Leveraging Analytical & Generative AI in Modern Portfolio Construction

KEY TAKEAWAYS:
SKIP TO SECTION:

Time Stamps:
00:00 Introduction
5:10 Why AI Today
13:48 The Stages Of AI Maturity in Investments
17:34 The technology behind Analytical & Generative AI
29:25 The value of combining the two in investments
35:20 Sphere walkthrough: ChatGPT Commentary & Ask Sphere
52:46 How to start your AI journey
57:35 Q&A

Download the PDF Summary of the event here.

In today's dynamic investment landscape, AI, Generative AI, and Analytical AI have become hot topics. But how can these cutting-edge technologies empower Investment Professionals to confidently navigate complex financial markets and make better-informed decisions?

In our latest live event, MDOTM Executives Tommaso Migliore, CEO & Co-Founder, Axel Maier, CFA, Partner, Joana Fernandes, Client Solutions Managers and Lorenzo Malanga, VP of Research, explained the role Analytical and Generative AI has in modern portfolio construction.

Here are the key insights of the event.

The Role of Generative and Analytical AI in Investment Decision-Making

In investments, two distinct yet complementary AI branches have emerged: analytical AI and generative AI. To understand their significance in investment decision-making, it's essential to grasp their fundamental differences and how they are leveraged in order to guide investment decisions.

Analytical AI: Uncovering Valuable Insights

Analytical AI is the technology that harnesses Machine Learning techniques to delve into data, uncover hidden insights, identify novel patterns, and establish relationships within datasets. This form of AI operates on principles deeply rooted in statistics and mathematics. Some common methodologies employed by analytical AI include Linear Regression, Neural Networks, Decision Trees, and Random Forests.

Analytical AI scrutinises vast volumes of historical and real-time data, aiming to extract meaningful information. In the financial landscape, the role of analytical AI can be exemplified by MDOTM's investment platform, Sphere. Sphere, with its analytical capabilities, meticulously analyses millions of historical, market, and macro data points. This thorough and advanced analysis unveils hidden signals within the noise of data, so then investment professionals can guide accordingly their investment decisions.

Generative AI: Creating Completely New Output

Generative AI has a completely different architecture than analytical AI. Generative AI models learn the patterns and structure of their input data, and then generate completely new data that has similar characteristics. Generative AI also facilitates an ongoing human interaction with the technology to produce novel outputs. Generative AI thrives on the power of creativity and imagination, and it often operates in response to specific prompts or requests provided by humans. Unlike analytical AI, which sifts through existing data to uncover valuable insights, generative AI generates entirely new content based on the input it receives. 

For example, consider Midjourney, an AI-powered image generation tool. Users interact with Midjourney by providing textual or visual prompts, such as a description of the image they wish to see or even an existing image as a reference. Generative AI like Midjourney then creates a unique image based solely on the user's input. 

Applying Generative and Analytical AI to Investment Decision-Making

Analytical AI excels at sifting through vast datasets, historical market data and economic indicators to identify trends and correlations that humans might traditionally overlook. It provides investment professionals with data-driven insights, risk assessments, and portfolio optimisation strategies. By automating complex data analysis, it frees up human experts to focus on higher-level strategic decisions.

Generative AI plays an important role in ensuring AI explainability in investments. It accomplishes this by generating comprehensive and clear explanations for the analytical output of AI. When investment professionals or clients receive AI recommendations, generative AI can provide detailed reports that break down the rationale behind each recommendation. These explanations shed light on why certain investment choices are made, helping to understand the technology’s reasoning, and ensuring AI explainability.

The Nexus Between Generative and Analytical AI

In investment management, the relationship between generative and analytical AI emerges as a powerful tool, enhancing the way investment decisions can be made and understood.

Analytical AI, with its proficiency in data analysis, serves as the foundation of this partnership. It's the engine that processes vast datasets, examining historical market data, and economic indicators. Analytical AI has the capacity to uncover intricate trends, correlations, and patterns. It provides investment professionals with invaluable data-driven insights, risk assessments, and strategies for optimising investment portfolios.

Generative AI, on the other hand, plays a role in ensuring transparency and explainability in investment decisions. It works with human interaction and operates in response to specific prompts or queries. This aspect of generative AI is particularly significant when applied to investments, as it bridges the gap between the analytical output of AI and the human understanding of the technology’s reasoning.

The synergy between these two forms of AI is most evident with regards to AI explainability. Investment professionals often seek to understand the rationale behind AI-generated investment inputs. While analytical AI can pinpoint trends and suggest portfolio adjustments, generative AI steps in to provide comprehensive explanations. When an investment decision is made based on analytical AI's insights, generative AI can create detailed reports that explain the reasoning behind each recommendation. These reports serve as a critical link between the intricate analyses performed by analytical AI and the decision-makers involved. They break down complex data into understandable narratives, allowing investment professionals and clients to grasp why particular investment insights have been provided.

For instance, if analytical AI recommends a shift in portfolio allocation, generative AI can produce an explanation outlining the underlying factors, such as recent market developments, economic indicators, or company performance metrics. This interpretation empowers investment professionals to make informed decisions and communicate the rationale behind their choices effectively.

The nexus between generative and analytical AI is reshaping the modern approach towards investment management. Analytical AI's data-driven capabilities are harnessed to uncover hidden insights and optimise portfolios, while generative AI ensures these insights are translated into clear and comprehensible explanations. Together, they empower investment professionals with a data-driven precision and human-centric understanding.

Download the PDF Summary of the event here.

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