Artificial Intelligence in Financial Markets: Global Impact on Retail Trading

Artificial Intelligence in Financial Markets: Global Impact on Retail Trading

The use of artificial intelligence (AI) in financial markets has shifted from an innovation reserved for large banks and hedge funds to a tool now within reach of retail traders around the world. From automated buy-sell algorithms to sentiment analysis on social media, AI is transforming how individual investors operate, make decisions, and manage risks. International organizations and regulators are closely monitoring this trend, highlighting its promising benefits while warning of emerging risks. Below, we explore the main applications of AI in retail trading, with a global perspective, and analyze its potential advantages and challenges for individual investors.

1. Trading Algorithms Within Everyone’s Reach

Trading algorithms, once the exclusive domain of sophisticated operators, are increasingly available to retail investors. It is estimated that over 60% of trading volume in developed markets comes from algorithmic operations, and in the U.S., only about 10% of transactions are executed by traditional human investors, according to JPMorgan (JPM). This dominance of automated trading encourages individuals not to be left behind. In fact, the retail investor segment is the fastest-growing group in algorithmic trading, expanding at an estimated 12% annually, fueled by the rise of accessible platforms, user-friendly interfaces, and preconfigured strategies. New cloud-based solutions with API access have dramatically lowered the barriers to entry, democratizing access to professional-grade trading tools and enabling small investors to deploy advanced strategies previously reserved for institutions.

2. OrionONE: Adaptive AI for Retail Traders

A clear example of how artificial intelligence is reaching individual investors is OrionONE, a platform developed by Whale Analytics. Designed for users without technical expertise, OrionONE acts as an intelligent assistant that continuously learns and adapts its strategies in real time based on current market conditions.

Unlike rigid tools that rely solely on historical data, OrionONE processes live information—such as prices, news, and even social media sentiment—to quickly identify opportunities and anticipate risks. In addition to automating repetitive tasks, the system integrates both technical and fundamental analysis, recalibrating its parameters when it detects significant changes, helping users make faster and more objective decisions.

With a strong focus on risk management and minimizing emotional bias, OrionONE aims to optimize the balance between returns and exposure, offering retail traders a professional-level experience through an accessible, scalable, and fully automated platform.

3. Sentiment Analysis on Social Media: The Market Thermometer

Another booming application of AI in retail trading is sentiment analysis on social media and financial forums. Platforms like Twitter, Reddit, or live news channels have become thermometers for retail investor sentiment. AI tools using machine learning and natural language processing (NLP) can scan millions of tweets or posts to extract the dominant tone (positive, negative, fear, euphoria) regarding certain assets, providing early signals of market movements.

Recent studies confirm that online sentiment has predictive power. One study analyzed nearly 3 million stock-related tweets over a year and found that sentiment indicators from Twitter could predict intraday movements of stock indices with over 50% accuracy, reaching up to 55% in the case of the S&P 500. Specific emotions like “fear” and “confidence” were the most effective at anticipating changes, which aligns with behavioral finance theories about investor psychology. Interestingly, this effect isn’t limited to one market: the study found a correlation between Twitter sentiment and stock direction in both developed (U.S., Europe, Japan) and emerging markets.

In practice, this means that a sudden shift in online mood—for instance, a wave of pessimism detected in thousands of posts—can serve as an early warning sign of corrections or even speculative bubbles driven by retail traders. It’s no coincidence that some regulators have begun monitoring these indicators: there’s a growing push to integrate social media sentiment into financial stability assessments and even use AI to track real-time disinformation campaigns or price manipulation via social platforms.

4. The Double-Edged Sword of Sentiment: Opportunities and Traps

However, capitalizing on mass sentiment carries significant risks. Emotional contagion can lead to herd behavior and irrational decisions. An academic study that analyzed the activity of 25 million retail users, comparing it with Reddit activity (a forum known for events like the GameStop surge), found that trades driven by social media trends tended to underperform compared to those that weren’t influenced by online chatter.

