You will be able to explain momentum, how it has been measured and the risks of crashes it carries.
Marcus's US chip designer rose about 60% in a year, and his group chat was full of people buying it because it had risen. He'd always thought of that as the worst kind of reasoning. Then he read that buying recent winners is the one chart-based idea with decades of academic research behind it. Both views turned out to be partly right, and the difference between them is in the details: how long, how many stocks, and at what cost.
Figures in this lesson are made up unless a study is named, and nothing here is a recommendation to trade.
In 1993 Narasimhan Jegadeesh and Sheridan Titman published "Returns to Buying Winners and Selling Losers" in the Journal of Finance. They ranked US stocks by their returns over the past three to twelve months, formed portfolios of the best and worst performers, and held them for the following three to twelve months. Past winners tended to keep beating past losers over those holding periods. Lesson 10.1, What technical analysis claims and what the evidence supports, introduced the result.
Two details matter. First, the effect works over months, not days or years. Over very short periods, around a month, earlier research by Jegadeesh found the opposite tendency, a short-term reversal, which is why many momentum measures skip the most recent month. Over several years, past winners have tended to lag. Second, the effect was measured on portfolios of many stocks, not on single shares. A single stock's past rise says very little about its own next year. A broad group of past winners, held together, has tended to beat a broad group of past losers.
Momentum has since been found in many stock markets, in sectors, in countries and in other asset classes. In 1997 Mark Carhart added it as a factor when explaining fund performance, and it has been part of standard fund analysis ever since. Researchers still argue about why it exists. Explanations include investors reacting slowly to news and then overreacting, and herding. The reason matters, because an effect with no reason behind it may not last.
Relative strength compares the return of a stock or sector with the return of the market over the same period. Divide one plus the stock's return by one plus the market's return. A result above 1 means it beat the market.
Here's Marcus's made-up ranking of six sectors in an index that rose 8% over twelve months. Technology rose 24%, a relative strength of about 1.15. Financials rose 15%, about 1.06. Industrials rose 10%, about 1.02. Utilities rose 3%, about 0.95. Real estate fell 4%, about 0.89. Energy fell 9%, about 0.84. A sector momentum rule would hold the leaders and avoid the laggards, then rerank every month or quarter.
Relative strength is a description of the past twelve months. The research says groups at the top have tended to stay ahead for a while, on average, across many periods. It doesn't say technology will lead next year.
The returns from momentum don't arrive smoothly. They come with occasional sharp losses, and those losses cluster in a particular moment: when markets rebound hard after a fall.
The logic is plain once you see it. After a long decline, the past losers are the stocks that fell hardest, often the most indebted and most cyclical. When the market turns, those stocks rise fastest. A momentum strategy is betting against exactly those stocks just as they jump. In 2009, after the financial crisis, beaten-down stocks rallied violently from March, and momentum strategies that had been short them suffered some of their worst losses on record. Kent Daniel and Tobias Moskowitz studied these crashes in a paper called "Momentum Crashes" and found the same pattern in other market rebounds.
So momentum is a strategy that wins slowly and loses quickly. Its returns look like the steady-income strategies in lesson 2.3, Sharpe and Sortino ratios, and when each misleads, in reverse: long stretches of gains, interrupted by short, deep losses.
The academic results are mostly measured before trading costs, or with costs typical of large institutions. A momentum strategy trades a lot, because the list of winners changes every month. For a small account, that's where most of the return goes.
Take a made-up S$20,000 account holding ten stocks, reranked monthly. Say three stocks drop out and three come in each month, which means six trades of about S$2,000 each, or 72 trades a year. With a made-up minimum commission of S$10 per trade and a cost of 0.2% for half the spread on each, every trade costs about S$14. Seventy-two of them cost about S$1,008 a year, about 5% of the account. That's before currency conversion on any US stocks, and before slippage on thinly traded names.
Taxes are a separate risk. Frequent buying and selling over short holding periods is one of the patterns IRAS looks at when deciding whether gains are trading income, as lesson 12.4, Tax on gains and dividends: check how IRAS treats you, explains. An investor running a high-turnover strategy should check that guidance first.
Momentum, then, has real evidence behind it, a known crash risk and costs that fall hardest on small accounts. Most retail investors meet it in a lower-cost form: through broad index funds, which hold more of the stocks that have risen simply because their market values grew, or through funds built around the momentum factor, whose costs you'd compare using Unit trusts, robo-advisors and managed money compared, lesson 2.2, Yearly charges: management fee, expense ratio and trailer fee.
For the activity, take one index you hold, find the twelve-month returns of its sectors from the index provider's factsheet, and get ready to rank them.
Rank the sectors in one index by twelve-month relative strength and note which led and lagged.
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