Unlike value investing — which has Warren Buffett as its singular, dominant figure — momentum investing was built by several practitioners working in parallel. Some came from trading floors. Others emerged from PhD programmes. All of them arrived at the same conclusion through different paths: recent winners keep winning, recent losers keep losing, and you can build a systematic strategy around that fact.
Cliff Asness studied for his PhD at the University of Chicago under Eugene Fama — the economist who built the theoretical framework for the Efficient Market Hypothesis (EMH), which says past prices contain no useful information about future returns. The irony of what happened next became one of finance’s great stories.
Asness’s dissertation documented price momentum in US stocks with overwhelming statistical force. The data was so strong that Fama — who built his career arguing momentum shouldn’t exist — co-authored a 1996 paper with Kenneth French acknowledging momentum as a genuine anomaly in their own model. Asness’s student had produced evidence his supervisor couldn’t dismiss.
In 1998, Asness founded AQR Capital Management (Applied Quantitative Research). Today AQR manages over $100 billion using systematic, factor-based strategies — momentum is one of their core pillars alongside value and quality. AQR’s research has been the most influential body of work on factor investing in the 21st century.
While Asness was proving momentum in academia, Richard Driehaus was already living it. Often called the “Father of Momentum Investing,” Driehaus built Driehaus Capital from 1982 on a principle that inverted conventional wisdom: buy high and sell higher.
The traditional investment mantra is “buy low, sell high.” Driehaus rejected this entirely. His insight: a stock making new highs has already proved itself. The market has validated the trend. Rather than searching for unloved bargains that might be discovered, Driehaus wanted stocks the market was already endorsing — and was prepared to pay a premium for that confirmation.
His approach: only buy stocks already making new 52-week or all-time highs, with strong earnings acceleration and rising institutional sponsorship. Cut losing positions ruthlessly when they fail to maintain their trend. Never average down into a declining stock. Driehaus achieved approximately 30%+ annualised returns over 30+ years — a real-money live track record predating the academic literature that would later justify it.
While Asness and AQR built institutional-grade factor portfolios, Gary Antonacci asked a different question: how can an individual investor with a simple brokerage account capture most of the momentum premium with minimal complexity and transaction costs?
His answer — the Dual Momentum system — uses just two rules checked once per month. The first rule (relative momentum): compare US stocks to international stocks over 12 months. Own whichever has performed better. The second rule (absolute momentum): is the winning asset actually trending upward, or is it just the least-bad option in a down market? If stocks in general are below their 12-month average, own bonds instead.
This elegantly simple system historically produced better risk-adjusted returns than a 60/40 portfolio, avoided most of the 2008 crash, and sidestepped the worst momentum crashes by switching to bonds when stocks overall were declining. It’s not glamorous — but it works, and any individual can implement it in 15 minutes per month.
Three Approaches Compared
| Investor Type | Signal Used | Buy Trigger | Sell Trigger | Complexity |
|---|---|---|---|---|
| Momentum (Driehaus/AQR) | 12-1 price return ranking | Top quintile of recent performers | Falls out of top 30% ranking | Medium–High |
| Dual Momentum (Antonacci) | Relative + absolute 12-month return | Winning asset class above own MA | Switch to bonds when trend breaks | Low |
| Fundamental / Value | Earnings, cash flow, book value | Price < intrinsic value | Price > intrinsic value or thesis breaks | High |
| Index Investor | None — buy the whole market | Regular contributions | Almost never | Very Low |
Cliff Asness completed his PhD under Eugene Fama — the father of the Efficient Market Hypothesis. What made Asness's momentum research remarkable in that context?
Momentum is not a folk story or a trader’s rule of thumb. It is the most thoroughly documented anomaly in the academic finance literature — tested across more countries, time periods, and asset classes than virtually any other investment strategy. Here is the evidence case, built systematically.
The Landmark 1993 Paper
In 1993, Narasimhan Jegadeesh and Sheridan Titman published “Returns to Buying Winners and Selling Losers” in the Journal of Finance. They sorted US stocks by their prior 12-month return, formed portfolios of top and bottom deciles, and measured subsequent returns. The result: top decile stocks outperformed bottom decile stocks by approximately 1% per month — about 12% annually — for up to 12 months, and this excess return survived adjustment for market beta, firm size, and the value factor.
