It’s Wednesday Again

In December 2014, a Tumblr post with the slogan “It’s Wednesday again…” quickly went viral among social media users.

Soon, it was observed that these mid-week posts sparked increased productivity and a boost in innovative thinking.

Even executives and engineers at Boeing were reportedly influenced by this so-called “Wednesday enthusiasm,” leading to a remarkable uptick in groundbreaking aircraft designs and strategic initiatives.

This momentum extended to investors as well, propelling Boeing stock to new heights. Who would’ve thought that such a simple slogan could elevate a company to success—and transform Wednesday from just a day of the week into a financial success marker?

Accompanied by a graph, it might seem like a convincing story. But it isn’t true.

Just because two variables exhibit a linear relationship (i.e., both increase or decrease in tandem), it doesn’t necessarily mean one causes the other.

For a factor to be considered a cause, there must be a traceable interaction between it and the outcome. If no such connection can be established, the relationship is likely coincidental—what we call a spurious correlation.

When solving a problem, the algorithm—a step-by-step method to reach a solution—can often be described as the path to the answer itself.

Algorithms are everywhere: from facial recognition systems ("Mirror, mirror, on the wall...") to social and digital media’s content recommendations. Algorithms work by breaking down a subject, system, or idea into its smallest components and modeling each both as an individual unit and as part of a larger whole.

With the rise of technological infrastructure, algorithms have become integral to financial markets. Algorithmic trading—also known as automated trading—allows investors to execute pre-defined buy/sell strategies automatically, without manual intervention. This also eliminates the need to continuously monitor market movements.

It allows strategies that have been back-tested to be deployed at high speeds, placing orders quickly, while enabling the setting of stop-loss/take-profit levels and reducing emotional biases in trading decisions.

One subclass of algorithmic trading is High-Frequency Trading (HFT), which uses pre-programmed investment strategies to perform trades at speeds measured in microseconds (one-millionth of a second) or even nanoseconds (one-billionth). Operating through advanced technological infrastructures, HFTs are used across stock markets, currency exchanges, and derivatives platforms.

HFTs generate profit through:

• exploiting price discrepancies between assets traded on multiple exchanges (cross-market arbitrage), or
• leveraging latency arbitrage from time delays in how new information is incorporated into prices.

They also profit by providing liquidity—continuously placing simultaneous buy and sell quotes, capturing gains from the bid-ask spread, or earning cost advantages through market-making roles.

We live in an era of data bombardment, and it’s become increasingly vital to analyze data critically and assess causal claims. This is an essential—and growing—skill.

Every new data point should prompt us to revisit and refine our understanding of a given event, phenomenon, or process. When this new data interacts with existing knowledge, it may increase—or decrease—the likelihood that our current model or theory is, in fact, real.