This article provides an overview of how AI technology might be used as part of investment analysis and strategy planning. It considers the potential advantages and disadvantages of using AI, and ways to manage the risks associated with its implementation.
Artificial intelligence has become the talk of the financial world, with promises of revolutionary trading insights and potential automated wealth generation flooding social media and investment forums.
Yet beneath the hype lies a complex reality: AI tools can indeed enhance investment analysis, but they also introduce unique risks and limitations that every investor must understand.
What Is AI Analysis in the Context of Trading?
AI analysis refers to using artificial intelligence technologies to process financial data, identify patterns, and generate insights that can inform investment decisions.
To shed light on how to get the most out of AI when investing, it helps to break it down into its distinct technological features.
| Machine Learning (ML) Models | Large Language Models (LLMs) | AI Agents |
|---|---|---|
| Machine learning technology excels at pattern recognition. This includes analysing historical price movements and volume data to identify recurring patterns that might signal future price movements. These models can process thousands of data points simultaneously, spotting correlations that human analysts might miss. | LLMs specialise in sentiment and news analysis. They can read and interpret vast quantities of text data from news articles, earnings reports, and social media posts and gauge market sentiment in real time. Unlike traditional keyword-based systems, LLMs understand context and nuance, providing more accurate sentiment readings. | AI agents actively gather and process information without constant human supervision. These digital assistants can monitor multiple data sources and alert investors to significant market developments. Think of them as tireless research analysts that automate your research, continuously scanning the investment landscape for relevant information. |
How AI Might Augment Investment Strategies
AI’s abilities include being able to process vast amounts of data quickly, identify patterns, and generate analysis free from emotional biases. All of those features mean it has the potential to support aspects of the investment process ranging from asset selection to risk management.
Tip: The processing power of AI makes it particularly useful when dealing with complex markets or a large portfolio.
These are some of the ways AI might potentially enhance an investor’s ability to make well-informed decisions.
Enhancing Market and Investor Sentiment Analysis
Modern AI systems can report on market sentiment by analysing millions of news articles, social media posts, and financial reports in real time and take a view on whether overall sentiment towards a particular sector is bullish or bearish.
Tip: Investment firms increasingly use sentiment analysis to complement traditional fundamental analysis by tracking mood shifts across various sources.
Automating Technical Analysis & Pattern Recognition
AI Machine Learning Models excel at identifying chart patterns across thousands of assets and timeframes, a task that would be impossible for human analysts. The systems can also adapt to changing market conditions, learning to identify new patterns as they emerge.
Tip: When using AI for pattern recognition, always verify the research manually and consider the broader market context before acting.
Beyond traditional patterns, AI can discover and report novel correlations between seemingly unrelated assets or indicators. For example, an AI system might identify that certain commodity price movements consistently precede shifts in specific currency pairs, providing traders with potentially useful leading indicators.
Backtesting Insights
AI transforms backtesting from a simple historical replay into a sophisticated analysis tool. Traditional backtesting might test a strategy against historical data, but AI-powered systems can introduce multiple variables, market conditions, and stress scenarios to provide more nuanced results.
Tip: AI backtesting extends to highlighting variable factors which might have caused a seemingly successful strategy to fail.
AI-Powered News & Data Aggregation
Information overload represents a significant challenge for modern investors. AI-powered aggregation tools can monitor hundreds of news sources, financial statements, and regulatory filings, delivering customised summaries of the most relevant information.
Tip: AI aggregators can monitor data sources ranging from central bank announcements to supply chain reports and geopolitical news updates.
Streamlining Research With AI Agents
AI agents act as intelligent research assistants, automating time-consuming tasks like gathering financial statements, comparing analyst reports, or compiling economic data.
These agents can be programmed to monitor specific metrics across a portfolio, alerting investors when predetermined thresholds are reached or when unusual patterns emerge.
Tip: Automated AI Agents free up investors’ time to focus on analysis and decision-making rather than data gathering.
Portfolio Balancing and Management
AI systems can continuously monitor portfolio composition, providing real-time insights into risk exposure. They can identify when portfolios drift from target allocations and even suggest rebalancing strategies that minimise transaction costs while maintaining desired risk-return profiles.
Tip: Machine learning algorithms can identify unidentified correlations within portfolios that might not be apparent through traditional analysis.
Using AI: A Case Study
A case study which highlights the potential of AI is one where a system might analyse ten years of earnings season reports to identify management teams that consistently deliver on guidance.
This analysis would be exceedingly time-consuming for humans, but straightforward for AI. However, the decision to invest based on this information should involve human judgement about current valuations, market conditions, and portfolio fit. It is also important to remember that past performance is not an indication of future results.
The Risks of Using AI for Trading and Investing
While AI offers powerful capabilities for investment analysis, improper use or overreliance on these tools could lead to significant financial losses, making it crucial to understand their limitations and risks.
Unlike traditional analysis tools, AI systems can fail in opaque ways, produce convincing but incorrect results, and amplify errors at unprecedented speeds. Understanding these risks isn’t about avoiding AI entirely — it’s about using these tools responsibly and maintaining appropriate safeguards.
The “Black Box” Problem
Many AI systems, particularly deep learning models, operate as “black boxes” where the reasoning behind decisions remains opaque.
An AI might signal a strong “buy” recommendation for a particular stock, but without understanding an AI’s reasoning, investors cannot assess whether the system is relying on spurious correlations, outdated patterns, or flawed assumptions.
Data Overfitting & Historical Bias
AI models trained in historical data can fall victim to overfitting — essentially memorising past patterns rather than learning genuine market dynamics. A model might perform brilliantly when tested against historical data, but fail when faced with current market conditions.
Tip: Another significant challenge is historical bias. Markets evolve continuously and AI might apply outdated assumptions to current markets.