This was shocking. The three-factor model of the time — market, size, value — was supposed to explain all systematic return differences between stocks. Momentum blew through that model as if it didn’t exist. The academic community had to add a fourth factor.
Based on Fama-French momentum factor research. Top decile = stocks with highest 12-month return (excluding most recent month). Bottom decile = recent losers.
The Global Evidence
If momentum only worked in US data, sceptics could dismiss it as data mining — the result of thousands of researchers combing through the same US dataset until something stuck. Then Clifford Asness, Tobias Moskowitz, and Lasse Pedersen published their 2013 paper “Value and Momentum Everywhere.” They documented momentum across 40+ countries and multiple asset classes simultaneously.
Momentum works in Japan. It works in Germany. It works in Brazil, the UK, Korea, and Australia. It works in currencies, commodities, and government bonds. When the identical signal produces excess returns in markets with different regulations, different investor bases, and entirely different economic structures, the most credible explanation is a universal feature of human psychology — not a data artifact.
| Research / Finding | Key Result | Significance |
|---|---|---|
| Jegadeesh & Titman (1993) | +1%/month excess return (US stocks) | First rigorous proof — survived three-factor model adjustment |
| Jegadeesh & Titman (2001) | Still significant out of sample 1990–1998 | Rules out 1993 being a data-mining accident |
| Asness et al. (2013) | Works in 40+ countries + bonds, currencies, commodities | Rules out US-specific artifact — universal human behaviour |
| Daniel & Moskowitz (2016) | Momentum crashes: −2% to −5% skewness in tail | Quantified crash risk precisely; crash more common after bear markets |
| AQR Momentum Factor (live) | ~3–5% annualised above market, long-run | Live fund data, not just backtests |
| Momentum vs Value correlation | ~−0.4 to −0.6 correlation | Diversification benefit of combining both factors |
| Transaction costs (Frazzini et al.) | Net premium 1–2% for retail monthly traders | Realistic cost adjustment shrinks the premium significantly |
Momentum vs Other Factors
Momentum does not exist in isolation. Academic finance now recognises five major equity factors that produce long-term excess returns: market, size, value, momentum, and quality (also called profitability). Momentum has the highest raw returns of the non-market factors — but also the highest crash risk. Combining factors is where the real risk-adjusted value lies.
Momentum Returns by Decade
The long-run premium is real — but it has not been uniform across time. Understanding which environments produce the best and worst momentum results is critical for any practitioner.
Approximate WML (Winners Minus Losers) factor returns. Momentum struggled in the 1930s (frequent reversals) and 2000s (two major crashes). Strongest in trending decades: 1980s and 1990s.
The Jegadeesh and Titman 1993 paper demonstrated momentum by:
Momentum is one of the few investment strategies where the signal is completely mechanical and requires no subjective judgement. Here is exactly how it is calculated, why each element exists, and how to apply it in practice.
The 12-1 Signal: Step by Step
Choose your investable universe before calculating anything. S&P 500 is the most practical for US retail investors — sufficient liquidity, 500 stocks for meaningful ranking, low transaction costs. Mid-cap (S&P 400) adds more names with similar results. Avoid applying momentum to the full Russell 2000 — small-cap momentum is theoretically strong but practically expensive to trade.
For every stock in your universe: pull the price 12 months ago and 1 month ago. Calculate the percentage change between those two dates. This skips the most recent month entirely — a key technical detail explained below. This is your raw momentum score for each stock.
Sort all 500 stocks (or however many in your universe) from highest to lowest 12-1 return. Assign each a percentile rank: the stock with the highest return is rank 100, the lowest is rank 1. The top 20% (rank 80–100) is your high-momentum universe.
Buy equal-weight positions in the top quintile (top 100 stocks from the S&P 500 if using that universe). Equal weight avoids concentration in the most volatile momentum stocks. Some practitioners use volatility-adjusted sizing — allocating less to high-volatility stocks — which improves risk-adjusted returns slightly but adds complexity.
Before buying: (a) exclude stocks below their own 200-day moving average — these are 'broken momentum' stocks trending down even within the top quintile; (b) exclude recent IPOs under 1 year old — insufficient history for the signal; (c) apply a market filter — if the S&P 500 itself is below its 200-day MA, reduce position sizes by 50% or more.
