The Black-Scholes model provides a nice case study of performativity in financial markets. Performativity, as a notion, refers to how economic models and theories can shape the markets they aim to describe, influencing behavior and, in turn, the dynamics of the market itself. This post will discuss the rise and fall of the Black-Scholes model, highlighting how external factors such as technological advancements and market conditions not only facilitated its adoption but also contributed to its eventual decline. The model itself shaped the reality it described, but it did this only in conditions created by the external factors. This is an insight on performativity itself: Performativity requires right conditions to happen. It is not an opaque phenomenon where reality is “socially constructed” in some murky sense, but an understandable phenomenon the dynamics of which can be analyzed.

In what follows, I rely heavily on the paper by Donal MacKenzie and Yuval Millo 2003: “Constructing a Market, Performing Theory: The Historical Sociology of a Financial Derivatives Exchange”. American Journal of Sociology Volume 109, Number 1.

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The Black-Scholes model, a mathematical formula, was developed by Fischer Black and Myron Scholes in 1973, with Robert Merton expanding on their work. Designed for pricing European-style options, it transformed the landscape of financial markets and earned Scholes and Merton the Nobel Prize in 1997. However, the rise of the Black-Scholes model was not simply due to its theoretical ingenuity; a combination of cultural and technological advancements and conducive market conditions played a critical role in its widespread adoption.

First, let’s note an interesting cultural factor. The Black-Scholes model involves complex mathematical computations, which would have been time-consuming and error-prone if performed manually. While electronic calculators programmed with Black-Scholes solutions were available, they were rarely used on the Chicago Board Options Exchange (CBOE) due to the time-consuming process of inputting parameters. Instead, traders preferred carrying printed sheets of theoretical prices. However, initially, this practice faced resistance in the macho culture of the trading floor where reliance on these sheets was frowned upon. Over time, cultural and economic shifts gradually lessened these barriers.

Surely, the increasing prevalence of computer systems further advanced the model’s adoption. Traders could program the Black-Scholes formula into their computer systems, automating calculations and allowing for the application of the model on a larger scale. In this way, computer systems played an important role in the model’s rise.

Market conditions were also instrumental in the model’s adoption. CBOE standardized options trading, creating a suitable environment for the model to be tested and implemented. The move towards standardization by the CBOE not only facilitated the model’s practical use but also lent it credibility, encouraging its acceptance within the trading community.

Furthermore, the availability of necessary data, enabled by technological advancements, allowed traders to input required variables like stock prices and volatility data into the model. The enhanced data collection, storage, and analysis capabilities made the model more accurate and reliable, thereby increasing its appeal to traders.

In summary, the rise of the Black-Scholes model can be attributed to a confluence of factors, including its innovative design, the advent of new technology, and favorable market conditions. The circumstances led to the widespread adoption of this groundbreaking model, setting the stage for a new era in options pricing. However, as we shall see next, the model’s reign was not destined to last forever.

Despite the successful rise and widespread adoption of the Black-Scholes model during the 1970s, the 1980s marked a dramatic shift in its fortune. A combination of changing market conditions, the emergence of alternative models, and a traumatic financial event led to a significant decline in the model’s dominance and credibility.

One of the most critical factors leading to the model’s fall was the change in market conditions. The Black-Scholes model is underpinned by several assumptions, one of which is the constancy of volatility—a key input in the model. However, during the 1980s, volatility began to fluctuate in ways that the model was not designed to handle. This shift in market dynamics led to less accurate option pricing, undermining the model’s effectiveness and reliability.

Another factor that contributed to the model’s decline was the introduction of alternative option pricing models. These new models, which emerged during the 1980s, offered different methods for pricing options. The availability of these alternatives reduced the dominance of the Black-Scholes model and further eroded its status as the de facto standard for options pricing.

However, the most significant event leading to the fall of the Black-Scholes model was the stock market crash of 1987, often referred to as “Black Monday.” On October 19, 1987, the Dow Jones Industrial Average plummeted by over 20% in a single day, an extreme event that the Black-Scholes model failed to predict. Traders who heavily relied on the model for options pricing suffered substantial losses. This event shook confidence in the model, sparking widespread skepticism and marking a turning point in its acceptance.

The fall of the Black-Scholes model in the 1980s serves as a potent reminder of the dynamic and unpredictable nature of financial markets. Models that work well under certain conditions can quickly become ineffective when those conditions change. To sum up the story of the Black-Scholes model, we see a nice example of performativity in the financial markets. Its rise was fueled by an interplay of technological innovation, culture, and favorable market conditions, while its fall was caused by changing market dynamics and traumatic events. This narrative underscores the changing status of financial models and their deep interconnection with the markets they aim to describe.