Financial markets are complex systems influenced by factors such as geopolitical events, interconnected institutions, and leverage used in asset control. Investors need a way to efficiently monitor all data and generate actionable insights to enable informed decision-making.
Many companies are conducting advanced research to leverage AI’s potential in solving financial market complexities, investing in deep tech, quantum computing, and energy innovation. Institutional investors benefit by gaining improved methods to capture, analyze, and extract insights from trading signals.
Deep tech investments in AI platforms for market analysis
An October 2024 IMF study reports that “adoption of AI in capital markets is likely to increase significantly in the near future, and AI could cause large changes in market structure.”
Market factors fluctuate frequently, so investors use algorithmic trading for real-time decisions. AI-powered platforms offer adaptability, enabling strategy adjustments based on trends, risks, and opportunities. These platforms can also handle higher trading volumes, especially liquid assets like government bonds and equities.
Hybrid intelligence systems combine AI with human expertise
Human expertise still plays a significant role in this industry. A Morgan Stanley survey finds that “even if AI will revolutionize the field (investing), investors are unlikely to place all of their trust in the technology, and human advisors will continue playing a critical role.” The survey indicates that 82 percent of investors are not keen on totally replacing human insights and guidance.
This leads to the fusion of AI analytics and human decision-making, forming a hybrid approach. AI takes the emotion out of investment decision-making, which results in more objective judgments. Meanwhile, human involvement helps establish trust in the system and gives investors a sense of accountability. AI systems can anticipate black swan events and market volatility movements, but it is part of human nature to seek reassurance and have someone to hold to account in case a system fails.
Adaptive and sophisticated trading tools in the financial market
AI-driven adaptive investment tools offer an alternative to algorithmic trading, which is associated with a number of drawbacks, such as lower effectiveness in the face of heightened market volatility and systemic risks since widespread algorithmic trading failures can severely destabilize the financial markets.
Adaptive AI-powered investment platforms enable investors to make personalized, precise predictions and adjust strategies in real-time based on market activity.
Examples of AI solutions in institutional trading
Even investment powerhouse Blackrock has no qualms about admitting it already uses an AI system. AI tools not only exist but are also being widely used. Below are some notable platforms demonstrating effective technological augmentation in investing, particularly hybrid implementations and adaptive trading.
Kavout
One of the biggest names in machine learning-driven investing, Kavout was hailed as the top FinTech company in 2016 at the NVIDIA GTC Global Conference. The platform adapts dynamically to market sentiment, covering about 80 percent of market factors with a database of over 1,000 elements. It features a proprietary machine learning system called Alpha Signal to implement adaptive asset allocation with minimal impact from market volatility.
AlphaSense
AlphaSense uses AI and natural language processing to analyze investment information efficiently. It is designed to ensure that the deluge of data does not become overwhelming for investors, allowing them to quickly extract insights based on millions of data sources, from earnings reports to broker research, regulatory documents, and expert calls.
It is designed to help investors avoid blind spots when tracking critical insights and getting useful insights before more prospective investors get to them.
Metafide
Originally a B2B trade information provider for hedge funds and market makers, Metafide seeks to become the go-to trading tool for institutional investors and hedge funds by providing reliable AI-powered real-time market analysis and investment signal tracking, focusing on Web3.
Metafide is designed as a hybrid platform that fuses the benefits of AI and human expertise. Its unique approach to investing is two-tiered. The first part thoroughly researches the markets and generates predictions from neural networks. The second part highlights human involvement, wherein users who made trades are viewed as signals and subjected to a recency-weighted bias. The AI system then discerns the impact of these users in generating trading insights. Moreover, Metafide continuously monitors various signals to support adaptive trading, generating and executing real-time trade plans based on dynamic market movements.
“By incorporating gamified systems to engage users, we extract real-time market sentiment in an effortless way,” says Frank Speiser, co-founder of Metafide. The platform “focuses on incentivizing community-driven insights, user-friendly experience, and trust through transparency,” adds Speiser, who was instrumental in making social sentiment-driven decision-making mainstream with having previously founded Socialflow.
Its RANGE FIDE(R) game is built on Mantle Network and recently had over 1 million transactions. RANGE FIDE(R) challenges human intelligence to outsmart AI predictions, with players forming a coalition to defend human decision-making in the crypto space.
The future of AI in institutional trading
AI is undoubtedly going to be a staple of modern institutional trading. However, it is not yet viewed as a replacement for human decision-making but as a vital aid.
Generally, institutional traders perceive AI trading platforms with enthusiasm, but they are also aptly cautious. AI-powered platforms offer valuable insights but require ongoing innovation to keep pace with the ever-evolving investment markets.
TNGlobal INSIDER publishes contributions relevant to entrepreneurship and innovation. You may submit your own original or published contributions subject to editorial discretion.
Featured image credit: Freepik