Survey Note: A Deep Dive into the Benner Cycle’s Inaccuracies and the Mental Gymnastics of Its Believers
Introduction to the Benner Cycle and Its Claims
The Benner Cycle, developed by Samuel Benner, an Ohio farmer, in the 1870s, is a historical model aimed at predicting market cycles based on patterns observed in agricultural commodity prices, particularly pig iron. First published in his 1875 book, “Benner’s Prophecies of Future Ups and Downs in Prices,” it categorizes years into three phases: Panic Years (marked by irrational market swings), Good Times (high prices, ideal for selling), and Hard Times (low prices, good for buying and holding). Benner suggested cycles of panics occurring roughly every 18, 20, or 16 years, with other phases following specific intervals, extending predictions to 2059.
Historical Accuracy: Hits and Misses
Proponents argue the Benner Cycle has predicted major economic events. For example, it forecasted a panic around 1927, close to the 1929 stock market crash that triggered the Great Depression, and marked “good times” in 2007, just before the 2008 financial crisis. It also predicted a panic in 1999, aligning with the Y2K scare and the dot-com bubble’s peak, which burst in 2000–2002. These instances suggest some historical alignment, but the timing is often approximate, not exact.
However, there are notable misses. The cycle predicted hard times in 1965, yet the US economy was robust, with GDP growth and low inflation, as evidenced by economic reports from that year (GDP Per Capita 1965). Another significant failure was in 2019, when it forecasted a panic, but markets remained strong until the 2020 COVID-19 crash, a delay of a year. Additionally, it predicted hard times in 1999, but the late 1990s saw strong growth due to the dot-com boom, contradicting its forecast.
Criticisms and Limitations: Why It Falls Short
The Benner Cycle faces several criticisms that question its reliability:
- Lack of Scientific Basis: The cycle is rooted in 19th-century agricultural observations, not applicable to today’s globalized, technology-driven markets. It lacks empirical support, especially given its inclusion of astrological influences, such as linking market cycles to planetary movements, which have no scientific backing.
- Overfitting and Cherry-Picking: Created to fit historical data up to 1872, the cycle may have selectively chosen data points to support its theory, ignoring contradictory evidence. This overfitting is evident in its inability to predict future trends accurately, as noted in discussions on Reddit (150 year old benner cycle).
- Oversimplification: The financial world is complex, influenced by factors like geopolitical events, technological innovations, and central bank policies (e.g., Federal Reserve interventions). The Benner Cycle does not account for these, offering a simplistic view that fails to capture modern market dynamics, as highlighted in critiques from financial blogs (The Benner Cycle: Sure Thing or an Illusion?).
- No Logical Explanation: There is no clear rationale for why market cycles should repeat every 27 years or be tied to pig iron prices. This lack of underlying theory weakens its credibility, as noted in academic discussions (Benner Cycles & the 9/56 year grid).
- Failed Predictions: Specific examples include:
- 1965: Predicted hard times, but the US economy was strong, with GDP growth and low inflation (US economy in 1965).
- 2019: Predicted a panic, but the market remained strong until the 2020 COVID-19 crash, a clear timing miss (Benner Cycle: Predicting the Future).
- 1999: Predicted hard times, but the late 1990s saw robust growth due to the dot-com boom, contradicting its forecast.
These failures are documented in various analyses, such as McMinn’s 2022 paper, which notes false predictions in 1965 and 1999, and a recession in early 2020 instead of 2019 as expected (Benner Cycles & the 9/56 year grid).
Modern Relevance: A Static Indicator in a Dynamic Market
Today’s markets are faster and more interconnected than ever, driven by globalization, financial innovation (e.g., derivatives, ETFs), and real-time information flow. The Benner Cycle’s static intervals cannot adapt to these changes. For instance, central banks like the Federal Reserve use tools such as interest rates and quantitative easing to stabilize economies, often overriding historical patterns. Unpredictable events, like the COVID-19 pandemic, further highlight the cycle’s inability to account for modern shocks, as seen in its 2019 prediction miss.
Mental Gymnastics: Why People Still Believe
Despite these limitations, some investors continue to believe in the Benner Cycle, engaging in mental gymnastics to justify its use. This can be attributed to cognitive biases:
- Confirmation Bias: Investors focus on instances where the cycle seemed correct, like the 2008 crash prediction, while ignoring misses like 2019. For example, they might highlight its alignment with the Great Depression but downplay 1965’s failure.
- Post Hoc Fallacy: After an event, they adjust interpretations to fit, such as claiming the 2020 crash was “close enough” to the 2019 prediction, rationalizing the discrepancy.
- Gambler’s Fallacy: Believing past patterns will repeat, they assume the cycle’s historical rhythm will continue, despite market evolution.
- Overconfidence Bias: Investors may overestimate their ability to predict using the cycle, leading to decisions based on flawed assumptions, as seen in discussions on investment forums (Investing with the Benner Cycle).
These biases are evident in social media, where users share charts aligning the cycle with recent events, ignoring its broader inaccuracies.
Conclusion: A Relic, Not a Tool
In conclusion, while the Benner Cycle offers a historical perspective, its accuracy is limited, and its static nature cannot keep pace with today’s dynamic markets. Its failures, such as missing the 2020 crash and predicting hard times in strong years like 1965, underscore its unreliability. Investors should rely on modern, evidence-based strategies, such as fundamental and technical analysis, rather than an outdated model. The mental gymnastics of belief highlight human tendencies to seek patterns, but in finance, adaptability and data-driven decisions are key.
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