• Algorithmic trading: AI-powered trading platforms can now execute trades at speeds impossible for humans to match. According to IBM's report, around 80% of financial firms leverage AI for real-time market analysis and trade execution. This has contributed to the growth of high-frequency trading (HFT), which allows thousands of trades to be made in microseconds. ^[6]
• Portfolio management: AI also plays a critical role in portfolio diversification. By assessing economic trends, geopolitical risks, and historical data, AI helps create more balanced portfolios. Forbes highlights^[7] that AI-powered portfolio management can reduce risk exposure by up to 25%, an advantage in volatile markets.

Similarly, BlackRock, the world's largest asset manager, utilises AI-driven tools^[10] to track market trends and improve its investment strategies. Their partnership with Microsoft and NVIDIA^[11] underscores the importance of building robust AI infrastructures to stay competitive in global markets.

The opportunities and risks of AI in trading While the benefits of AI in trading are compelling, it's essential to acknowledge the risks. According to the State of AI in Financial Services: 2024 Trends^[12] report, one of the major challenges for businesses is preserving data privacy and building secure AI: 84% of financial organisations have already implemented or plan to implement a framework to govern how AI will be built, trained, and used to adhere to business principles and relevant regulation.

For traders and investors, a key concern is over-reliance on algorithms. They might become too dependent on AI systems, leading to a disconnect from market fundamentals. In extreme cases, this could result in flash crashes, where AI systems react too quickly to market anomalies, causing extreme volatility in short periods.

Furthermore, AI models are only as good as the data they're trained on. Poor data quality can lead to inaccurate predictions, which, in turn, may cause significant financial losses. This is why financial institutions must prioritise data integrity and transparency when deploying AI systems.

Despite these challenges, the risks can be mitigated through a combination of human oversight and continuous model improvement. When used responsibly, AI provides immense value to investors by reducing human error and making more informed, data-driven decisions.

Here are a few practical steps for those considering integrating AI into their trading strategies:

1. Test AI tools before full integration: for example, through demo accounts or backtesting. This allows investors to see how the AI performs under different market conditions without risking real capital.
2. Stay informed: the AI sphere constantly evolves, with new tools and models emerging regularly. Stay updated on the latest advancements in AI to ensure you're leveraging the most up-to-date technologies.
3. Diversify with AI: don't rely solely on AI for trading decisions. Use it as part of a broader strategy that includes traditional analysis and risk management techniques.

Hashtag: #Octa

The issuer is solely responsible for the content of this announcement.

References

1. ^{^} Media OutReach Newswire (www.media-outreach.com)
2. ^{^} NVIDIA's 2024 survey (blogs.nvidia.com)
3. ^{^} According to BCG (www.bcg.com)
4. ^{^} According to NVIDIA (resources.nvidia.com)
5. ^{^} provides AI-driven analysis (tradersunion.com)
6. ^{^} IBM's report (newsroom.ibm.com)
7. ^{^} highlights (www.forbes.com)
8. ^{^} leveraged AI-driven models (quartr.com)
9. ^{^} returns exceeding 66% (www.flossbachvonstorch-researchinstitute.com)
10. ^{^} utilises AI-driven tools (www.blackrock.com)
11. ^{^} partnership with Microsoft and NVIDIA (www.datacenterfrontier.com)
12. ^{^} State of AI in Financial Services: 2024 Trends (resources.nvidia.com)