The Power of Social Influence on Investing, and the “Wisdom” of the Crowd
The legend of how Nathan Rothschild made a killing at the London Stock Exchange in the wake of Napoleon’s defeat at the Battle of Waterloo goes something like this: by deploying the most advanced tech available at the time (courier pigeon, the fastest horses, sailing across the English Channel ahead of others), Rothschild was able to “see around corners” relative to other Londoners waiting for news from France. He used that intel to then lean against his favorite post at the exchange and quietly sell, sell, sell, all with a sullen look on his face. Word spread rapidly and rampantly around the trading floor — Wellington had surely been routed and all was lost — and the other traders fell in lockstep, trying to get out while they still could. Toward the end of the day, Rothschild then bought back everything for pennies on the dollar (pence on the pound), making millions as news arrived that Wellington had indeed been victorious. It is not a true story, but people believe that it is, and perhaps that alone makes it an instructive and cautionary tale, given just how “FOMO” (fear of missing out) is as prominent as ever in our collective human thinking.
A more recent (and entirely real) account of trading in behavioral economics at its lowest form is the crypto-Ponzi scheme known as BitConnect. Initiated in early 2016, and using modern-day mirrors and smoke as substitutes for transparency, a white paper, legal docs, etc., the company obtained a market cap of $2.7 billion in under two years before being exposed as a total scam this past January. The BCC coin has crashed, the exchanged platform closed, and investors lost pretty much everything, save for a meme-inducing promotional video, which will exist until the end of time. As it probably should.
The Changing Landscape: AI Analyzes Everything, Including Humans Behaving Predictably Irrational
“No man is better than a machine, and no machine is better than a man with a machine.” — Paul Tudor Jones
In late 2017, I began writing for TrueRisk Labs (TRL), a company that builds Artificial Intelligence as a Service (AIaaS) for distribution across the financial services industry. The firm is part of an emerging group deploying proprietary AI to redefine the conventional approaches toward investment — conventional, because someday soon it will become exceedingly unconventional to not use artificial intelligence in analysis and decision making within the fintech sector, and this includes helping humans decipher times both past and present when the wisdom of the crowd hasn’t been all that wise. Armed with a working knowledge of history, a human decision maker can only benefit from access to machines constantly processing data. For example, most economists consider Bitcoin a bursting bubble, while some in crypto declare the coin will again prosper after some market correction. TRL’s Chief Quantitative Officer, Ritabrata Bhattacharyya, agreed with the former position when he suggested shorting Bitcoin for an article published in Hacker Noon on January 18th of this year. Ritabrata presented data explaining the correlation between Bitcoin’s lifetime price high and CME launching a Bitcoin futures contract on the same date in December, 2017, and contrasted the scale of Bitcoin with past asset bubbles. In the three weeks that followed the article’s publication, Bitcoin’s price dipped from over $12,000 to under $7,000.
Machines are already analyzing what economist Richard Thaler has preached for decades (an awarded a Nobel prize for last year): behavioral economics and its impact on financial markets. Alternative data sets, like social media feeds or point-of-sale transactions, are fed into an artificial neural network alongside quantitative and fundamental data sets, with a self-learning — and self-adapting — autonomous framework generating predictive analytics that aim to become prescriptive analytics about future human behavior. The ironic constant is that humans are reliably and predictably irrational when it comes to investment. As the Nobel committee stated in their award to Thaler, the “consequences of limited rationality, social preferences, and lack of self-control…systematically affect individual decisions as well as market outcomes.” And, obviously, this intel is also fed into machines, ceaselessly training them to outperform.
The application of artificial intelligence to both gauge and drive investor sentiment and derive analytics from crowd-sourced data is but one aspect of this new fintech world. For each headline detailing the mountains of cash a firm like Man Group invested in artificial intelligence once it decided to seriously commit to AI research, or that Goldman Sachs has more engineers than Facebook, there are also those startups that are actively shifting the paradigm in other fields: Robinhood has now added cryptocurrencies to its (no fee) stock trading app, and Equbot recently launched the first AI-powered ETF. The continued explosion of processing power, juxtaposed with the collapsing cost of harnessing that power, is allowing new, focused players on a field long-dominated by much older and much larger corporations, and is providing a company like TRL the vertical integration necessary to simultaneously build proprietary machine-learning frameworks, employ their use in active portfolio management, and distribute AIaaS. And while machine learning platforms operate without human sentiment or bias, they certainly measure it, and its repercussions. Social is the future, whether we like it or not, and may have already crushed SEO by the time you read this. (Anyone requiring proof would do well to recall the effect of Twitter bots on the 2016 US presidential election, or that Kylie Jenner recently used social media to express her displeasure with Snapchat, promptly erasing $1.3 billion of Snap’s market value with one afternoon tweet.)
AI, IQ, and EQ
“It it not the most intellectual of the species that survives; it is not the strongest that survives; but the species that survives is the one that is able best to adapt and adjust to the changing environment in which it finds itself” — Charles Darwin
The fear-of-missing-out characteristic is as old as humanity itself; what is changing daily is the scale at which data is generated, distributed, and requires analysis, and the necessity for humans to adapt in real time. The abilities and application of machine learning in the world of fintech have exploded in recent years, and that exponential integration will continue to climb toward ubiquity. The advantage shifts to those humans with the highest degree of EQ, as the challenge is continued adaptation toward emotional intelligence in our decision making. Artificial neural networks can do the work of innumerable humans, and all without sleep, or coffee, or displaying sentimental biases. In short, AI exhibits zero FOMO, but does require direction. Emotionally intelligent people will be there to take the helm.
TrueRisk Labs builds AI. Our machine learning platforms are constructed specifically for applications across the financial services industry, allowing both investors and fintech professionals access to the power of a true, native artificial intelligence.
