Silicon Valley’s artificial intelligence (AI) tech is seeping into global stock markets. The use of AI in managing has become one of the biggest trends in managing investment portfolios and picking winning stocks. While large companies have been using AI for years to mine massive amounts of data (including not only stock performance, but also social media trends, corporate commentary, credit card trends, consumer behavior, etc.), the advent and proliferation of AI-based tech has set global stock markets in a new era.

In the past, analyzing this data via quantitative analysis was time-consuming and beyond the abilities of mere mortals, utilized only for big investment companies like Goldman Sachs and J.P Morgan, which managed nearly 20% of its portfolios with AI. Now that AI is nearly ubiquitous and the barriers to entry have decreased, small-time brokers and startups are looking to leverage this tech into building a new model for investors to pick stocks.

Let’s take a look at exactly what artificial intelligence is, how it is used in trading and analyzing stocks, and some controversy surrounding AI’s mass-acceptance.

What is AI?

The simplest definition of artificial intelligence, dating back to the 1950s by Dartmouth professor Joseph McCarthy, is a process of using software to mimic aspects of learning and decision-making so that a machine can be made to simulate it. Since the inception of artificial intelligence, its applications have changed and methodology has scaled to accommodate burgeoning tech. Now that modern technology has “caught up” to the concept, AI is being used nearly everywhere:

• Google’s Waze app uses AI to predict traffic patterns to offer the quickest route.
• Online shopping at retailers like Amazon and Walmart use AI to make price changes and product recommendations to meet customer’s demands.
• Uber and Lyft use AI to determine fare pricing based on peak usage.
• Banks use AI as part of their fraud protection and prevent identity theft.
• Credit card companies use AI to determine whether a customer is eligible for an credit increase.
• Every flight in the world uses AI-powered autopilot to steer the vehicle (humans only account for ~7 minutes of control, reserved for take-offs and landings).
• Spam filters on your email sort out behavior patterns of junk mail and scammer tactics.
• Plagiarism checks in professional and academic settings can quickly analyze papers for stolen or redundant content
• Social media, like Facebook and Snapchat, uses AI in a number of functions to pair you with friends, recognize faces for tagging, and determine which posts on your newsfeed are available for you to view.
• There are also “celebrity AI” like IBM’s Watson, which bested Jeopardy record-setting contestant Ken Jennings.

The list goes on. So, if you’re wondering if AI is a new phenomenon, the answer is that it has been around for several decades (depending on your definition of AI). Aspects of AI have been refined in recent years, with machine learning and deep learning being popular buzzwords.

Machine learning means teaching a machine how to perform a particular action by programming algorithms set by a technician. This includes things like recognizing a particular virtual phone number from another country and then routing it to a particular call center. As more algorithms are added and data accumulated, the AI becomes more precise and capable of processing data to make better-informed decisions.

Deep learning, similar to machine learning, is a process of teaching a machine to perform actions and become more precise over time. However, deep learning goes further, involving an approach that involves artificial neural networks—similar to how human brains learn patterns of behavior (ex. someone sneezes and you automatically say “god bless you” without conscious thought). With researchers advancing the concept each year, deep learning becomes a type of sentient intelligence which learns as it goes. Typically, deep learning builds on machine learning by being able to adapt to new data by itself, changing algorithms to create more favorable output. Of course, this requires considerable amounts of computing power and it hasn’t been until recent years where humans have closed the gap to creating a better form of AI.

How is AI used in trading?

So, how does this apply to the stock markets? For a technology that’s used to crunch numbers rapidly and make optimal decisions, AI is a natural fit for the world of finance. Deep learning and machine learning allow financial firms to analyze not only stock price fluctuations, but also unstructured data that reveals patterns of behavior that may have not been perceptible by a human. This allows for a new form of accuracy in trading decisions that goes beyond traditional investing strategies. Similarly, the AI tech has spurned its own demand for “robo-advisors,” which can personalize an investor’s trading patterns and hit their financial objectives in a more cohesive manner.

Of course, these are just some of the known usages of artificial intelligences. Stock markets around the world have realized the application of AI and begun to shift their focus toward bringing in AI experts from Silicon Valley and into Wall Street (and beyond). This competitive furor has led to companies advancing this technology with real-world investing applications, but the extent of how investment firms, both big and small, is obviously veiled in secrecy.

However, not all industry experts are saying that artificial intelligence will become the next big thing. In fact, it already has achieved this distinction, which critics argue that mass acceptance of artificial intelligence renders it useless. This evening of the playing field casts doubt on whether there will be any variance to decision-making by investors, especially if they are all using the same stock picks as other financial firms. While there is a still a range between those who are using better artificial intelligence and to what extent it is being implemented by a company, all signs point to a homogenous investing environment. Still, it remains impressive that AI is better at picking stocks than most people.

Author: Tom Senkus

Tom Senkus is a freelance writer and author of 9 books (available on Amazon). His work has included articles in the financial sector, as well as.other contributions to other industries. In his spare time, Tom enjoys playing music, staying up to date on the latest tech, and reading the great works of Russian literature.