Neural networks, particularly large language models (LLMs), have shown immense potential for sentiment analysis in trading. Since their introduction in 2017, the share of AI-related patent applications in algorithmic trading has surged from 19% to over 50% annually^[6]. The tools are already deployed to process market sentiment from news and social media in near real-time, offering traders insights into geopolitical developments and economic forecasts.

Algorithmic trading has grown substantially, with AI-driven systems enabling faster execution and reduced operational errors. High-frequency trading powered by AI has seen significant adoption, particularly in liquid asset classes such as equities and derivatives. While detailed statistics on future adoption rates remain speculative, the World Trade Organization's focus on the digital transformation of markets underscores the increasing reliance on automation^[7] to enhance trading efficiency and liquidity.

Opportunities for traders in 2025 AI's capacity to process sizable quantities of historical and real-time facts allows investors to benefit from predictive insights that had been formerly inconceivable. Advancements in AI-powered predictive analytics are changing how we forecast international markets by making predictions more accurate^[8] and providing clear, actionable insights. These tools are transforming financial markets, helping investors spot trends and respond to changes with greater confidence.

Emotions can often get in the way of smart trading decisions, especially when markets are highly volatile. AI helps solve this problem by relying purely on data and predictive models for decision-making. According to the IMF's Global Financial Stability Report, AI-driven tools are already helping retail traders manage risks more effectively and avoid impulsive trades^[9] that could lead to losses.

As AI tool costs decrease, features like real-time portfolio optimisation and automated trading strategies are becoming accessible to individual traders. Previously available only to large financial institutions, these advanced tools are levelling the playing field, enabling retail investors to trade with more confidence and accuracy.

Risks and challenges in AI integration AI-driven business strategies come with inherent risks. The IMF warns^[10] that over-reliance on algorithmic models could increase market volatility during a global crisis. For example, AI-driven exchanges exhibited herd-like behaviour during the March 2020 market turmoil. This resulted in significant price volatility and required a robust regulatory mechanism to manage the risks involved.

The integrity of AI systems faces increasing security challenges. Research shows that the effectiveness of AI models depends on data quality and security. Recent statistics reveal an alarming trend: cyber threats targeting AI are increasing by 47%^[11]. The industry requires robust security measures to protect the algorithms against data manipulation and unauthorised access.

While AI offers tremendous value, its complexity poses a challenge for low-tech businesses. The complexity of advanced AI systems makes it crucial to have accessible training resources and intuitive interfaces. These tools help traders, especially newcomers, understand and use AI effectively, paving the way for broader adoption across trading communities.

Preparing for AI-driven transformation Thriving in an AI-pushed trading environment requires specific training. Otherwise, traders risk facing sophisticated systems they can't properly handle or misusing AI-based tools, consequently missing out on their benefits. To prepare for future AI tools, traders should try the available software now. The safest option is to test AI deployment when trading on a demo account. This option is available on trading platforms provided by global brokers like Octa. For example, Octa broker currently uses AI to facilitate graph analysis and boost pattern identification when conducting technical analysis. Following the increased AI adoption trend, the company will likely keep embedding more AI-based tools on the platform.

AI is set to further redefine trading in 2025. From enhanced predictive analytics to democratising organisational productivity tools, the technology enables traders to make smarter, faster decisions. However, sustainable usage should remain at the core. One should be aware of risks such as over-reliance on algorithms and data security. To mitigate these risks, a reasonable strategy would be to combine AI-based analytics with human market monitoring and decision-making. AI should be perceived as a convenient tool rather than a magic pill for making accurate trading decisions.

Hashtag: #Octa

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

References

1. ^{^} Media OutReach Newswire (www.media-outreach.com)
2. ^{^} PwC findings (www.pwc.com)
3. ^{^} Statista (www.statista.com)
4. ^{^} PwC (www.pwc.com)
5. ^{^} 2024 IMF Global Financial Stability Report (www.imf.org)
6. ^{^} surged from 19% to over 50% annually (www.imf.org)
7. ^{^} underscores the increasing reliance on automation (www.wto.org)
8. ^{^} more accurate (www.globaltrademag.com)
9. ^{^} avoid impulsive trades (www.imf.org)
10. ^{^} warns (www.imf.org)
11. ^{^} targeting AI are increasing by 47% (www.cobalt.io)