Neural networks, particularly large language models, 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. 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 Organisation’s focus on the digital transformation of markets underscores the increasing reliance on automation to enhance trading efficiency and liquidity.
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 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.
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%. 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.
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.