On your rebalancing date (first trading day of each month recommended), recalculate rankings for the entire universe. Stocks that have fallen below the top 30% ranking get sold. New entrants into the top 20% get bought. Stocks remaining in the top quintile — usually 70–80% of the portfolio — are held. This reduces unnecessary turnover.
Worked Example: NVDA in December 2022 vs December 2023
Theory becomes clear with a real example. Let’s apply the 12-1 calculation to Nvidia (NVDA) at two very different points in time.
Why You Skip the Most Recent Month
The exclusion of the most recent month is not arbitrary. There is a well-documented short-term reversal effect: stocks that just had an exceptional month tend to slightly underperform in the immediate next month. This is a microstructure phenomenon driven by temporary liquidity effects, profit-taking, and mean-reversion of short-term overshoot.
This reversal effect is entirely separate from 12-month momentum. Including the most recent month “contaminates” the clean 12-month signal with this short-term noise. By skipping it, you get a purer reading of the underlying trend without the month-end noise.
Cross-Sectional vs Time-Series Momentum
Buy stocks ranked highest relative to other stocks in the universe. This is the AQR / Driehaus approach. A stock can have a negative 12-month return and still be in the top quintile if every other stock fell further.
Buy a stock only when it is above its own 12-month historical return (absolute trend filter). This is the Antonacci Dual Momentum approach. If everything is falling, you go to cash or bonds instead of buying the least-bad option.
When calculating 12-1 momentum, you exclude the most recent month. Why?
Every investment strategy has a specific environment where it fails. For momentum, that environment is a violent, sustained market reversal — particularly one that comes after an extended bear market, when the most beaten-down stocks suddenly become the most attractive to every buyer in the market simultaneously. What follows is not abstract theory. It is a specific story about what happened to real money.
The 2009 Crash: A Story in Three Acts
The 2020 COVID Crash: Faster, Shorter, Same Pattern
In February–March 2020, the COVID-19 pandemic caused the fastest bear market in history. Momentum portfolios held the winners of the 2019 bull market: technology, cloud software, consumer discretionary. When the market crashed 34% in one month, these momentum holdings also fell — but not disproportionately. So far, no momentum crash.
Then came November 2020. Pfizer’s vaccine announcement on November 9 triggered one of the most concentrated single-day rotations in decades. Beaten-down cyclicals, airlines, hotels, energy companies — everything that had suffered most — surged 20–40% in days. Meanwhile, the defensive and technology stocks that momentum portfolios held were flat or fell. Over November–December 2020, momentum experienced approximately a 35% crash from its September 2020 high. Shorter than 2009, but equally violent in its mechanism.
Why This Is Structural, Not Random
Momentum crashes are not accidents. They follow a predictable structural logic: at market bottoms, the stocks momentum has been avoiding (the biggest losers) are simultaneously the most undervalued, the most shorted, and the most likely to benefit from any positive news. When positive news arrives, every category of buyer — value investors seeking bargains, short sellers covering, corporate buyback programs, institutional rebalancers — buys the same depressed stocks at the same time. The result is a violent, concentrated squeeze against momentum positions.
Momentum winners become momentum because their price trend has persisted. At market bottoms, the most beaten-down stocks (momentum losers) are also simultaneously the most undervalued, the most shorted, and the most oversold. When a catalyst arrives — a Fed pivot, a vaccine, a peace deal — every buyer targets the same discarded stocks simultaneously. The resulting buying pressure is disproportionate and concentrated, creating a violent squeeze.
Most professional momentum funds use 2–3× leverage to amplify their returns. When momentum reverses sharply, a leveraged fund's losses are amplified proportionally. Risk management systems trigger automatic position reductions to maintain safe leverage ratios. As multiple leveraged momentum funds reduce positions simultaneously, they create additional selling pressure on momentum stocks and additional buying pressure on their short positions (the momentum losers) — amplifying the crash in a self-reinforcing cascade. Unlevered retail investors are affected less.
Momentum requires trending markets. In trending markets, it harvests the continuation premium. In choppy, mean-reverting markets, it loses on every whipsaw. The critical problem: you cannot reliably distinguish in real time between a temporary interruption in a bull market trend (buy the dip) and a genuine regime change (get out and wait). Even systematic strategies with sophisticated market filters miss some reversals and avoid some recoveries — no filter is perfect.
