AI stock trading is not a new concept. Algorithmic or automated trading has been around for years and plays a vital part in the movement of markets and the global economy.
However, since the emergence of OpenAI’s ChatGPT and its more advanced successor, ChatGPT-4, the financial sector has taken notice and started to leverage a wider range of AI-powered trading tools, bots, and software.
In this article, we will take a closer look at AI stock trading, its legal and ethical implications, and ways that AI models are being used by investors, traders, and brokers.
What Is AI Stock Trading?
Before diving into how AI stock trading and investment strategies are implemented and whether you want to become reliant on AI tools and software, let’s examine what AI stock trading is.
The term “artificial intelligence” (AI) is a broad one. In simple terms, AI is a way to define a branch of machine learning and data science. Machine learning has been around for decades since Professor Alan Turing made the first significant breakthroughs in building an “electronic brain” during World War II.
Decades later, millions of researchers worldwide are dedicated to every field of machine learning, such as large language models, foundation models, natural language processing, deep learning, and AI. It’s a multibillion-dollar sector. AI is deeply embedded into the products most people use every day, including those from Facebook, Google, Netflix, Amazon, and Apple.
Some of the most exciting recent innovations in this field are “generative” AI tools, meaning people give them prompts, and the AI model generates a response. These are all built using large language models or foundation models. Vast amounts of data are needed to train these models. This makes them ideally suited for financial services and investing, a sector that runs on billions of numeric and quantitative data points.
In the case of ChatGPT-4, it was trained on 45 terabytes of data thanks to its 175 billion parameters. Other examples of similar models and tools include Google’s BERT, OpenAI’s DALLE-2, and Meta’s Segment Anything.
Before all of this hype around ChatGPT, AI tools — also known as algorithmic, algo, or black-box trading — were already playing a massive role in the financial sector and investment strategies. Many retail investors, day traders, forex traders, cryptocurrency traders, and hedge funds use these solutions. As an SEC staff report said in 2020, algorithmic trading is “widespread and integral to the operation of our capital markets.”
Algorithmic trading is already a $15.77 billion market and is expected to grow to $23.74 billion by 2028, a compound annual growth rate of 8.53%. AI-implemented investing, in one form or another, already accounts for 63% to 70% of equity trades in the U.S. stock market.
Although human investors and brokers still pick, refine, and adjust AI models and tools for AI stock trading, many of the trades are executed by algorithmic automation tools. There are numerous advantages to this approach, although there are many detractors of AI stock trading, too.
How Does AI Stock Trading Work?
Let’s say you’ve got a portfolio of 100 low-risk stocks, securities, or ETFs. You don’t have time to monitor all of them, but you want to maximize returns and minimize the risk of losses.
Instead of trying to monitor these securities manually, you could set up an AI trading platform to automatically oversee this part of your stock market portfolio in real-time and then execute trading strategies according to your criteria. AI stock trading tools are designed to combine historical data with real-time market data, analyze price movements, and help investors outperform the market and make more profitable trades.
For example, you could optimize the AI trading software to buy and sell according to moving averages and daily fluctuation criteria. And if a security drops too far below preset moving average criteria, then it could be sold to minimize losses and a new investment holding bought to take its place.
The goal of using AI technology is to execute trades much faster than humans could, and/or to find inefficiencies automatically faster than a human could. AI software trading bots also prevent emotion from impacting trades. Algorithms can more effectively assess whether today’s volatility will subside, whereas an investor might panic and sell just before the stock price recovers.
Artificial intelligence stock market software simplifies and effectively outsources decision-making, using algorithmic models and automated trading functionality.
Is AI Stock Trading Legal?
Al trading has been around for decades. It’s now significantly more sophisticated, and even newbie investors can sign up for automated trading platforms and (hopefully) watch their capital grow.
AI trading is legal, for the most part, except when algorithmic trades are used to manipulate markets. Flash crashes are examples of this, such as the May 6, 2010, flash crash that erased $1 trillion in equity value in under an hour. There was also the Sterling flash crash on October, 7 2016, when the British pound fell 9% against the U.S. Dollar in only a few hours.
Another example is what happened to Knight Capital Group in 2012, when a source code bug caused a $440 million loss. In this case, there was no kill switch to stop the algorithm from making automatic trades.
As Spencer Greenberg, formerly the CEO and CTO of Rebellion Research, an AI hedge fund, says an AI “input can be carefully manipulated so that the predictions about it are highly inaccurate.” In other words, if someone wants to manipulate stock markets or the share price of individual organizations, AI trading and adversarial strategies are some of the best ways to go about this.
Is AI Stock Trading Ethical?
Investors trying to manipulate financial markets is nothing new. Stock markets have been manipulated ever since they first came into existence.