Paradigm Shift
Markets occasionally experience paradigm shifts where established relationships break down entirely. The rise of passive investing, or the emergence of cryptocurrency markets all represent fundamental changes that can render historical analysis obsolete.
AI systems, no matter how sophisticated, cannot predict or immediately adapt to such revolutionary changes.
Tip: Investors relying heavily on AI during paradigm shifts may find themselves positioned exactly wrong for new market realities.
AI Hallucinations & Data Integrity
AI hallucinations — instances where AI systems generate false or misleading information — pose particular dangers in financial contexts. For example, an AI might confidently report non-existent earnings figures or invent analysts’ recommendations.
These errors can be subtle, convincing, and hard to identify, especially when embedded within otherwise accurate analysis.
Tip: Always verify AI findings relating to critical financial data against primary sources before trading.
Security & Data Privacy
Inputting sensitive portfolio information into third-party AI tools could create significant security risks. This information could be valuable to competitors, hackers, or even used for front-running trades.
Tip: Always scrutinise privacy policies and security measures before entrusting AI systems with sensitive financial information.
The Speed of AI-Driven Errors
AI systems can execute analysis and generate recommendations at superhuman speeds — but this velocity becomes dangerous when errors occur. A flawed AI model has the potential to generate hundreds of incorrect trading signals before anyone notices the problem.
Tip: “Flash crash” events seen in various markets demonstrate how algorithmic trading can spiral out of control.
Linguistic developments
Claims that Large Language Models (LLMs) are able to effectively interpret nuance could be challenged when a model bases its decisions on a term having a particular meaning for multiple years, but for the popular meaning to suddenly change.
Integrating AI Into Your Investing Workflows
Successful AI integration requires thoughtful planning and clear boundaries. The goal is that your strategy benefits from AI’s computational power while maintaining human oversight. The following are steps you might take to incorporate AI into your trading plans:
| Step | How to approach it |
|---|---|
| Keep Humans in the Loop | AI can highlight potential opportunities, but consider them in the context of the broader market and your personal investment objectives. |
| Evaluate AI Tools | Systems trained on limited datasets or which aren’t regularly updated could lose their relevance. |
| Establish Trust Thresholds | Establish to what extent your AI tools can explain their recommendations in understandable terms. |
| The Importance of Backtesting | Comprehensive backtesting goes beyond simple historical replay — it should include stress testing, scenario analysis, out-of-sample validation as well as transaction costs and slippage. |
| Use AI as a Guidance and Analysis Tool | The most sustainable approach to incorporating AI into research and strategy planning draws on using the technology to process vast datasets and present findings for human evaluation. |
AI can not only test your theories, but also provide insights you might otherwise not be aware of.
Final thoughts
One important factor to bear in mind is that the pace of AI development far exceeds previous financial technology evolutions, and there are few signs of the rate of adoption slowing down.
The one constant within this tumultuous period is that human oversight remains of paramount importance, making it crucial that investors stay informed about the emerging risks as well as the possible opportunities presented by AI.
Visit the eToro Academy to learn more ways to enhance your investing skill set.
FAQs
- How much of the stock market is made up of automated trading?
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It is estimated that between 70 to 90% of stock market transactions are attributable to automated trading. The relatively large range of that estimate is explained by there being localised differences – depending on the specific asset class involved, the ways in which “automated trading” is defined and how trading styles adjust to market conditions.
The definition used to come up with the 70–90% range includes algorithmic trading and high-frequency trading by institutional investment firms and passive investment strategies such as ETFs.
- What is “explainable AI”?
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Explainable AI (XAI) is a term which relates to artificial intelligence systems that provide explanations of their decision-making and predictions in a clear format which is understandable by human operators. It could offer opportunities to increase trust in AI, facilitate compliance with regulations, and is often referred to as unboxing the “black box.”
- What is the difference between an AI agent and a robo-advisor?
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Robo-advisors are a form of automated portfolio management which have little to no human interaction. They use algorithms based on established “best practice” to invest in a way which matches a client’s stated investment aims. In the context of investing, the term AI agents refers to models which replicate human-like decision-making to gather data, generate insights and test and implement investment strategies.
- What is the meaning of “thematic insight” in investing?
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The term “thematic insight” in investing refers to a long-term strategy that identifies and invests in companies expected to benefit from major global themes, such as AI adoption or renewable energy. It is based on the belief that these trends will create significant structural changes and growth across industries and economies.
It does not necessarily focus on a particular sector, such as AI stocks, but adopts a broader mandate and seeks out companies across different sectors that may initially appear to be uncorrelated, but which are positioned to benefit from the same overarching trends.
- Can I invest in AI stocks and companies?
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Yes, it is possible to invest in AI stocks and there are various pathways into the sector. One is to invest in single AI stocks or you might alternatively consider diversifying your exposure to the sector (and mitigate against the risk of making a bad stock pick) by investing in ETFs such as the ARK Artificial Intelligence and Robotics ETF (ARKI).
The ARK ETF is a basket of companies that have exposure to the development of new AI products or services, technological improvements and advancements in scientific research. Investing in companies developing AI is distinct from using AI tools for your own trading decisions.
This information is for educational purposes only and should not be taken as investment advice, personal recommendation, or an offer of, or solicitation to, buy or sell any financial instruments.
This material has been prepared without regard to any particular investment objectives or financial situation and has not been prepared in accordance with the legal and regulatory requirements to promote independent research. Not all of the financial instruments and services referred to are offered by eToro and any references to past performance of a financial instrument, index, or a packaged investment product are not, and should not be taken as, a reliable indicator of future results.
eToro makes no representation and assumes no liability as to the accuracy or completeness of the content of this guide. Make sure you understand the risks involved in trading before committing any capital. Never risk more than you are prepared to lose.