The Architecture of Illusion - Part 1:The Early Days: When Science First Met Unpredictable Prices

The Paradox of the Permanently High Plateau

In the early decades of the twentieth century, the idea that science and reason might be applied to the stock exchange was considered radical. Wall Street was largely run by established captains and cronies who controlled speculative pools, leaving the general public to grope for success. Yale University economics professor Irving Fisher sought to change this, aiming to impose reason and scientific order on the marketplace. Fisher was a pioneering mathematical economist who believed financial value could be determined rationally by discounting the expected income an asset would produce. However, the very man who championed this scientific behavior—and helped lay the intellectual groundwork for modern quantitative finance—also made himself a historical buffoon. Fisher lost his entire fortune in the 1929 crash after confidently asserting that stock prices had reached a “permanently high plateau”.

This paradox highlights the enduring tension at the heart of finance: the pursuit of objective, scientific order in a realm defined by unpredictable human behavior.

The Flawed Ideal of Market Foresight

Fisher’s ultimate conviction—that America’s masses of investors were following his rational advice and thus “nothing, therefore, could go wrong”—defined his intellectual error. The question of whether markets inherently possess wisdom, or whether they are prone to mass delusion, became the central arguable claim in finance. The utility of any rational model relies on the assumption that economic actors possess sufficient foresight to adjust to changing realities. Yet, history demonstrates that market participants frequently succumb to systematic errors due to “emotion, lack of logic and insufficiency of knowledge,” causing “violent social disturbances”. Understanding the foundational attempts to model this chaotic environment is crucial, as the initial scientific tools were flawed but undeniably useful.

Mapping Uncertainty: Mechanism and Context

The Randomness of Short Swings

The earliest sophisticated attempt to model financial markets mathematically came from French mathematics student Louis Bachelier in 1900. Studying the Paris Bourse, Bachelier developed mathematical tools to describe the movement of prices, concluding that they were akin to a game of chance, like roulette or dice. His key insight was that the movements of stock prices were random and unpredictable based on past activity. Bachelier modeled these movements using the Gaussian (bell curve) distribution, assuming that uncertainty itself could be quantified if causes were many and results were random. This concept later underpinned the “random walk hypothesis” and the description of prices as exhibiting “Brownian motion,” a phenomenon Einstein would later apply to physics. However, Bachelier wisely limited the applicability of his formulas only to an “instant” into the future.

Sheep and Systematic Error

Despite the rigor of mathematical models, early analysts quickly recognized confounding factors. Bachelier’s professor, Henri Poincaré (Social Dynamics lens), warned that applying the bell curve to human behavior required caution, noting that when men are brought together, they do not decide independently but “react upon one another”. This herding behavior, Poincaré observed, makes them susceptible to following the destructive “habits they have of Panurge’s sheep”. Fisher himself, learning from experience (Policy and Critique lens), backpedaled from the pure randomness hypothesis, noting that while independent errors might cancel out, the mistakes of the common herd were usually in the same direction—like sheep following a single leader. This meant the price movements were not truly random; the errors were “systematic”. This persistent systematic error resulted in large market swings—the very “violent social disturbances” (business cycles) that mathematically-minded thinkers desperately wanted to cure or predict.

Foresight and Futility

The early 20th century saw two main attempts to conquer this volatility: fundamental economic data analysis and chart reading (Technological History lens). Roger Babson attempted to predict cycles by smoothing economic data onto a “Babsonchart,” believing that for every period above the long-term trend line, the economy must fall below it for an equal period. William Peter Hamilton, editor of the Wall Street Journal, countered that the market predicted the economy, believing patterns could be found in stock movements themselves, leading to Dow Theory. However, the foundational work of statistician Frederick Macaulay—who used coin flips to show that purely chance curves looked “eerily like a stock chart”—mocked the pretensions of all forecasters. Alfred Cowles III confirmed this statistical futility in 1932: his exhaustive analysis found that professional stock market forecasters were little better than chance, a finding noted publicly with the headline, “Rates Luck Above Wall St. Experts”. Holbrook Working argued later that this apparent failure of forecasting actually reflected credit on the market, as market perfection implies unpredictability.

A Flaw that Fuels the Pursuit

The early days of rational finance were marked by spectacular failures, yet they established the enduring challenge: if markets are prone to systematic human error, why do price movements remain so resistant to consistent prediction? The consensus emerging from the data was that markets possessed a kind of statistical truth that defied individual foresight. Fisher had sought to make investors less “ovine” by applying probability. Despite his personal ruin, his core vision—converting rules of thumb into science—did not fade. As institutionalists retreated following the Great Depression, the mathematical framework Fisher and Bachelier introduced survived, setting the stage for a new generation of academics to build a truly unified, if ruthlessly abstract, science of markets. This early conflict between mathematical idealism and human reality crystallized the dilemma: achieving a rational market seemed to require ignoring the messy, sheep-like nature of the people who composed it.