Ever since the launch of ChatGPT, the business world has been captivated by artificial intelligence (AI), so it shouldn't be a surprise that investors are looking for new ways to use AI in investing.
The applications for generative AI and other forms of the emerging technology are opening up new ways for its use in investing. Now, investors are not only looking for companies that could make a fortune from AI but also for ways to use AI to become better investors and improve their returns.
Below, we break down what AI actually is, how it’s being used in investing right now, and what it could mean for the future of building a portfolio.
Understanding AI in Investing
AI has been part of investing for years, especially in quantitative analysis, statistical modeling, and systems designed to mimic human decision-making. What’s changed is how accessible and practical these tools have become.
Today, AI helps investors in a few key ways. It’s used to deliver personalized financial guidance by analyzing data like credit scores, spending patterns, and stated goals. Tools from companies like Credit Karma tailor insights without the need for a human advisor. AI also powers automated and high-frequency trading, where algorithms analyze markets and execute trades in real time.
Together, these uses show how AI is moving beyond theory and into everyday investing decisions.
Innovative ways AI can shape your investing strategies
1. Algorithmic trading
Algorithmic trading might be the most direct way in which AI is used in investing.
Traders use AI algorithms to analyze large datasets and trade at high speeds, making trades based on market trends and patterns.
Computers have the ability to analyze data much faster than humans can, giving them an advantage in high-frequency trading. Algorithms also aren't subject to human biases, which range from loss aversion to anchoring to framing, none of which affect AI algorithms.
Algorithmic trading often focuses on taking advantage of price discrepancies such as the bid-ask spread, and the gains are often small enough that it only works in high volume.
Algorithmic trading does work, but no trading strategy works 100% of the time since market conditions and traders adjust to new information quickly.
2. Sentiment analysis
Another way AI is used in investing is for sentiment analysis. Markets move according to a variety of factors, such as macroeconomic data, earnings reports, geopolitical issues, interest rates, and market sentiment.
Sentiment is difficult to quantify, but investor feelings often dictate the direction of the stock market more than any other data point.
Artificial intelligence programs can help traders assess market sentiment by collecting news articles, social media posts, and other online activity to measure market sentiment and predict movements.
Sentiment can also have a significant impact at the sector level, driving booms in industries like electric vehicles, cannabis, cryptocurrency -- and now, artificial intelligence stocks.
3. Portfolio optimization
Portfolio management is another bedrock concept in investing. Money managers try to maintain a balance around diversification, risk, and factors like income and growth. AI can help fund managers optimize their portfolios to balance between these goals and prioritize any one of them.
There could also be a future where generative AI technologies like ChatGPT are used in portfolio management; one research team found that ChatGPT can be effective as a co-pilot when putting together a portfolio. Such a tool can be especially useful for retail investors who may not be as experienced at managing their own investments.
AI investing bots can also advise money managers on what's missing in their portfolios, informing them on how to better balance them.
4. Risk management
AI can also play a role in risk management, helping money managers and companies by analyzing historical market data, volatility, and any correlations that could affect returns.
Machine learning techniques are also used in risk management to help improve efficiency and reduce costs.
In some ways, the technology can replace human labor since it's able to analyze large datasets at fast speeds with relatively little need for human intervention.
These models have demonstrated better forecasting accuracy than traditional regression models and are able to capture nonlinear relationships between risk factors and other variables.
5. Personalized investment advice
Thanks to the innovation behind ChatGPT and generative AI, AI programs are starting to be able to offer personalized investment advice.
One such app is called Magnifi, which uses ChatGPT and other AI tools to provide real-time investment advice.
Magnifi acts like something of an AI-powered Robinhood (HOOD -1.65%), functioning as a trading platform that can answer questions with a chatbot interface like ChatGPT.
Although Magnifi only recently launched, we'll likely see more such AI investing platforms as investors are eager to take advantage of the new technology.
Mainstream trading platforms like Robinhood and others could also start to incorporate some of these AI trading tools soon.
6. Generate backtesting insights
Backtesting is another common investing practice that fits well with AI. Through backtesting, investors can game out how a stock might do under certain circumstances.
For example, AI can help run the test and generate insights based on, say, which stocks have done well over the past five years or which do well when interest rates are up.
Gaining the appropriate insights from backtesting is arguably more valuable than running the tests themselves, and AI can help with that. It may observe trends that would go unnoticed to most human observers, or deliver novel explanations for why one set of stocks did better than another.
7. Pull synthetic data
One of the more creative applications of AI in investing is pulling synthetic data.
"Synthetic data" refers to datasets created by artificial intelligence for the purpose of providing more data for analysis. This tactic can be particularly useful when trying to model "black swan" events and other such crises. Since these are relatively rare, synthetic data can give investors an understanding of expectations when a similar crisis rolls around.
Synthetic data is a relatively new concept, but it's a good example of how AI can create entirely new ways of researching market activity and getting an edge in investing.
Benefits and risks of using AI in investing
Like most investing strategies, there are both benefits and risks to using AI in investing. Let's review some of the more significant ones.
Benefits:
- AI can be a timesaver in doing research.
- AI can help with functions like modeling and backtesting.
- AI can avoid the human emotion and bias that sometimes complicates decision-making in investing.
- AI can analyze vast amounts of data in a way that a human or traditional software could not.
Risks:
- AI can't predict the stock market, and investors should be wary of overrelying on it.
- AI chatbots are generally overconfident and can get facts wrong.
- AI may miss aspects of the stock market that a human readily understands.
- There isn't an artificial intelligence tool that can be fully relied on to pick stocks or beat the market.
It's best to think of an AI as a complement to investors rather than a do-it-all tool. Currently, AI is useful in investing, but it also has its shortcomings.
The future of AI in investing
How AI impacts investing in the future is difficult to predict, but there are a number of potential use cases for the technology going forward. Here are some of the possibilities.
- Large language models (LLMs) for investing will get more specialized, perhaps focusing on specific sectors or stocks.
- Explainable AI is likely to gain greater adoption. Explainable AI, or XAI, refers to making AI decisions more understandable. In a field like finance, millions or even billions of dollars could be riding on these decisions, so explainability is key.
- Hyper-personalization is also expected to become more of a factor in AI as technology allows for better analysis of individual customer data and is able to create personalized experiences.
- Improved textual analysis. Computers have long been used for quantitative analysis, but LLMs have made textual analysis, or interpreting statements and comments such as earnings calls, possible. Analysis of speech for clues and patterns is likely to improve, giving investors further insights.
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