How Artificial Intelligence Can Be Used in Your Investing in 2025

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.

In this look at AI and investing, we'll review the definition of artificial intelligence, discuss several ways that AI is being applied in investing, and explore how artificial intelligence could affect the future of investing.

Overview

Understanding AI in Investing

AI is playing an increasing role in investing, but tech-savvy investors have long sought to use the technology to their advantage, including in quantitative analysis, statistical analysis, and through neural networks meant to mimic human decision-making.

These days, AI is being embraced to help investors in a number of significant ways. First, AI is being used to provide personalized financial advice. While it may not offer the same personal touch as a human, AI tools, such as those used by Credit Karma, can help personalize financial advice based on demographics, investing style, and goals.

Information like credit scores is a good starting point for AI since it tells you a lot about the users' spending history, and most borrowers want to improve their credit scores.

Elsewhere, AI is used for robotrading and high-frequency trading, helping to develop algorithms that can execute trades.

Keep reading to see seven ways that AI is used in investing.

How AI can shape your strategy

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 -1.19%), 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.

With backtesting, AI can help run the test and generate insights based on, say, what stocks have done well over the past five years or what does 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.

Related investing topics

Is artificial intelligence the future of investing?

There's no single way to invest, and investing can't be neatly categorized like other industries.

However, investors are likely to embrace any new technology that can improve performance and alleviate some of the labor of investing, and artificial intelligence fits both criteria.

Although there will always be a human component in investing, picking stocks, and managing a portfolio, artificial intelligence is likely to play a greater role in investing as the technology develops.

If you're interested in getting exposure to artificial intelligence in your own portfolio, consider looking at AI stocks or an AI ETF to gain broad exposure to this emerging technology.

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