In this article, you will learn how Artificial Intelligence (AI) is transforming asset management by offering a powerful tool for investment decision-making. AI excels in rapidly evolving financial markets by processing real-time data and adapting to market conditions. By effectively separating valuable insights from noise, AI empowers investment managers to optimise portfolios with a competitive edge. From customising portfolios to considering risk profiles and adapting to market changes, AI seamlessly enhances the investment process, ensuring informed, efficient decision-making in the dynamic world of investments.
Benefits of AI-Driven Portfolio Enhancement
Artificial Intelligence (AI) is revolutionising the world of asset management. As financial markets become increasingly complex, Asset and Wealth managers are turning to AI to guide their investment decisions. From analysing market regimes to constructing portfolios, AI is proving to be a powerful tool that enhances the efficiency and effectiveness of investment strategies. In many ways, AI can be considered a game-changer in asset management, just as it has transformed various other industries. By enabling more efficient data processing and enhancing our capacity to comprehend and adapt to the complexities of the real world, AI has found its way into the modern toolset of the financial sector.
In the ever-shifting landscape of financial markets, the ability to adapt to changes in market conditions is a priceless asset. Traditional quantitative approaches often struggle with the rapid evolution of financial markets. These approaches are constrained by their inability to comprehensively analyse and adapt to the intricacies of the real world.
AI, on the other hand, thrives in the dynamic environment of financial markets. Its capacity to process vast amounts of data in real time and identify patterns and trends allows it to adapt to changing market conditions with agility and precision. This adaptability ensures that investment strategies remain relevant and effective even in the face of rapidly evolving market dynamics.
Financial markets are characterised by a high degree of complexity and constant evolution. Effectively separating the signal (useful information) from the noise (non-useful information) is essential for modelling and controlling interactions among the variables involved and obtaining valuable insights. Employing the scientific method, with an understanding of financial market dynamics and their economic and behavioural rationale, allows us to anticipate how financial markets will respond to various circumstances and maintain control over their evolution.
With a holistic understanding of market dynamics, AI empowers asset and portfolio managers to make informed investment decisions, reducing the risk of unexpected events derailing their strategies. AI-driven models perform the work of thousands of analysts, making it possible to chase the statistical relevance of investable signals hidden within data. By gaining control over market dynamics through AI, asset and portfolio managers can develop strategies that transform collected information into profitable and resilient portfolios, enabling them to navigate market complexity successfully.
With its ability to identify the signals within the noise of data, and provide a comprehensive understanding of market dynamics, AI is the technology that is powering asset managers to gain a competitive advantage in this evolving landscape.
How AI’s Features Can Be Seamlessly Integrated Into The Investment Process
As financial markets become increasingly intricate and competitive, investment managers are turning to AI to enhance their portfolio construction process. Sphere's AI is designed to understand the intricate dynamics of financial markets, providing investment professionals with invaluable insights and strategies tailored to its robust features. It's no longer a matter of "if" AI should be incorporated; it's a question of "how" AI tools can help deliver streamlined, efficient portfolio construction.
Sphere simplifies each step of the portfolio construction process with its unique capabilities, ensuring that investment managers can easily create customised portfolios. Let's delve into the steps involved in this seamless portfolio construction process with Sphere:
Base Currency and Benchmark Selection:
Sphere offers investment managers the ease of choosing their base currency and preferred benchmark with a few clicks. This flexibility enables investment managers to align portfolios with their specific requirements, making it easier to achieve investment objectives. The extensive range of MDOTM universes, covering various asset classes like indices, funds, ETFs, and single stocks, empowers investment managers to construct portfolios that precisely suit their needs. Sphere seamlessly integrates into an existing investment process by providing investment managers with the flexibility to set their base currency and preferred benchmark. This ensures that portfolios are constructed in line with the manager's investment strategy and objectives.
Defining the Investable Universe:
Sphere takes customisation to the next level, allowing investment managers to define their investable universe effortlessly. Whether opting for an existing universe or creating a custom one, Sphere's intuitive interface ensures that investment managers can work with the exact assets they want. This feature ensures a seamless fit into the investment process as it streamlines the process of selecting assets and adds a layer of adaptability to existing practices.