Risk Management for Momentum Crashes
The market filter is the most widely adopted tool for crash management. When the S&P 500 is below its 200-day moving average, reduce momentum exposure by 50–100%. Historical analysis suggests this filter reduces crash exposure by approximately 40% at the cost of roughly 1% per year in foregone returns during normal bull market periods. Most practitioners consider that trade-off worth taking.
| Crash Protection Tool | How It Works | Crash Reduction | Performance Cost |
|---|---|---|---|
| 200-day MA market filter | Reduce/exit when S&P below 200-day MA | ~40% fewer crash losses | ~1%/yr in normal markets |
| Dual Momentum absolute filter | Exit to bonds when asset class in downtrend | ~50–60% crash reduction | ~1–1.5%/yr |
| Volatility-adjusted sizing | Smaller positions in high-vol momentum stocks | Reduces crash severity | Minimal — improves Sharpe |
| Factor diversification (50% value) | Value tends to outperform when momentum crashes | ~30–40% total crash reduction | ~0.5%/yr (value drags in bull markets) |
| No leverage | Removes cascade amplification | ~50% reduction vs levered strategy | None for retail investors |
In March 2009, momentum strategies lost 25–30% in a single month. Why did this happen?
The key to successful momentum investing is strict rules with no discretion. The moment you start overriding the signal — “I really believe in this company even though it dropped out of the top quintile,” or “I’ll skip this month’s rebalance because markets feel uncertain” — you have destroyed the momentum premium. You are now doing momentum-influenced stock picking, which has no documented long-term edge.
| Component | Rule | Why This Rule Exists |
|---|---|---|
| Universe | S&P 500 constituents only | Sufficient daily volume for 20–25 positions without meaningful market impact. Avoids illiquidity premium contaminating the momentum signal. |
| Signal | 12-month return, skip last month | Best documented signal in the literature. Skip 1 month to remove short-term reversal noise. Consistent with Jegadeesh & Titman (1993). |
| Selection | Top 20% (top quintile, ~100 stocks) | Enough concentration for premium capture; enough names to diversify away individual stock risk. |
| Position count | 20–25 equal-weight stocks | Diversified enough that any single momentum crash in one sector does not destroy the portfolio. Equal weight avoids volatility-concentration. |
| Rebalance | Monthly, first 3 trading days | Captures most of the monthly premium. First 3 days avoids month-end window dressing effects and start-of-month liquidity. |
| Market filter | 50% cash if S&P below 200-day MA | Systematic crash reduction without eliminating all upside. Does not require predicting reversals — just observing whether the trend has broken. |
| Exit rule | Sell when rank drops below top 30% | Clear, mechanical trigger. Not based on price action or news — based purely on relative rank. Gives a buffer zone to avoid excessive turnover. |
| Sector cap | Max 30% in any single sector | Prevents momentum from becoming a single-sector bet. In AI-driven bull markets, momentum would otherwise concentrate 70%+ in technology. |
| Risk management | Max 6% per position at entry | Hard stop on single-position concentration. Prevents one bad momentum crash from causing outsized portfolio damage. |
| Tax wrapper | ISA (UK) or Roth IRA (US) | High turnover generates frequent short-term capital gains. Tax-advantaged wrappers shelter these entirely. Running momentum in a taxable account is significantly less efficient. |
Walk-Through: January 2024 Rebalance
Let’s pretend it’s January 1, 2024. You pull 12-1 momentum data for all S&P 500 stocks (using the return from January 2023 to November 2023, skipping December 2023). The top quintile at that moment would have included a cluster of AI and technology names that had surged on the 2023 AI boom, several energy names that benefited from post-2022 oil price normalisation, and some industrials.
Nvidia would likely be near rank 1 or 2 — its 12-1 return through November 2023 was approximately +200%. You buy your 20–25 equal-weight positions. In February, you rebalance: most hold, a few rotate out and new entrants replace them. By December 2024, your portfolio would have tracked the AI momentum story, with Nvidia remaining in the top quintile for most of the year and continuing to contribute outperformance.
Tools for Implementation
Filter S&P 500 by 52-week performance, price above 200-day MA. Gives a rough top-quintile approximation. Sufficient for monthly screening.
Factor analysis and backtest tools. Test your specific momentum rules on real historical data. Free tier covers most retail use cases.
For more advanced users: write the exact momentum algorithm and backtest it on institutional-quality data. Steep learning curve but precise.
Interactive Brokers, Fidelity (US), or any ISA provider with zero trading commissions. Monthly rebalancing with commissions eliminates most of the net premium.