Specifically, trades triggered by spikes in mentions on social media yielded 1.6% to 2.8% lower returns than comparable positions opened the same day. Furthermore, investors who dedicated a greater portion of their portfolios to “trendy” stocks often recommended on forums earned around 2% less annually than their peers. The authors attribute this underperformance to poor timing (retail investors often join too late, after an asset has already surged due to hype) and to classic behavioral biases amplified by social media—such as selling winners too soon and clinging to losers (known as the disposition effect).

Recent events highlight this dynamic: the collapse of Silicon Valley Bank in 2023 was dubbed the first “social media-driven bank run,” as rumors and discussions online exacerbated depositor panic and accelerated fund withdrawals. This extreme case illustrates how the same networks that create opportunities can also amplify systemic risks.

5. AI in Portfolio and Risk Management: The New Investor Advisor

Portfolio management hasn’t escaped the AI wave either, offering retail investors tools once exclusive to professional fund managers. Robo-advisors are a clear example: they use AI algorithms to design and rebalance customized portfolios based on each client’s risk profile. Their global adoption has grown exponentially over the last decade, surpassing $1 trillion in assets under management in the U.S. alone by 2023 (compared to $200 billion in 2018). Globally, robo-advisors are projected to manage $3.3 trillion in assets by 2030, reflecting the trust that millions of retail investors are placing in automated wealth management. AI enables these systems to adjust allocations among stocks, bonds, or ETFs dynamically, responding quickly to market changes or shifts in the investor’s life circumstances, all while charging lower fees than traditional advisors.

Beyond robo-advisors, retail traders are using AI in daily risk management. For instance, there are applications that, using machine learning, analyze a trader’s transaction history and suggest optimal stop-loss levels or position sizes to avoid overexposure. Other tools assess a portfolio’s diversification and alert the user if excessive correlation among assets is detected—indicating the investor might be more exposed than they realize in certain adverse scenarios. In more sophisticated markets, AI can now simulate thousands of stress scenarios (e.g., crashes, liquidity crises) almost instantly—something an individual operator could hardly do manually. This allows the retail investor to identify vulnerabilities and proactively adjust their strategy, similar to the stress testing conducted by large asset managers.

6. Informational Automation: When AI Reads for Us

Even in the realm of information, AI is making a significant impact. Traditionally, when the Federal Reserve released the minutes of its meetings, markets would take minutes or even hours to digest the dense content. Today, however, AI-powered algorithms can read and summarize complex documents in seconds, extracting key phrases related to monetary policy and executing trades before a human finishes scanning the first page.

The IMF notes that since the emergence of advanced language models (LLMs) in 2017, the movements of the S&P 500 during the first 15 seconds after the Fed’s lengthy statements tend to already align with the direction the market takes 15 minutes later—something that didn’t happen in the pre-AI era. This suggests that machines are reacting earlier, arbitraging information faster than ever. For the individual investor, this can mean greater efficiency (less informational lag), but also implies that the advantage of “being the first to know” has essentially vanished in the face of algorithms. Without speed on their side, strategy and planning become the retail trader’s best edge.

7. AI as a Shield: Preventing Fraud and Cyber Threats

Security is another critical area where AI is transforming the retail trading experience. Brokerages, stock exchanges, and payment providers are increasingly deploying intelligent systems to detect fraud, hacking attempts, or market manipulation before they can cause damage. These AI systems monitor unusual patterns: for example, an algorithm might detect atypical behavior in a client’s account—such as sudden massive sell-offs or transfers to unfamiliar destinations—and immediately flag the activity as potential fraud, requiring additional verification.

Similarly, exchanges are using AI-powered surveillance tools to recognize market manipulation signals, such as coordinated “pump and dump” schemes spread via online platforms or out-of-control algorithms, helping to stop suspicious activity. According to the European Securities and Markets Authority (ESMA), the growing automation of trading—especially high-frequency trading (HFT), which accounts for 24% to 43% of stock exchange volume in Europe—has required more robust monitoring systems, many based on AI, to preserve market integrity and prevent abuse.