Portfolio Constraints at Your Fingertips:
With Sphere, setting portfolio constraints is a seamless process, aligning portfolios with the chosen strategy. Investment managers can include asset allocation constraints, specify minimum or maximum weights for individual instruments, or add unique instrument constraints, ensuring that portfolios adhere to predefined rules and objectives. This level of control enhances risk management and strategy execution. This feature directly integrates with the investment process by allowing investment managers to set rules and objectives for their portfolios. These constraints help ensure that the portfolio aligns with the chosen strategy and risk management parameters, seamlessly enhancing the investment process and contributing to portfolio construction efficiency.
Setting Risk Constraints:
With Sphere, investment managers can establish risk constraints, such as value-at-risk and other risk measures, to ensure that portfolios align perfectly with the desired risk profile. This risk-aware approach empowers investment managers to create portfolios that balance returns and risk effectively. The feature fits seamlessly into the investment process by enabling investment managers to establish specific risk parameters for their portfolios.
Implementing Portfolio Strategy:
Sphere empowers investment managers to set precise portfolio targets, representing their desired objectives and allocations for specific assets, sectors, or other investment categories within the portfolio. Sphere optimises portfolios to meet these targets while considering additional constraints, such as tracking errors. This strategic flexibility ensures that investment managers can effortlessly tailor portfolios to their unique investment goals. Sphere's portfolio strategy implementation feature directly integrates into the investment process by allowing investment managers to set specific objectives and allocations for assets within the portfolio. This integration enhances the precision and customisation of portfolio construction, aligning the portfolios with the manager's investment goals within the existing investment framework.
Shaping Your Market View:
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, managers can customise their expectations concerning the future performance of asset classes, sectors, or markets, adding a personalised touch to portfolio construction. Sphere's market view feature seamlessly integrates into the investment process by allowing investment managers to adjust portfolio construction based on their market outlook. This feature enhances the adaptability and customisation of portfolios, aligning them with the manager's market perspective within the existing investment process.
Adaptive Portfolio Construction:
In the final step, investment managers can combine all their inputs by simply clicking "create portfolio.” Sphere's AI generates optimal portfolios based on these inputs. 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 constraints, risk parameters, and strategic targets. Sphere's adaptive portfolio construction feature smoothly integrates into the investment process by providing investment managers with the ability to generate optimal portfolios based on their inputs.
Ensuring A Seamless AI Integration
Over the past few years, the adoption of Artificial Intelligence into institutional investors' investment processes has proved to be successful in increasing investment results and operational efficiency. The remarkable application of such technology in this field has then grabbed substantial attention, particularly from large financial institutions, to the point that Forrester – the world's leading strategic consultancy firm – estimates that by 2025 an estimated $64 billion will be spent by large organisations to buy AI software and platforms.
However, there’s no such thing as two financial institutions sharing a similar investment process. Thousands of nuances make decision-making and portfolio construction processes unique for each firm and so require an AI adoption journey to be carefully crafted to achieve a specific set of investment objectives, risk budgets, and constraints. That is why seamlessly integrating AI into the investment process – both at a product or process level – requires agreeing on some ground truths and standard best practices.
In this article, we share a common framework that Wealth or Asset Managers can use to seamlessly integrate this powerful technology into their investment process, answering the most burning questions we collected from our clients that everyday use it to support their investment decisions.
The 3 Principles of AI Adoption in Investments
At MDOTM, we have been supporting institutional investors with AI-driven investment solutions since 2015. During our decade-long experience, our team of investment data scientists and experts have been challenged with one of the most complex tasks: developing reliable AI for institutional investors, banks, insurance companies, and family offices. We successfully did it by creating Sphere, our AI investment platform that is used and trusted by hundreds of investment professionals at a global level.
However, integrating this technology into the investment process is the tip of the iceberg, as it often forms an integral part of a journey. This process frequently starts by recognising that there is an untapped potential for large organisations to improve processes with Artificial Intelligence. What follows are the best practices we learned from supporting dozens of investment professionals and large financial institutions integrating AI-driven investment solutions.
