Artificial Intelligence (AI) has been revolutionising investment management, offering a departure from traditional models. By utilising machine learning algorithms and big data analytics, AI systems can process vast arrays of data, including news, social media sentiments, and economic indicators. This capability allows for the identification of complex patterns that would otherwise remain hidden.
Discretionary vs systematic trading
In discretionary trading, AI insights support traditional analysis methods. Asset managers utilise these signals to discover new investment opportunities and detect subtle patterns that may remain unnoticed.
This integration of AI allows them to enrich their informational arsenal, akin to having a super-analyst that processes data and identifies correlations. Essentially, AI is a sophisticated tool that augments the depth and breadth of asset managers’ market understanding, ensuring they stay ahead in a competitive landscape.
In systematic trading, instead, AI signals are foundational in developing robust, data-driven investment strategies. Asset managers can incorporate these insights to build portfolios designed to outperform benchmarks or market averages. Decisions to buy stocks predicted to excel or to sell those expected to underperform are based on advanced AI models.
See also: Sector in review: IA Commodities and Natural Resources
While the ultimate decision rests with the asset managers, the strategies are crafted around specific parameters such as investment time horizons, targeted sectors, and geographical focus. This approach ensures that each strategy is not only grounded in comprehensive AI analysis but also aligned with predefined investment criteria and constraints.
Implementing a robust process
Data is the core foundation of any AI-based predictive model. Models require extensive historical data, including stock prices, trading volumes, and non-traditional data like social media sentiment. Preprocessing this data into a clean and coherent dataset is crucial for the analysis.
The second step is identifying patterns and correlations in the datasets. AI models employ deep learning techniques, such as neural networks, to uncover complex patterns and correlations within the data. These insights might relate to the impact of economic indicators on specific sectors or correlations between media sentiment and stock prices, for instance.
See also: Robin Powell: Nobody has a retirement number
After the data preparation and analysis steps, the models have all the ingredients to generate the predictions. Quantitative models differ from traditional strategies as they are built to make numerous small bets across a broad spectrum of stocks, aiming for a collective accuracy rate better than 50%. This method spreads risk and aligns with the diversification principle in portfolio management.
Measuring success
The main goal of the AI models is to consistently make accurate predictions at scale, thereby achieving returns that surpass market benchmarks. Success is measured not by a single correct prediction but by numerous small, successful bets over time.
Predictive AI-based models offer a sophisticated approach to stock forecasting, leveraging the collective accuracy of numerous predictions. For quantitative asset managers and traders, these models present an opportunity to enhance investment strategies and generate more alpha.
The integration of AI into trading underscores a shift towards more data-driven, analytically precise methodologies in the finance sector and can complement traditional methods.
Daniele Grassi is the co-founder and CEO of Axyon AI
