Artificial intelligence (
AI) is currently in big hype. It has already expanded its reach and transformed the efficiency of numerous industries. Investing is very demanding and requires skills, experience, and an understanding of stocks and the market, and that’s why many beginners need assistance to get started. Thanks to rapid advancements in AI, investing is no longer that complicated. It is now
faster, a bit easier, and much more accessible — even to complete newbies. In this article, we share our insights on how AI is changing the way we invest and what lies ahead.
The basics of AI
AI results from people's efforts to automate tasks that they perform, such as learning, reasoning, understanding natural language, solving problems, and making decisions. Unlike traditional computer programs that operate on specific instructions, AI systems learn on experience/data rather than explicit programming. That’s why this is also called machine learning.
The AI market is growing very fast due to technological innovations and large financial investments. The United States is currently the
largest investor in AI development. The AI US market is projected to reach US$50.16 billion in 2024. According to Statista, the AI global market size is expected to grow by
28.46% every year from 2024 to 2030, reaching US$826 billion by 2030.
Understanding AI in investing
In investing, AI handles tasks like market analysis, forecasting, stock picking, and personal portfolio management. AI algorithms examine large amounts of data and use insights to predict market trends and make decisions. This helps investors identify opportunities with more confidence. The AI we use today is the narrow AI model. These models are very successful in mining a large number of simple patterns from datasets in seconds.
The next AI, which is still in the domain of theoretical research, is called general (strong) AI. General AI aims to combine human-like intelligence with the ability to self-learn. In this future, AI could take over investment management by automating all complex and time-consuming tasks. That said, let us stay grounded. The evolution of artificial intelligence may intrigue us, but it is more likely to complement human roles rather than replace them entirely.
How AI is changing the investment sector
Until now, people have created investment strategies using their own intuition, which stems from years of experience and deep, ongoing market research. Now, however, we have modern investing, where people use data-driven AI insights to make more accurate decisions. While human intuition and reasoning remain the basis of investment decision-making, AI is here to assist us with that and save us time.
AI-driven stock picking
AI trading platforms are trained to rate and choose
stocks and exchange-traded funds (ETFs). These platforms filter stocks based on criteria like stock market value and number of trades, and then selected stocks are rated on a scale — usually 1 to 10 — where higher numbers indicate stronger investment potential.
Another highly valuable application of AI is natural language processing (NLP). NLP analyzes large amounts of financial data, economic indicators, social media posts, news, and company reports to gain real-time insights into public opinion and its effect on stock prices.
Advanced risk management with AI
Investing does come with risks. Markets rise and fall. The impacts are numerous and often unpredictable, and sometimes, these fluctuations are so frequent or drastic that they can cause headaches and anxiety for investors. So, the role of risk management is to strike a balance between potential gains and losses, easing that anxiety.
Before AI, investors monitored risk through financial analysis, historical data, and personal judgment. They would look at company reports and market trends and use economic indicators to investigate whether an investment was likely to succeed or fail.
Today, risk management has been greatly improved. AI tools can analyze investment portfolios and their risk levels in real time. AI simulates different scenarios to see how portfolios perform during an economic crisis or market swing. Based on these insights, AI recommends adjustments to help investors minimize unnecessary exposure to risk.
Algorithmic trading
Algorithmic trading uses a computer program that follows an algorithm to place trades. Such trading can generate profits at extremely high speed and frequency — the so-called high-frequency trading (HFT).
AI algorithms almost instantly analyze large amounts of market data, identify tiny deviations in price, and place trades at the right moment. This rapid data processing minimizes human error and maximizes gains, which allows investors to complete trades faster and much more accurately.
Portfolio management with AI
Another valuable application of AI in investing is its ability to rebalance portfolios automatically. It does this in response to market changes in order to maintain the investor's desired level of risk. This rebalancing is achieved by selling high-performing assets as well as moving assets into underperforming areas.
Modern AI technology has given us
robo-advisors. They are financial advisors who provide online advice and manage portfolios. The goal of a
robo-advisor is to balance portfolios by diversifying investments across different asset classes. For example, a robo-advisor might suggest a 90/10 allocation with 90% in stocks and 10% in bonds for investors looking for high growth or recommend a more balanced 60/40 split for investors who want less risk.
Stock market prediction using AI
Machine learning and deep learning are advanced AI methods that have proven to be very effective in discovering complex patterns and trends in stock market data, and based on this, they can predict future price movements of financial assets. The incredible power of AI lies in its ability to process and analyze large amounts of structured and unstructured data. It can process data over a large time span, measured in months and even years. Therefore, AI is used to create stock market prediction models.
While AI-based stock predictions can offer valuable insights for investors, we need to remember that stock market prediction is simply uncertain, so AI models should be used to support human decision-making, not as final predictors of future price shifts.
Sentiment analysis with AI
Sentiment analysis is the process of extracting insights and sentiments from textual data. AI strongly supports sentiment analysis precisely because of its ability to process massive data from news and trends on social media. AI classifies the processed content into positive, negative, or neutral, which gives investors a predictive view of market changes.
AI also connects local market trends with global events to create a complete view of market dynamics. For example, AI can link international events, such as changes in the US economy or geopolitical tensions between nations, to predict potential impacts on specific sectors. Using these predictions, investors can make better decisions about entering and exiting the market.
Ethical considerations of AI in investing
One of the biggest concerns when using AI technology in investing is the transparency of AI algorithms. AI algorithms operate through sophisticated processes that are hard for humans to understand, which could lead to concerns about process liability. If we want to have greater trust and fairness in trading, it is necessary to make trading algorithms transparent and understandable to all who wish to invest.
Another ethical issue is the potential for bias and discrimination within AI algorithms used for trading. These systems can inherit biases from the data on which they are trained and may favor certain groups over others. This risk increases when large institutions use advanced AI tools that provide a major advantage over smaller investors. Such a problem can be addressed through ongoing algorithm reviews and regulatory oversight.
What does the future hold for investing?
As we look to the future, we can see that AI's potential for investing is enormous. This is especially true since new technologies such as quantum computing, blockchain, the Internet of Things (IoT), decentralized finance (DeFi), and predictive analytics are well-positioned to revolutionize the financial industry.
Quantum computing has the potential to dramatically increase the speed and sophistication of AI algorithms, while combining AI with blockchain technology could increase the transparency and security of transactions.
If we assume that humans manage to develop the next generation of smart AI, we will witness a remarkable evolution in which people can invest much faster and more efficiently. For now, these ideas remain in the realm of theoretical research, but we’re already seeing human expertise enhanced by AI technology, which results in stronger investment outcomes. The future of investing has already begun.