Momentum as One Component of a Multi-Factor Portfolio
Most successful practitioners do not run pure momentum. They blend it with other factors to reduce crash risk while preserving most of the premium. AQR’s research shows that combining momentum and value — which are negatively correlated — produces better risk-adjusted returns than either alone. Here is an example allocation framework:
Top quintile S&P 500 by 12-1 return. Highest return, highest crash risk.
Low P/B or P/E quintile. Negative correlation with momentum — provides crash hedge.
High ROE, low debt, strong cash flow. Most consistent across market regimes.
Defensive anchor. Outperforms in down markets, underperforms in strong bull markets.
This blended approach historically delivered ~80% of pure momentum returns with ~50% of the crash severity. Not a universal recommendation — illustrates the diversification principle.
Most investors who attempt momentum investing underperform a simple index fund. Not because momentum doesn’t work — the academic evidence is overwhelming. But because executing it correctly is psychologically harder than almost any other strategy. These seven pitfalls explain exactly how and why retail investors fail.
This is the most common mistake. An investor screens for momentum, buys a top-quintile stock, and then — when it drops out of the top quintile — discovers they have a strong opinion about the business and decide to hold. "I still believe in this company's long-term story." This sentence is the death of a momentum strategy. Your belief about the business is not the momentum signal. The signal says: this stock no longer has the price trend required to be in the portfolio. By holding on, you have changed strategies mid-position without consciously deciding to.
Write down — in a trading journal — the exact rule for selling each position before you buy it. "I will sell this position when it falls below the 30th percentile in 12-1 momentum rank." No exceptions. If you want to express a fundamental conviction about a stock, put it in a separate bucket of your portfolio explicitly labelled 'fundamental picks' and do not mix it with your momentum allocation. The two strategies are incompatible.
Reading that momentum can lose 30–80% in weeks is very different from experiencing it. In March 2009, investors who understood the theoretical crash risk still sold their momentum positions — because watching a portfolio lose 25% in 30 days while other investors appear to be making money on value stocks is psychologically excruciating. The emotional pain is compounded by the fact that momentum crashes typically happen during market recoveries — so every news headline is positive, every commentator is saying the worst is over, and your portfolio is still declining.
Before starting a momentum strategy, run a thought experiment: "What will I do if this portfolio loses 40% in 3 months during a market recovery?" Write the answer down. "I will continue the rebalance schedule, buying new top-quintile entrants and selling fallen positions." Only start momentum if you can honestly say yes to that scenario. Consider sizing your momentum allocation smaller than feels optimal — a position you can maintain through a crash is worth more than a larger one you will abandon.
The theoretical momentum premium of 3–5% per year sounds attractive. But monthly rebalancing of 20–25 positions — even with zero-commission brokers — incurs bid-ask spread costs on every trade. A typical spread of 0.05–0.2% on each buy and sell, applied across 20 positions monthly, compounds to 1–2% annually. In a taxable account, frequent short-term realised gains (taxed at income tax rates in the UK, or short-term capital gains rates in the US — up to 37%) can consume most of the remaining premium. Many retail investors are therefore actually running a negative expected value momentum strategy without realising it.
Run momentum exclusively inside a tax-advantaged account: ISA (UK) or Roth IRA (US). Use a zero-commission broker with tight bid-ask spreads on S&P 500 constituents. Consider quarterly rebalancing instead of monthly — you sacrifice roughly 10–15% of the gross premium but reduce transaction costs by 67%. A net 1.5% annual edge in a Roth IRA is still genuinely worth pursuing. A net 0.5% edge in a taxable account after costs is probably not.
Momentum portfolios have a well-documented sector concentration problem. In a technology bull market, the top quintile of the S&P 500 by 12-1 return will be 60–70% technology stocks. In an energy bull market, it will be 50–60% energy. Running a 5–8 position momentum portfolio in 2022 would have meant owning 5–6 energy stocks and 1–2 commodity stocks — essentially a sector ETF dressed up as a momentum strategy. When that sector reverses, the losses are catastrophic and the diversification assumption collapses.
Maintain a minimum of 20 positions and impose a hard sector cap of 30%. This prevents any single sector from dominating the portfolio even when sector momentum is extreme. At 20 positions with 30% sector caps, you might hold 5–6 tech stocks, 3–4 energy, 3–4 healthcare, and so on. This is still a concentrated portfolio — but diversified enough to survive a sector-specific reversal.
