In today's dynamic investment landscape, Generative AI, and Analytical AI have become hot topics. But how can these technologies help Investment Professionals unlock hidden Alpha?
In our latest live event, MDOTM Executives Michele Zoccali, PhD, Head of Mission Control, Tarek Moursi, Business Development Manager, and Joana Fernandes, Client Solutions Manager, explained how Analytical and Generative AI can help investment professionals generate superior Alpha.
Here are the key insights of the event.
What It Means To Have An Investment Process Enhanced By AI
The financial market landscape has transformed significantly over the past few decades. Rapid growth in data volume and complexity has reshaped the industry, making it increasingly challenging for investment professionals to identify meaningful insights. As technology evolved and data surged, financial markets grew more intricate, posing substantial challenges to institutional investors. In this intricate environment, the ability to analyse vast and diverse datasets has become essential to making more informed investment decisions and gaining a competitive edge.
AI steps into this complexity as a valuable ally. In various industries, AI has already demonstrated its effectiveness in data analysis. In healthcare, AI assists in the analysis of complex medical data, aiding in diagnosis and anticipating patient outcomes. In the automotive sector, AI processes vast amounts of data from sensors and connected cars, improving vehicle safety and optimising performance.
The investment industry, in particular, stands to benefit significantly from AI adoption. Institutional investors need to analyse enormous volumes of data daily, and AI-based tools offer a way to extract insights efficiently, providing forward-looking analysis and more accurate forecasts.
Consider the use of Neural Networks, which are AI algorithms inspired by the human brain's structure and function. They can identify relationships between different economic indicators and stock market performance, aiding in forecasting market reactions to economic changes. These are just a few examples of how AI technologies have evolved into valuable tools for professionals, particularly institutional investors, seeking to maintain a competitive edge in a complex and rapidly changing landscape.
How Sphere’s Methodology Distinguish Itself From More Traditional Approaches
Sphere combines two fundamental components that drive human decision-making: experience and data evaluation. Let's delve into how our investment model reasons, ensuring that every investment decision is made with precision.
To understand the approach that guides our investment decisions, it's important to first examine how human decision-making works. Experienced portfolio managers, in particular, rely on two key components: their past experiences and a thorough evaluation of data.
When faced with the task of making an investment decision, a portfolio manager often begins by drawing from their experience. They recollect past market phases, market dynamics, and even significant black swan events. This experiential knowledge is crucial, as it forms the basis for making educated guesses about the future.
The second component of human decision-making involves data evaluation. In this phase, portfolio managers scrutinise the data to gain a comprehensive understanding of the current market scenario. This data-driven approach is aimed at obtaining the best possible insight in regard to the financial landscape.
Artificial Intelligence also leverages both experience and data evaluation but with a distinct methodology. AI achieves this by harnessing the power of data. This approach complements the traditional/fundamental approach taken by humans. While humans excel when information is scarce and relies on a heuristic approach, AI thrives when faced with abundant information, especially when it entails connecting and analysing a vast number of data points.
Sphere’s investment methodology combines experience (Non-Chronological Learning) and data evaluation (Regime Analysis) to create probabilistic forecasts for key portfolio components, such as expected returns and var-covar matrices. It also incorporates a risk management layer into the investment methodology itself, ensuring that it's an integral part of every decision rather than an afterthought.
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Combining Personal Market Views With Sphere’s Forward-Looking Forecasts
When using Sphere, investment managers work in synergy with the technology rather than each working separately. Sphere provides relevant information so decision-makers can actively interact with it and proceed with their investment decisions. Sphere doesn’t exclude the manager’s point of view, but empowers it with investment choices assisted by AI’s inputs for a constant monitoring of risks, a dynamic analysis of market regimes, diversification between asset classes, and much more.
For example, with Sphere, investment managers can finely tune their portfolios based on their market outlook. Whether relying on their insights or Sphere's forecasted market perspective, investment managers can customise their expectations concerning the future performance of asset classes, sectors, or markets, adding a personalised touch to portfolio construction.
Through this assisted decision-making, Sphere ensures that investment professionals always remain at the centre of any investment decision. As a result, the dynamic interaction between investment professionals and the technology allows for a more efficient decision-making process, rather than if one of the two were to work on its own.
AI’s Customisation At Scale
Sphere's AI generates optimal portfolios based on the individual inputs provided by each investment manager. The remarkable adaptability of Sphere sets it apart from the rest; should market conditions change, the platform promptly adjusts portfolios to ensure they remain in line with defined custom constraints, risk parameters, and strategic targets.
Sphere provides a new simplicity and precision for portfolio construction. Its unique features empower investment professionals to effortlessly create customised portfolios tailored to their specific needs, enabling them to navigate the intricacies of financial markets with confidence and deliver value to their clients.
Sphere’s Generative AI Feature
Ask Sphere AI, which is Sphere’s Generative AI feature, serves as a ChatGPT-powered copilot, and takes on the vital role of analysing the extensive proprietary data generated and computed by Sphere regarding users' portfolios. By leveraging this amount of data, Ask Sphere AI empowers users to engage with their portfolio data in a transformative way. This innovative integration is set to redefine investment explainability within the platform, offering a unique and unparalleled experience for investment professionals.
With Ask Sphere AI, users can delve deeper into their portfolios, gaining real-time insights and answers to their queries, ultimately enhancing their understanding of their investments. The fusion of ChatGPT's natural language processing capabilities with Sphere's data-driven insights provides an exceptional feature for investment managers to interact with their portfolios in a more intuitive and user-friendly manner.
This collaboration of analytical and generative AI demonstrates Sphere's commitment to enhancing AI explainability and enabling investment professionals to make more informed decisions. Investment managers can seek real-time answers to questions and make more informed and explainable decisions.
Investment managers can now navigate the dynamic and competitive financial markets with greater precision and confidence. AI-driven technologies have redefined portfolio management by simplifying complex processes, enhancing risk management, and providing valuable insights to inform data-driven decision-making. As the investment landscape continues to evolve, the adoption of AI is not just a choice; it is an essential strategic edge for modern investment management. With Sphere, investment professionals can easily optimise their portfolios, whilst having a new level of customisability, advanced analytics, and enhanced explainability.
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