AI in Retail Trading: What We Know About its Growing Impact

This technology can be used in financial markets to forecast future trends. There is a catch though. Many companies were quick to join the AI trend, often rebranding their existing data analytics tools as “AI” for a boost in public relations. However, discerning which companies are genuinely leveraging AI in their trading products can be a complex task. 

That said, if AI enables every trader to stand out, does that mean nobody truly stands out? This is somewhat accurate. As AI evolves, the benefits gained from data analysis, technological leverage, and operational improvements are becoming increasingly marginal. Additionally, it’s not a situation where everyone can triumph. In every trade, there’s always someone on the other side, whether buying or selling. For there to be a winner, there must be a loser.

In this article, we’re discussing how AI is used in retail trading, highlighting relevant trends, benefits, risks, and mitigation techniques. Let’s get started!

What Is AI Trading?

AI trading involves using artificial intelligence, predictive analytics, and machine learning for analyzing market data, generating investment ideas, building portfolios, and automating buying and selling decisions.

In retail trading, AI employs machine learning, sentiment analysis, and algorithmic predictions to analyze vast data sets and execute trades at optimal prices.

Benefits of AI in trading

Predictive Power: AI can predict market trends and capitalize on price fluctuations more effectively than human traders. AI bots are trained on historical data to apply acquired knowledge to current market conditions.

Accessibility for Retail Traders: AI enables individual traders to manage complex operations, like statistical arbitrage, across many asset classes.

Cost Reduction: AI cuts costs by automating repetitive tasks, benefiting both big investment operations and individual traders.

Applications of AI in Trading

AI facilitates near-instantaneous market trading and sophisticated trade management. Tools are available for setting criteria-based entry and exit strategies, reducing the emotional impact on trading decisions.

AI aids in portfolio optimization, helping investors identify portfolios that align with their risk tolerance and time horizon. It can also craft portfolios that lie on the efficient frontier, maximizing returns relative to risk.

AI algorithms can predict stock or security movements for profit. While not all models are accurate, they can be used for identifying market cycles or making automated entries and exits based on technical analysis.

AI can be employed for various risk management techniques, such as reducing over-exposure to individual stocks or establishing automated options strategies for risk mitigation.

Summing up, these are the practical use cases:

• Trading Strategies: AI is used to develop and refine trading strategies.
• Data Analysis: Identifying patterns and predicting market movements.
• Task Automation: Streamlining financial operations.
• Asset Picking: AI can efficiently sort through numerous data points to identify assets that meet investors’ criteria. 

Challenges for Retail Traders in AI Trading:

• Technical and Financial Barriers: Retail traders face challenges in entering AI trading, such as the need to learn coding, hire technical experts, or invest in expensive software.
• Requirement for Specialized Platforms: They need access to platforms that support AI algorithm validation, like backtesting and paper trading, which can be another hurdle.
• Black Box Risk: Difficulty in understanding and interpreting AI systems.
• Overfitting Risk: AI systems recognize patterns only in training data, leading to inaccuracies in new data.
• Model Risk: Complex statistical models in AI systems are hard to interpret.
• Data Risk: Bias in AI systems due to unrepresentative training data.
• Privacy Risk: Potential privacy violations from processing large amounts of personal data.
• Security Risk: Vulnerability of AI systems to hacking and malicious activities.
• Hallucinations: AI systems, such as trading bots, can generate data or signals without referencing actual data. This phenomenon, seen in technologies like ChatGPT and Google’s Bard, could lead to trading systems producing baseless signals.
• Emergence of New Functionality: AI systems may develop unintended features or properties, which can be particularly problematic in financial applications.
• Quality of Data: The effectiveness of AI in predicting market trends heavily depends on the quality and diversity of the data fed into the system. Limited or biased data can result in poor predictions.

Mitigating Risks in AI Trade Systems

• Multiple Data Sources: Reduce overfitting by training AI systems with diverse data sources.
• Continuous Research: Stay updated with AI and machine learning research to use the most current standards.
• Backtesting: Use historical data to test how the AI system would have performed in the past.
• Simulation: Create real-world models to test the AI system in various scenarios.

The Institutional vs. Retail Investor Divide 

The key differences between institutional and retail investors in the world of finance revolve around two main aspects: data analysis and operations.

Data Analysis Edge: Institutional investors have access to superior, real-time data and top-tier analytical talent. Retail investors, by comparison, work with more limited resources.

Operations Edge: Institutional players are better equipped to quickly and effectively act on market opportunities, whereas retail investors may struggle with the complexities of trading in derivatives or other advanced financial instruments.

The Role of AI in Bridging the Gap

Evolving Data Analytics: As AI technology advances, the edge provided by data analytics and operational efficiencies is expected to diminish, making the financial market more accessible to retail investors.

Creativity: The uniquely human trait of creativity remains crucial. AI relies on historical data, but human investors can anticipate and innovate based on emerging trends and societal shifts.

Are Retail Traders Still able to Compete in AI Trading? 

Embarking on a journey into AI-driven trading can be a challenging endeavor for retail traders. Initially, they are faced with the need to either acquire coding skills, hire specialized technical experts, or invest in costly legacy software to start trading with AI. Additionally, they need to seek out platforms that support essential AI algorithm validation methods like backtesting and paper trading. These hurdles, among others, can discourage those retail traders who are keen on incorporating AI into their trading strategies.

However, the notion of AI trading in the retail sector is not a far-fetched one. While retail traders may lack the financial clout to sway market directions, their smaller trade sizes afford them greater agility in market navigation. Furthermore, they are not subject to the same regulatory constraints as larger market participants. These advantages, coupled with the growing availability of automated trading tools, have the potential to empower retail investors and contribute to a more equitable financial market.

The Bottom Line

Artificial intelligence’s integration into the investment world is not just a passing trend—it’s here to stay. Even if you’re not personally harnessing AI for your investments, it’s highly likely that your portfolio or fund managers are, as are investment advisors who are increasingly relying on the same technologies as robo-advisors to assess risk profiles and curate optimal portfolios for their clients. The good news is, that AI-powered investment tools have become broadly accessible for individual investors.

For those who actively manage their investments, the use of AI extends beyond basic portfolio oversight. It plays a vital role in guiding buy and sell decisions, as well as in handling trading strategies. Therefore, it’s crucial for investors to stay informed about the latest advancements in AI within the investment sector. Investors should evaluate the AI tools available on their current platforms to ensure these meet their specific needs. If they find these tools lacking, they might consider switching to a broker with more sophisticated AI capabilities or enhancing their current setup with third-party AI investment software. 

However, AI trading also introduces new risks, including increased volatility due to instant reactions to market conditions. During turbulent times, this volatility can lead to negative ripple effects. The quality of AI algorithms is crucial, and due diligence is necessary to ensure their reliability.