Principle #1: Customise & Integrate
Unlike decades ago, today, we have access to No-Code AI tools that have been progressively replacing complex methods. In that case, we finally have the chance to develop tools that adapt to how investment professionals think and to their way of getting things done – and not the other way around.
AI-powered investment tools are flexible and allow investors to combine their views and objectives with forward-looking insights. By doing so, investment professionals can tailor their investment decisions to their specific needs and preferences, and more importantly, be aware of the current market regime. In this sense, full customisation and integration are key for successful AI adoption, especially when we think about mission-critical activities like supporting the investment committee’s Strategic Asset Allocation or developing a new investment product, like a discretionary mandate, fund, or certificate. This pillar emphasises the importance of technology adapting to you, rather than expecting you to adapt to it, fostering a more human-centric and tailored experience.
Principle #2: Leverage internal and external capabilities
Over the last decade, financial markets’ complexity has driven the asset and wealth management industry to shift from vertical to horizontal integration. In this light, the double-digit rise in open innovation partnerships is evidence that more than ever, innovation is frequently obtained by mixing up (and compounding) different know-how, and not every company has the necessary resources – or strategic incentive – to undertake such an initiative. For this reason, Open Innovation partnerships with external AI specialists and data providers can be a game-changer.
By leveraging the expertise of both internal and external experts, companies can ensure that their AI-powered investment tools are of the highest quality and are constantly being updated with the latest market trends and data.
Principle #3: Having the right mentality
Having the right mentality is crucial when it comes to AI adoption: technology is not meant to replace humans but rather to empower them. Having the right mindset enables individuals and organisations to leverage their capabilities as a tool to enhance human decision-making, efficiency, and productivity. By recognising the potential of AI as a complementary technology, investment professionals can embrace collaboration between humans and machines, leading to innovative solutions, improved outcomes, and the exploration of new opportunities. The right mentality fosters a harmonious integration of AI into various processes, harnessing its power while keeping humans at the forefront of decision-making and strategic thinking.
What are the key steps for AI Adoption in Asset Management?
Even though the final output of AI’s Adoption may differ from one institution to another according to each one’s objectives and requirements, many common traits serve as a baseline to assess where your institution is in this journey.
Step #1: Exploration Phase - During the Exploration Phase, financial institution’s decision-makers typically investigate and explore the benefits of AI applied to a specific part of their process, identifying the areas where AI can provide valuable insights and improvements.
Step #2: Partner Selection - As a result of the Exploration Phase, clients typically end up with a range of potential solution providers and then evaluate, based on a specific set of rigid criteria, whether their solution can be applied to their existing framework and how reliable it is.
Step #3: Investment Process Support - At this point, investment professionals need to analyse and break down each step of their existing investment process. This examination helps them identify specific areas where AI can provide valuable support, such as automation, data analysis, or pattern recognition. By pinpointing these opportunities, they can strategically integrate AI to enhance decision-making.
Step #4: Training of the Models - To align AI with investors’ unique requirements, it’s pivotal to train AI models to fit in their investment process precisely. This step involves feeding the models with relevant historical data and refining them to provide accurate insights. Tailoring the models to specific investment objectives and risk tolerance ensures optimal performance.
Step #5: AI Integration - This step involves combining the insights generated by AI models with the expertise and perspectives of your asset management team. This integration allows asset managers to leverage the capabilities of this technology while maintaining human judgment, ultimately enhancing the decision-making process. Of course, after such integration, continuous monitoring and optimisation are essential for successful AI adoption. Regularly assess the performance and effectiveness of the integrated system and make adjustments and improvements as needed to maximise the value and benefits derived from AI.
How You Can Get Started with AI Adoption Today
The integration of Artificial Intelligence in asset managers' investment processes holds immense potential for enhancing operational efficiency and improving outcomes. By customising AI to specific needs, leveraging internal and external capabilities, adopting the right mentality, and following a structured adoption process, asset managers can easily start integrating this technology into their processes, without the need for IT or Operations teams. Embracing these best practices will enable any investment professional to outperform their competitors and deliver greater value to their clients.
However, to start the whole journey, it’s crucial to find the visionary innovator within the organisation, the person who is willing to challenge the status quo and pave the way for a positive and impactful change in the daily workflow.