is required for momentum to be profitable?

Foltice and Langer find that individual investors need approximately $5,000 in starting capital for a simplified long-only momentum strategy to produce positive returns after transaction costs. Below this level, commissions as a percentage of capital are large enough to eliminate the edge.

How does rebalancing frequency affect momentum strategy performance?

According to the paper, optimal rebalancing frequency scales with account size. Smaller accounts around $5,000 should rebalance bi-yearly to minimize cost impact. Accounts around $25,000 can rebalance quarterly to monthly. Accounts of $50,000 or more can support monthly or more frequent rebalancing while still clearing the transaction cost hurdle.

Why do standard academic momentum strategies not work for individual investors?

Standard academic momentum strategies require short selling (not available to most retail investors), involve hundreds of simultaneous positions (requiring substantial capital), and ignore transaction costs entirely. Applying these constraints in the real world significantly reduces the performance figures typically reported in academic research.

What is an overlapping momentum strategy?

An overlapping strategy enters new momentum positions on a rolling basis more frequently than the full holding period, rather than doing a single full portfolio rebalance at fixed intervals. For example, instead of rebalancing the full portfolio quarterly, you might rotate one-third of positions monthly. The paper shows this approach improves risk-adjusted returns up to an optimal frequency, after which excess trading costs reduce performance.

Does the strategy hold up through bear markets?

The paper tests across the full NYSE dataset from July 1991 to December 2010, which includes the dot-com drawdown from 2000 to 2002 and the 2008 financial crisis. Performance is evaluated across the full period rather than filtered to favorable windows. The paper does not specifically address the 2009 momentum crash, which is a known risk for momentum strategies during abrupt market reversals.

What data period and universe does the study use?

NYSE data from July 1991 to December 2010, a 19.5-year window covering multiple market cycles. The universe is limited to NYSE-listed equities. Performance on other exchanges or in different market structure periods may differ.

How do I backtest momentum strategies with realistic costs?

In RealTest, configure per-share commissions, slippage, and limit order buffers directly in strategy code. For replicating the paper's methodology, set the ranking metric to 12-month return excluding the most recent month, use point-in-time Norgate Data to avoid survivorship bias, and configure rebalancing frequency according to your account size using the paper's thresholds as a baseline.

How does this research relate to SetupAlpha strategies?

SetupAlpha strategies are backtested with Interactive Brokers commissions and realistic slippage, consistent with the cost-modeling approach the paper identifies as necessary for valid evaluation. The Weekly Pullback and Breakout strategies apply long-only momentum principles with trading frequencies calibrated to the account sizes they are designed for.

What limitations does the paper have?

The paper does not address momentum crashes (sharp reversals in early recovery periods like 2009), tax treatment of frequent short-term gains, or performance outside NYSE-listed US equities. These are scope limitations: the paper answers a specific question about simplified long-only momentum for individual US equity investors after transaction costs, and within that scope the findings are well-supported.