Artificial intelligence (AI) technology from Silicon Valley is making its way into global stock markets. The use of artificial intelligence (AI) in portfolio management and stock selection has become one of the most popular trends. While large corporations have been using AI to mine massive amounts of data for years (including not only stock performance, but also social media trends, corporate commentary, credit card trends, consumer behaviour, and so on), the advent and widespread use of AI-based technology has ushered in a new era for global stock markets.
Previously, assessing this data via quantitative analysis was time-consuming and beyond the capabilities of common humans, and was reserved for large financial firms such as Goldman Sachs and J.P Morgan, which used AI to manage over 20% of their portfolios. Small-time brokers and startups are attempting to harness AI to establish a new model for investors to buy stocks now that AI is practically ubiquitous and the hurdles to entry have lowered.
Let’s look at what artificial intelligence is, how it’s utilised in stock trading and analysis, and some of the debates surrounding AI’s widespread acceptance.
What is artificial intelligence (AI)?
The most basic definition of artificial intelligence, coined by Dartmouth professor Joseph McCarthy in the 1950s, is the process of utilising software to model components of learning and decision-making so that a machine can duplicate it. Artificial intelligence’s applications have evolved and methodology has scaled to meet advancing technology since its debut. AI is being applied almost everywhere now that current technology has “caught up” to the concept:
- Waze, a Google software, uses artificial intelligence to forecast traffic patterns and provide the shortest route.
- To match client needs, online merchants such as Amazon and Walmart employ AI to make price changes and product recommendations.
- Uber and Lyft employ artificial intelligence to calculate fare pricing based on peak usage.
- AI is used by banks to safeguard against fraud and identity theft.
- Credit card issuers employ artificial intelligence to assess if a consumer is eligible for a credit boost.
- Every flight in the globe employs AI-powered autopilot to guide the vehicle (human control is limited to 7 minutes for take-offs and landings).
- Spam filters on your email sort junk mail and fraudster tactics based on their behaviour patterns.
- Plagiarism checks can swiftly scan documents for stolen or repetitive content in professional and academic settings.
- AI is used by social media platforms like Facebook and Snapchat to link you with friends, detect faces for tagging, and identify which posts in your newsfeed are available for you to view.
- There’s also “celebrity AI,” such as IBM’s Watson, which beat Ken Jennings, the record-setting Jeopardy contestant.
The list could go on and on. So, if you’re wondering if artificial intelligence is a new phenomenon, the answer is that it has existed for decades (depending on your definition of AI). Machine learning and deep learning have been prominent buzzwords in recent years as aspects of AI have been developed.
By programming algorithms specified by a technician, a technician can educate a machine how to accomplish a specific activity. This can involve things like detecting and routing a virtual phone number from another country to a specific call centre. The AI grows more precise and capable of digesting data to make better-informed decisions as additional algorithms are added and data is amassed.
Deep learning, like machine learning, is a method of teaching a machine to execute tasks and improving its precision over time. Deep learning, on the other hand, goes a step further, employing artificial neural networks in a manner similar to how human brains learn patterns of behaviour (for example, when someone sneezes, you reflexively say “God bless you” without thinking about it). Deep learning is evolving into a form of sentient intelligence that learns as it goes, with academics improving the notion each year. Deep learning, in most cases, builds on machine learning by being able to adapt to new data on its own, modifying algorithms to get better results. Of course, this necessitates a significant amount of computer power, and humans have just recently narrowed the gap in terms of developing a better kind of AI.
What role does artificial intelligence play in trading?
So, what does this mean in terms of stock markets? AI is a natural fit for the financial realm, as it is utilised to crunch numbers quickly and make optimal decisions. Financial institutions may use deep learning and machine learning to evaluate not only stock price swings, but also unstructured data that exposes patterns of behaviour that a human might not see. This enables a new level of trading accuracy that goes beyond typical investment tactics. Similarly, AI has turned down its own demand for “robo-advisors,” which may tailor an investor’s trading behaviours and help them achieve their financial goals more consistently.
Of course, these are just a few examples of artificial intelligence’s applications. Stock markets all across the world have recognised the value of AI and have began to focus on attracting AI professionals in from Silicon Valley and Wall Street (and beyond). Companies are advancing this technology with real-world investing applications as a result of the competitive frenzy, but the extent to which investment firms, large and small, use it is obviously shrouded in secrecy.
Artificial intelligence, on the other hand, is not universally seen as the next big thing. Indeed, it has already reached this distinction, which detractors believe renders artificial intelligence useless due to widespread acceptance. This levelling of the playing field raises doubts about whether investors will make different decisions, especially if they are all utilising the same stock selection as other financial institutions. While there is still a gap between those who are employing better artificial intelligence and the extent to which it is being used by businesses, all indicators lead to a homogeneous investing environment. Still, the fact that AI can pick stocks better than most people is noteworthy.