AI is used in investing to analyse extensive datasets, and extract the valuable signals hidden in its noise, which investment managers can then use to guide their decisions. The use of AI in the investment process has become widespread, and has helped numerous institutional investors across the globe navigate market complexity.
No, they are not. Even though AI and ML are often used interchangeably, ML is a subset of the broader category of AI. To put it simply, AI refers to the simulation of human intelligence processes by machines, while Machine Learning is a subset of AI, referring to the technologies and algorithms that enable systems to make decisions, and improve themselves through experience and data.
The adoption of AI in the investment process provides institutional investors with an unbiased and independent view which is complementary to their own. As AI has the computational power that a human mind lacks, by analysing extensive data and detecting relevant information, it can provide a holistic and impartial perspective for any investment decision.
In contrast to traditional quantitative models that are explicitly programmed to perform a sequence of consecutive tasks, AI models are designed to learn from vast sets of data and to thereafter find a solution. This makes them suited to solve problems which are not only complex by nature, but involve high-dimensional datasets and evolving dynamics, like financial markets.
Sphere does not require institutional investors to have any experience with AI. Its friendly interface allows any client to create or upload a portfolio, setting their custom objectives, risks and constraints. The outcome consists of timely AI-driven market views, asset allocation suggestions, and portfolio rebalances that will support and enhance any investment decision.
At MDOTM, AI is deployed at every stage of the investment process: from research and hypothesis testing, to the generation of the investment signals and market views, until the creation of investable portfolios. In Sphere, AI is used both in the market outlook, as well as in the portfolio creation and rebalancing tool.
Sphere gives its users an edge in investing. It allows investment professionals to have additional and unbiased inputs that simplify and empower their work. The dynamic interaction between investment managers and technology makes the decision-making process frictionless. Our White Paper gives a thorough overview of the use of Sphere in the investment process.
Clients can choose to create a totally new portfolio with Sphere or upload an already existing one. In both cases, the insights produced are fully tailored to respect the client's objectives and constraints, and asset allocation enhancement changes are suggested considering the alignment of the client’s portfolio to what the model considers to be optimal.
Yes. Type of portfolio, base currency, benchmark, strategy time horizon, asset allocation constraints, weights, and geographical preferences are just a few of the characteristics that can be personalised with Sphere. If you’d like to learn more in detail about how Sphere can be tailored to your specific needs, do not hesitate to book a demo with our experts.
No. However, in a time of great complexity in financial markets, investment managers can leverage Sphere to extrapolate value from data and make more informed decisions. When using Sphere, investment managers work in synergy with the technology. As a result, Sphere empowers investment managers by turning signals into actionable insights and effectively enhancing existing portfolios.