It’s human nature to strive to decipher ambiguity and complexity. For our early ancestors, recognizing patterns and making sense of the world around them was crucial for survival, and we retain those instincts today. The financial markets are a prime example. We want to understand what’s going on, and, crucially, predict what’s going to happen. But anyone who’s ever watched CNBC during periods of market volatility will know, even the so-called experts struggle to do it.
In fact, over the centuries, some of the brightest minds have tried and failed. Even Isaac Newton managed to lose around a third of his wealth in the South Sea Bubble in 1720. “I can calculate the motion of heavenly bodies,” he is reported to have said, “but not the madness of people.”
It wasn’t until 1900 that Louis Bachelier, a much lesser-known mathematician than Newton, published a thesis that helped to shape our understanding of how markets work. Even then, his contribution wasn’t even recognised until the 1950s.
Short-term market movements are random
It was while studying at the Sorbonne in Paris that Bachelier’s interest in the markets developed. Bachelier spent time closely observing proceedings at the Bourse in the Palais Brongniart and was struck by how prices seemed to move in a random manner. This observation led him to question whether there could be a mathematical framework to describe these erratic fluctuations.
The key concept Bachelier grasped was that markets are competitive, with a buyer and seller for every trade. When more people want to buy, the price goes up; when more people want to sell, it goes down. The problem is, there are all sorts of factors that affect investor sentiment — the economy, for example, wars, elections, technological changes and even the weather.
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