The global trading market is a diverse and dynamic financial ecosystem. It engages various types of players such as institutional hedge funds, family offices, individual traders, and quant trading firms.
In 2022, there were over 1.5 million retail investors actively trading shares, who collectively invested $1.51 billion into U.S. stocks every day. In the allure of get-rich narratives surrounding investments, the stark reality reveals that up to 84 percent of the 1.5 million retail investors found themselves with losses.
Besides the stock market, there is growing interest in emerging asset classes like the digital asset market, fueled by the anticipation around the US SEC’s approval of a Spot Bitcoin Exchange-Traded Fund (ETF). An estimated $5 billion of new investments are expected with the approval, ushering in a flood of institutional and retail investors.
Copy trading’s double-edged sword
In the fiercely competitive financial landscape, the dynamic interplay between retail investors, professional traders, and institutional players underscores a stark disparity in expertise and resources. For instance, institutions and professional traders typically have top-notch trading tools, including cutting-edge technology and privileged information. This often places them on a superior level among new entrants.
Amidst the challenges faced by inexperienced investors, copy trading has gained popularity beginning in the early 2010s through platforms such as eToro. This method enables less experienced investors to mirror the trading activities of seasoned professionals, providing access to successful strategies for those lacking the expertise to formulate their own.
Copy trading has become widely offered among brokers, trading platforms, and digital asset exchanges. However, despite its appeal to novices, this approach comes with its set of risks.
“Conventional copy trading has apparent benefits for novice users. However, the shortcomings often outweigh the benefits — the technical inefficiencies associated with this model lead to varying results for the copiers. The leading traders’ results will always be different than the copiers’, making it an ineffective tool for portfolio management,” says Bartolome R. Bordallo, Co-Founder and CEO of Zignaly.
Copy trading can also lead to misaligned incentives when experienced traders, aware of their copiers, use or exploit this knowledge to their advantage, and even trade against their copiers.
Aligning incentives with AI FinTech
Just as AI is transforming numerous sectors, it is creating new financial opportunities for retail investors by providing advanced tools that enable informed decision-making. Through machine learning algorithms, AI platforms can analyze vast datasets in real-time, identifying trends and patterns that human analysis might overlook, thus empowering retail investors with valuable insights.
For businesses, AI has the scalable power to automate financial services and better serve their end users. “While advances in AI are incapable of eliminating credit, markets, liquidity, and operational risks entirely, we believe that this technology can play a significant role in helping financial institutions more quickly identify, plan for, and respond when these risks inevitably arise,“ according to insights from Andreessen Horowitz.
For investors, AI helps both new and experienced to find the right financial products, each with its varying risk profiles and potential returns. For example, Zignaly’s AI algorithm Z-Score analyzes datasets spanning 22 million trades, assessing factors like profitability, risk, and consistency, to evaluate traders and pinpoint the most suitable fund managers for investors.
By leveraging AI to select optimal traders for different types of users, Zignaly’s AI-powered social investment platform enables a profit-sharing structure, strongly aligning the incentives of investors and fund managers. “AI-driven profit sharing models are an automated investment solution that assures identical results for all of the participating investors. By utilizing a pooled fund management strategy, it allows users to safely delegate their funds to be professionally managed by fund managers,” says Bordallo.
The platform connects over 500,000 users to more than 150 veteran fund managers, who collectively manage over $125 million in digital assets.
Unlike conventional copy trading platforms, funds in Zignaly’s profit-sharing model are managed in isolated pools to mitigate risk for investors, while simultaneously creating a transparent and fair profit-sharing structure. To reach a broader audience of international investors, its Z-Prime solution enables pro traders and enterprises to customize their own profit-sharing platforms.
Forging sustainable financial ecosystems
The transformative impact of AI in the financial landscape is evident through advanced tools and personalized strategies tailored for retail investors. For profit-sharing models that leverage AI, they not only facilitate informed decision-making but also foster a transparent and fair ecosystem for investors of all backgrounds.
Utilizing a democratized hedge fund strategy, Zignaly has amassed a trading volume surpassing $10 billion, forging a more robust alignment of incentives between digital asset investors and fund managers. These sustainable revenue-generating platforms play a pivotal role in empowering the emerging digital asset ecosystem, supported by initiatives like deploying significant portions of the revenue to buy back and burn the platform’s ZIG asset.
The rise of AI in finance signifies a paradigm shift, providing unprecedented opportunities for investors to navigate and thrive in an ever-evolving market.
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Featured image: Freepik.
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