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I recently came across a fascinating theory that has been working for over 150 years—and honestly, more people should be interested in it. It’s about the Benner Cycle, a market forecasting method developed by an American farmer named Samuel Benner.
So, the story is actually quite interesting. Benner was a farmer in Ohio and went bankrupt in 1873 due to a market panic. Instead of giving up, he started researching why that happened. As a farmer, he knew that harvest cycles influence prices—and that led him to a bigger discovery. He realized that market movements occur in recurring cycles. In 1875, he published his book "Trends and Phases of Business," explaining how to utilize these cycles.
The Benner Cycle is based on two main observations: an 11-year cycle in corn and hog prices (which correlates with the solar cycle), and a 27-year cycle in iron prices. That might sound abstract, but Benner was able to predict major economic events with it—the Great Depression of 1929, the Dotcom Bubble of 2000, and even the COVID crisis of 2020.
The theory divides market phases into three categories. First are the panic years—times of extreme volatility when investors act irrationally, prices fluctuate wildly, and big opportunities arise if you make the right decisions. Then come the good times, when prices are high and it’s ideal to sell. And finally, the tough times—the buying phase—to profit later during the boom years.
What fascinates me about the Benner Cycle is its consistency over more than a century. Despite all technological changes, cryptocurrencies, and decentralized markets—the underlying psychology remains the same. People panic, people become greedy, and these cycles repeat.
According to this analysis, we are currently in a phase where asset prices are falling—which theoretically means it’s a good time to accumulate. Whether that applies to Bitcoin, other cryptocurrencies, or traditional assets, the Benner Cycle offers an interesting framework for thinking about long-term market movements beyond short-term fluctuations.