However, there is a significant movement toward ensuring that emerging AI models are ethical and aren’t able to autonomously manipulate financial markets. Governments, regulatory bodies, and even AI industry leaders are calling for new laws to regulate AI development, use cases, applications and an enhanced focus on AI ethics.
Ethical and legal academics are concerned that AI-based financial systems will train themselves to manipulate markets because they’ve learned from human actors. Because AI models can only learn ethics from people, it’s already widely documented that they can and will generate responses that reflect the worst of humanity.
In 2021, three academics at the University of Oxford and Hamburg published a paper to explore the following scenario: “Autonomous AI trading agents that, thanks to self-learning capabilities, can discover both old and new forms of market abuse, including emerging risks of ‘tacit’ collusion, in a fully autonomous way (i.e., without being expressively programmed or instructed in that way by human experts).”
The paper concluded that markets can be manipulated autonomously by AIs, and this poses a risk for the whole financial sector. Reforms and policy changes are being encouraged to be taken seriously by regulators to avoid future flash crashes and other AI-based actions that could put whole markets, companies, and sectors at risk.
Despite various and considerable upsides to AI trading, there are downsides and risks that investors, brokers, and analysts need to consider. Whether you’re developing an in-house AI system or using or modifying an off-the-shelf solution, make sure you’ve got access to experts, such as developers with big data experience, for any complex fine-tuning tasks.
How Is AI Being Used by Investors?
AI trading is already a major player in the buying and selling of equities, commodities, and stocks every day. The most widely used forms of AI trading include algorithmic trading, quantitative (also known as quant) trading, and high-frequency automated trading.
AI tools are also used as part of risk management and investment or stock trading strategy backtesting and simulations.
Risk and Fraud Management
Risk management is an essential part of any investment strategy. Whether it’s your own money or you’re managing millions or billions worth of investor funds, you’ve got a legal and professional duty to mitigate risk at every turn.
Fortunately, this is one area that the financial sector and fintech products have been investing in for decades. There is no shortage of risk and fraud management tools, and many of those were already AI-powered long before ChatGPT arrived.
Everything from know-your-customer compliance to automated trading platforms has risk management built in as a standard feature. If you’re buying any new AI tools, make sure they’re compliant with regulator guidelines, including data protection and security laws.
AI for Investment Strategy Backtesting
Stock investment strategy backtesting is a great way to apply technical indicators to assess whether a plan you’ve got is going to work.
Now, you can use AI tools to test quantifiable theories using historical data, real-time share price movements, and any number of mathematical models, e.g., simple moving averages (SMAs), Bollinger bands, mean reversion, volatility indicators, and numerous others.
Once you’ve developed a quantifiable theory and tested it, you can use AI-powered software to simulate whether it’s likely to work. QuantConnect and Trade Ideas are two of the many options that investors and analysts can use to assess the viability of a backtesting strategy before making live trades.
AI-Powered Algorithmic Trading
Naturally, the most famous and widely used application of AI in investing is algorithmic trading.
Investors use AI models and software to execute anything from instant or near-instant high-volume day trading and arbitrage-based strategies, such as forex, to more long-term strategies.
AI trading can leverage technical indicators to manage massive portfolios almost automatically. Like with the use of AI and software in any sector, there’s simply too much data for humans to sit, monitor, and crunch the numbers.
AI systems give human traders and analysts the freedom to think more creatively, add value for clients, and be more innovative with investment strategies instead of spending all day going over charts and hoping to spot micro-second opportunities. AI tools can do all of that; hence, they are already responsible (under human supervision) for 63% to 70% of U.S. stock market activity.
AI tools have the advantage of being purely analytical, pre-programmed, customizable by investors, and unencumbered by emotion. An AI is never going to buy or sell too soon because it saw a Tweet or heard or rumor. AIs don’t have the fear of missing out and will buy or sell based on your investment criteria, regardless of what’s being published in The Wall Street Journal.
Accelerate Your Investment Strategy With AI
It’s worth remembering that although the rest of the world is experiencing a sudden “AI revolution,” investors and the financial sector have been relying on algorithms for trading, backtesting, and risk management for decades. AI software is already responsible for making 63% to 70% of U.S. stock market trades.
There are numerous advantages to relying on AI tools to execute stock trading strategies, such as speed, algorithmic and emotionless investing, the ability to process vast amounts of data, and rapid, iterative learning cycles. However, investors need to be wary of the disadvantages too, such as legal and ethical concerns, the risk of flash crashes, and even the danger that an AI model could manipulate markets autonomously.
Investors need to factor in the value of using clean financial data to train AI-based stock trading strategy software tools. Tiingo’s stock trading APIs are a powerful source of multi-market, national and international, historical, and real-time data.
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