In payments and retail banking, the results of AI-based fraud prevention are tangible. One major global card network reported preventing nearly $40 billion in fraudulent activity between October 2022 and September 2023, thanks to intensive use of AI and machine learning—almost double the amount stopped the previous year. These technologies can detect illicit patterns among millions of transactions in real time—such as identifying a cloned card by the sequence of purchases—far more accurately and rapidly than traditional rule-based systems.

8. From Shield to Weapon: AI Also Powers New Scams

Paradoxically, while AI helps fight fraud, it is also being exploited by malicious actors to develop new types of investment scams targeting retail investors. A report from the Ontario Securities Commission (Canada) describes how generative AI is “turbocharging” traditional scams: with deepfakes (hyper-realistic fake videos) and voice cloning, fraudsters can impersonate celebrities or trusted advisors to promote fake investment opportunities.

The reach and effectiveness of these schemes increase dramatically with technology, creating fraud scenarios that would have been impossible without AI. In controlled experiments, researchers found that novice investors exposed to scam content enhanced with AI were 22% more likely to invest in the fraud than those who saw the same scams without the AI enhancements. These findings suggest that the “polished” appearance created by AI—professional-looking websites, tailored psychological messages, and convincingly realistic multimedia—makes fraudulent schemes more believable, increasing the risk for retail audiences.

Authorities and experts are proposing countermeasures, from “inoculation” campaigns that train users to spot too-good-to-be-true offers, to browser plugins that alert users when they visit high-risk investment pages. Still, the first line of defense remains digital and financial literacy: understanding that in the AI era, not everything that shines is gold, and being extremely cautious before committing any money.

9. Benefits of AI for Retail Traders

As we’ve seen, AI offers individual investors unprecedented capabilities, but it’s important to clearly outline the main benefits it can provide to retail traders:

• Greater access to information and advanced analysis: A retail trader equipped with AI can process in minutes the news, prices, financial statements, and tweets that would take days to analyze manually. This partially levels the playing field with professional investors, allowing individuals to detect opportunities or risks hidden within large volumes of data that would otherwise be unmanageable. For example, AI tools can scan thousands of data points and detect profitable patterns a human might overlook.
• More efficient and emotion-free trading: Automation through algorithms allows trades to be executed 24/7 without fatigue or emotional interference. This helps retail traders stick to their strategies (avoiding panic or euphoric decisions) and capitalize on opportunities even while they sleep. It also reduces operational costs and human error in execution. A well-configured bot doesn’t “freeze” during a crash or make impulsive decisions—it acts strictly according to predefined rules.
• Personalized management and optimal diversification: With AI, even a small saver can access a globally diversified portfolio tailored exactly to their goals and risk tolerance—something that previously required an expensive financial advisor. Algorithms can rebalance assets in response to market movements or life changes (e.g., adjusting to lower risk as retirement approaches) dynamically. This keeps investments aligned with personal objectives and better protected.
• Real-time risk monitoring: AI can constantly watch an investor’s portfolio and alert—or act—when early signs of danger appear, whether it’s a breached Value at Risk (VaR), an unusual spike in volatility, or breaking news that negatively affects held assets. This allows for quick responses to limit losses or hedge exposures. AI systems can also suggest hedging strategies (like buying options) or adjusting leverage levels proactively, improving retail investor resilience to market shocks.

