Here's a puzzle that has divided finance academics and practitioners for decades: if markets efficiently incorporate all available information into prices, why do some traders consistently profit from chart patterns and technical indicators?

The standard academic answer is that they don't—any apparent success is luck, survivorship bias, or compensation for hidden risks. Yet billions of dollars flow through quantitative trading strategies that rely heavily on price-based signals. Hedge funds employ armies of technical analysts. Major banks maintain trading desks dedicated to momentum and pattern recognition.

Something doesn't add up. Either markets aren't as efficient as theory suggests, or technical analysis works through mechanisms that don't violate market efficiency at all. The reality, as we'll explore, is more nuanced than either camp typically admits.

Eugene Fama's efficient market hypothesis remains one of finance's most influential ideas. In its strongest form, it claims prices reflect all available information, making consistent outperformance impossible. But here's what many critics miss: Fama himself never claimed markets were perfectly efficient, only that they were efficient enough to make beating them extremely difficult.

This distinction matters enormously. Markets can be highly efficient at processing fundamental information over months and years while still exhibiting predictable patterns over hours and days. The key is understanding what efficiency actually requires—and what it costs.

Market efficiency depends on arbitrageurs. When prices deviate from fair value, these traders swoop in, buy undervalued assets, sell overvalued ones, and push prices back toward equilibrium. But arbitrage isn't free. It requires capital, carries risk, and faces constraints.

When arbitrage costs exceed potential profits, inefficiencies persist. This is why short-term price patterns can exist even in markets that efficiently price assets over longer horizons. A momentum pattern that lasts three days might offer profits too small to attract institutional capital after accounting for transaction costs, market impact, and operational overhead. Yet for nimble traders with low costs, these same patterns represent genuine opportunities.

Takeaway

Markets can be efficient at processing information over long timeframes while still containing exploitable patterns in the short term—efficiency is a spectrum, not a binary state.

Consider the 200-day moving average, perhaps the most watched technical indicator in equity markets. When a major index crosses below this threshold, selling often accelerates. When it bounces off it, buyers emerge. Is this evidence of mystical predictive power? No. It's evidence of coordinated human behavior.

Technical patterns work partly because traders believe they work. When enough market participants watch the same support level, they place buy orders at similar prices. This concentration of demand creates the very floor they anticipated. The pattern becomes prophecy.

This self-fulfilling dynamic doesn't violate market efficiency—it is market efficiency in action. Prices reflect not just fundamental information but also information about what other traders will do. A widely-followed technical signal contains real information: the likely behavior of market participants who follow it.

The implication is profound. Technical analysis can generate profits not by predicting fundamental value, but by predicting other traders' reactions to price movements. This is a different game entirely from fundamental analysis, and it plays by different rules. Success requires understanding which patterns command attention, how strongly traders respond to them, and when that coordination might break down.

Takeaway

Technical patterns often work through coordination—they predict trader behavior rather than fundamental value, and this prediction itself contains tradeable information.

Not all markets and timeframes reward technical analysis equally. Understanding where chart patterns provide genuine edge requires examining market microstructure—the mechanics of how orders interact and prices form.

Technical analysis tends to work best where three conditions converge: high liquidity, active retail participation, and momentum-driven institutional flows. Liquid markets allow positions to be established and exited efficiently. Retail traders create behavioral patterns that more systematic players can exploit. Institutional momentum strategies amplify trends once they begin.

Currency markets and liquid equity indices often exhibit these characteristics. So do popular individual stocks during periods of heightened attention. Conversely, technical signals tend to fail in illiquid markets where order flow is sparse and unpredictable, or in markets dominated by fundamentals-focused investors with long holding periods.

Timeframe matters too. Intraday patterns persist because transaction costs make them unprofitable for large institutions to arbitrage. Multi-week momentum persists because behavioral biases—anchoring, herding, overreaction—take time to work through investor psychology. The worst timeframe for technicals is often the middle ground: too slow for microstructure effects, too fast for behavioral patterns to fully develop.

Takeaway

Technical analysis adds value in specific conditions—high liquidity, active retail participation, and timeframes where arbitrage costs or behavioral persistence create exploitable patterns.

The debate between efficient market believers and technical analysts rests on a false dichotomy. Markets can be remarkably efficient at incorporating fundamental information while still offering tactical opportunities based on price patterns and trader behavior.

Technical analysis works not by violating market efficiency, but by operating in the gaps efficiency leaves behind—in short timeframes where arbitrage costs exceed profits, and through coordination effects where predicting trader behavior matters as much as predicting value.

The practical lesson: approach technical analysis not as chart mysticism, but as applied behavioral finance. Understand why patterns exist, where they're likely to persist, and when they'll fail. That's the difference between trading on superstition and trading on market structure.