The timing of rebalancing execution matters more than most investors realise. The last day of the month is contaminated by institutional window dressing — fund managers buying their best performers to show on end-of-month statements, temporarily inflating those stocks' prices. The first day of the month sees these effects reverse. An investor who buys momentum stocks on the last day of the month systematically overpays, and the investor who sells on the first day of a new quarter systematically sells into weakness.
Execute rebalances on the 2nd to 5th trading day of the month. This avoids the end-of-month dressing effect and the first-day reversal. For quarterly rebalancers, avoid March/June/September/December end-of-quarter entirely — the window dressing effects are strongest at quarter-ends. The difference in execution quality compounds significantly over years of monthly rebalancing.
Academic momentum studies sometimes document the strongest gross returns in micro-cap and small-cap stocks. This is academically accurate and practically useless. When you try to buy a momentum micro-cap stock, your purchase order alone moves the price against you. When you try to sell, you move the price down further. This market impact — called slippage — means you buy higher and sell lower than the theoretical backtest price. For a stock with $1M average daily volume, a $20,000 position change can move the price by 1–2% alone. That 1–2% slippage per trade, applied to monthly rebalancing, eliminates the entire momentum premium in illiquid names.
Restrict your universe strictly to liquid, large-cap and mid-cap stocks. S&P 500 constituents have average daily volumes in the hundreds of millions — your individual trades create zero meaningful price impact. The gross premium is lower in large-caps than small-caps, but the net premium after slippage is significantly higher. Mid-cap (S&P 400) is the sweet spot: sufficient liquidity for most retail portfolio sizes, and somewhat higher momentum premium than the S&P 500.
Pure momentum has the highest raw excess returns of any equity factor — and the most violent crash risk. The 2009 −83% and 2020 −38% crashes were pure momentum events. An investor running 100% momentum who experienced the 2009 crash and sold near the bottom would have needed a 490% gain just to return to breakeven. That is a realistic scenario for investors who concentrate entirely in momentum without any diversification into factors with a different risk profile.
Treat momentum as one component of a multi-factor approach. A 30% momentum / 30% value / 25% quality / 15% low-volatility allocation historically delivered most of momentum's outperformance with a fraction of the crash risk. Value and momentum tend to move in opposite directions during trend reversals — value typically outperforms when momentum crashes. This natural hedge reduces the emotional pain of crashes and makes the overall strategy far easier to stick to.
Is Momentum Investing Right for You?
Momentum is not a beginner strategy. It is not even an intermediate strategy. It requires genuine emotional resilience, a specific portfolio infrastructure, and the discipline to follow rules mechanically through conditions that feel wrong. Most investors should master index investing first. Here is an honest assessment:
- ✓Can follow systematic rules mechanically — buying and selling on data signals, not emotional reactions or business convictions
- ✓Fully understand momentum crash risk and have sized your momentum allocation so a 40% drawdown doesn't impair your life
- ✓Have at least £50k or $60k to build a sufficiently diversified 20+ position portfolio where costs don't eat the premium
- ✓Can execute monthly (or quarterly) rebalancing consistently for at least 3–5 years — momentum is not a strategy you can dip in and out of
- ✓Have a tax-advantaged account (ISA / Roth IRA) available to shelter frequent capital gains from high turnover
- ✓See momentum as one component of a diversified multi-factor portfolio — not your entire approach
- ✓Are interested in the systematic, rules-based aspect of investing and enjoy the discipline of following a process regardless of current events
- ✗Find yourself wanting to override the system when your favourite stocks fall out of the top quintile
- ✗Are a beginner investor — the emotional and operational complexity of momentum is genuinely dangerous without a foundation in simpler strategies first
- ✗Want a low-maintenance portfolio. Momentum requires monthly attention, consistent execution, and immediate rebalancing on schedule
- ✗Have a small account (under £30k / $35k) where transaction costs and minimum position sizes make diversification impractical
- ✗Cannot emotionally handle watching your portfolio drop 30–40% in weeks while news headlines are positive and other strategies are recovering
- ✗Confuse momentum investing with 'buying quality businesses I believe in' — that is growth investing, which has a different logic entirely
- ✗Need your portfolio to generate regular income — momentum is a pure capital appreciation strategy with high turnover and no income focus
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