Does it matter? - Strong form efficient market hypothesis

The Myth of Market Efficiency: A Critical Examination

Oct 31, 2024

When Warren Buffett famously declared, “Be fearful when others are greedy, and greedy when others are fearful,” he wasn’t just offering investment advice—he was challenging the very foundation of Efficient Market Hypothesis (EMH), particularly its strongest form. As markets continue to display increasingly erratic behavior, driven by algorithmic trading, social media influence, and unprecedented retail investor participation, the question becomes more pressing: Does Strong Form EMH still hold any relevance in modern markets?

The Psychology Behind Market Movements

Human emotions remain the primary drivers of market behaviour despite technological advances and sophisticated trading algorithms. The 2008 financial crisis serves as a perfect case study. While EMH suggests that all information, including insider knowledge, is immediately reflected in stock prices, the reality proved far different. Months before the crash, warning signs were visible—declining housing prices, rising default rates, and deteriorating mortgage quality. Yet, the market continued its upward march, fueled by collective optimism and willful blindness.

This psychological aspect of market behaviour directly contradicts Strong Form EMH’s central thesis. If markets were truly efficient, such dramatic mispricings would be impossible. The fact that they occur regularly suggests that human psychology, rather than pure rationality, often determines market prices.

Technical Analysis: The Language of Market Psychology

In October 1987, the Relative Strength Index (RSI) reached an unprecedented 85.87 on the S&P 500, signalling extreme overbought conditions. Two weeks later, “Black Monday” occurred, with markets plunging 22.6% in a single day. A similar pattern emerged before the 2000 dot-com crash, when the NASDAQ’s RSI hit 84.5 in March, preceding an 80% decline over the next two years.

During the 2008 financial crisis, the Moving Average Convergence Divergence (MACD) indicator generated a bearish cross on the S&P 500 in December 2007, three months before Bear Stearns’ collapse. Investors following this signal could have avoided a 56% drawdown. The VIX index, known as the “fear gauge,” spiked to 80.86 in November 2008, marking peak fear—precisely when Warren Buffett published his famous “Buy American” op-ed in The New York Times.

Statistical analysis from 2010-2020 showed that when the S&P 500’s 50-day moving average crossed below its 200-day moving average (the “death cross”), the market declined an average of 12.5% in the following six months. Conversely, when the 50-day moving average crossed above the 200-day moving average (the “golden cross”), the market gained an average of 15.3% over the next year.

The 2020 COVID-19 crash provided striking validation of Fibonacci retracement levels. After falling 35%, the S&P 500 bounced exactly at the 61.8% Fibonacci retracement level of its 2009-2020 bull run, around 2,192 points. This level acted as strong support, leading to a powerful recovery rally.

Volume analysis has proven equally telling. In March 2000, the NASDAQ peaked with declining volume—a classic warning sign. Daily trading volume had dropped 28% from its January highs, even as prices continued rising. This divergence preceded the index’s collapse. Similarly, in 2007, NYSE volume began declining while prices made new highs, with average daily volume dropping 15% between June and October.

Market breadth indicators have demonstrated remarkable predictive power. The NYSE Advance-Decline line peaked in June 2007, four months before the S&P 500’s all-time high, warning of deteriorating market internals. By September 2007, only 45% of stocks traded above their 200-day moving average despite major indices reaching new highs—a clear sign of market weakness.

The 2021 meme stock phenomenon highlighted how technical patterns still influence modern markets. GameStop’s short interest exceeding 140% of float triggered a technical squeeze, driving the stock from $17 to $483. When its relative volume indicator showed trading activity at 100 times normal levels, the peak was near.

Recent research spanning 1990-2020 revealed that stocks experiencing “golden cross” formations outperformed the broader market by an average of 4.2% annually. The study, covering 5,000 stocks, showed this effect was particularly strong in high-volatility periods, suggesting technical analysis becomes more relevant during market stress.

Timing and Strategic Decision-Making

The ability to identify market extremes through psychological and technical indicators offers significant advantages to strategic investors. During the March 2020 COVID-19 market crash, panic selling created extraordinary opportunities. While EMH would suggest that prices perfectly reflected all available information about the pandemic’s impact, history shows that markets overreacted, driven by fear rather than rational analysis.

Successful investors who bought during this period weren’t just lucky—they recognized the pattern of market overreaction that occurs during periods of extreme stress. This pattern, repeated throughout market history, demonstrates how emotional extremes create predictable opportunities that shouldn’t exist under Strong Form EMH.

The Role of Information Processing

Modern markets generate vast amounts of data, yet Strong Form EMH assumes that all market participants can process this information instantly and rationally. Reality proves otherwise. Information overload, cognitive biases, and processing limitations mean markets often take time to fully incorporate new information.

The NYSE processes over 350 billion data points during each trading session. On May 6, 2010, the infamous “Flash Crash” saw the Dow Jones Industrial Average plunge 9% in minutes, temporarily erasing $1 trillion in market value. This occurred when high-frequency trading algorithms responded to a large sell order of E-Mini S&P 500 futures contracts worth $4.1 billion.

In 2012, Knight Capital Group lost $440 million in 45 minutes due to a trading algorithm malfunction, executing millions of unintended trades. The incident highlighted how automated systems can misinterpret and mishandle market information. Similarly, on August 24, 2015, more than 1,200 trading halts were triggered in ETFs and stocks, causing price discrepancies of up to 35% between ETFs and their underlying assets.

Research by Thomson Reuters reveals that over 400,000 news articles and social media posts related to financial markets are generated daily. The average trader must process approximately 6,000 economic releases annually from the U.S. government alone. Studies by MIT’s Laboratory for Financial Engineering found that human traders take an average of 320 milliseconds to react to price changes, while algorithms can respond in microseconds.

In 2019, JPMorgan estimated that only 10% of U.S. stock trading represents fundamental discretionary trading, while 60% comes from passive and quantitative investing. According to TABB Group data, high-frequency trading firms now execute 50-60% of all U.S. equity trading volume. During peak market volatility, message traffic can exceed 1 million messages per second across U.S. exchanges.

The Securities and Exchange Commission reported that between 2016 and 2019, there were 4,700 cases of “disruptive” algorithmic trading events that led to market distortions. In 2021, the average holding period for NYSE-listed stocks was 5.5 months, compared to 8.3 years in 1960, indicating how processing speed has transformed market behaviour.

Practical Applications for Modern Investors

Understanding the limitations of Strong Form EMH offers practical advantages for investment strategy. Rather than accepting market prices as always correct, investors can:

1. Monitor sentiment indicators to identify extreme optimism or pessimism
2. Use technical analysis to spot potential market turning points
3. Maintain cash reserves for opportunities during market panics
4. Take profits when euphoria reaches extreme levels

These strategies directly contradict EMH but have proven effective across market cycles.

Conclusion: A New Understanding

While Strong Form EMH provided valuable theoretical insights about market behaviour, its practical relevance has diminished. Modern markets, shaped by mass psychology, technological innovation, and increased retail participation, demonstrate regular violations of EMH’s core assumptions.

This doesn’t mean markets are entirely inefficient—they remain highly competitive and challenging to beat consistently. However, understanding the interplay between mass psychology, technical analysis, and market behaviour offers opportunities for strategic investors willing to think independently and act contrary to the crowd.

The future of investment success lies not in blindly accepting or rejecting EMH but in recognizing when markets are likely to deviate from efficiency due to psychological factors. In this way, Strong Form EMH serves better as a theoretical benchmark than a practical guide for modern investors.