10. Risks Retail Investors Should Consider

Alongside its many benefits, AI also presents new challenges that retail traders must understand and manage carefully. Here are some of the main risks:

• Opaque complexity and blind trust: Many AI solutions operate as black boxes, with models so complex that even their creators can’t easily explain why they make certain decisions. For a retail investor, this means relying on tools whose exact criteria they don’t understand, making it difficult to judge whether a recommendation is sensible or flawed. Regulators are concerned that this lack of explainability makes risk management more difficult: poorly calibrated models or ones trained on biased data can fail catastrophically in unforeseen conditions, and users might not understand why.
• Herding behavior and amplified volatility: If many retail investors use similar algorithms or rely on the same AI signals, it increases the risk of synchronized behavior. The widespread use of common models can drive up correlations in trades and asset prices. In other words, everyone buys or sells at once in response to the same trigger, amplifying market movements. This could fuel flash crashes or liquidity crunches under stress. One international report warned that widely used AI strategies could worsen instability: during the March 2020 selloff, some quant funds accelerated their selling, deepening the drop—and AI-driven funds showed even faster rotations than usual.
• Conflicts of interest and manipulation risks: AI also opens the door to conflicts of interest in platforms that provide advice or execute trades for retail clients. The U.S. SEC has warned that some brokers could use predictive algorithms to maximize their own profits at the expense of clients—for instance, by encouraging excessive trading if the models prioritize firm revenue. In 2023, the SEC proposed new rules requiring firms to identify and eliminate any algorithmic biases that favor the broker over the client, due to the massive scale these technologies can reach. Additionally, AI can be used to manipulate investors—from bots inflating online sentiment around a stock to deceptive “advisors” pushing assets their creators want to exit. Retail traders must stay alert to seemingly personalized recommendations that may hide hidden agendas.
• More sophisticated fraud and cyberattacks: As discussed earlier, AI is enhancing new forms of investment fraud (deepfakes, automated scam bots, etc.). Cybercriminals can also use AI to exploit vulnerabilities in trading platforms or digital wallets, attempt password combinations at scale, or even mimic a user’s voice to trick verification systems. In a hyper-digitized and interconnected financial world, security failures can be catastrophic.
• Third-party dependence and systemic risks: Many retail investors rely on AI tools provided by third-party companies or startups. If one of these providers suffers a failure, cyberattack, or mass-scale error in their signal generation, thousands of users could be impacted simultaneously. The Financial Stability Board (FSB) warns that heavy concentration in a few AI model or infrastructure providers creates operational risk: a disruption in a widely used service could have systemic repercussions. Also, access to high-quality data is critical—any sudden restriction or cost increase in data sources could leave many algorithms “blind” at the same time.

A New Financial Era, With a Critical Mindset

In summary, AI provides retail investors with unprecedented tools to improve their decision-making and outcomes—but it’s far from an infallible solution. The advantages—more information, objectivity, efficiency, and control—come hand-in-hand with new risks that require caution, education, and, in many cases, updated regulations.

Experts recommend a “human + machine” approach: leveraging the speed and power of AI, while still applying critical human judgment, especially when making high-stakes investment decisions. In the years ahead, broader adoption of AI in financial markets is expected: surveys of market participants predict that AI-driven high-frequency trading will become much more common in equities, bonds, and liquid derivatives. At the same time, most agree that the human role will remain essential—to supervise the machines and make final decisions on major capital allocations.

Ultimately, AI is transforming global retail trading by empowering small investors with tools once exclusive to institutions. Its influence is already felt across every corner of the market—from how a trader accesses news on their phone to how orders are executed milliseconds after a press release is published. It’s clear we are entering a new era of AI-powered finance. The challenge will be to maximize the advantages of this technological revolution while minimizing its risks, ensuring AI becomes a force for the democratization of investment opportunities—without compromising market stability or investor protection.

As with any major innovation, the right balance between excitement and prudence will be key to long-term success.

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Ignacio N. Ayago CEO Whale Analytics & Mentes Brillantes

Permíteme presentarme: soy Ignacio N. Ayago, un emprendedor consolidado 🚀, papá con poderes 🦄, un apasionado de la tecnología y la inteligencia artificial 🤖 y el fundador de esta plataforma 💡. Estoy aquí para ser tu guía en este emocionante viaje hacia el crecimiento personal 🌱 y el éxito financiero 💰.

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