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The Digital HUB is dedicated to the digital transformation of enterprises, providing tools, resources and skills for practical applications of AI and Machine Learning. I hope that you enjoy this letter and please provide feedback and feel free to connect with me here. Subscribe to receive this newsletter with original curated articles.

AI in Financial Services – Removing Barriers

The Takeaway: Generative AI has incredible potential to disrupt every industry including financial services; yet adoption continues at a very slow pace. Consider portfolio construction in investment management as shown in the figure below. Current Portfolio construction techniques rely on inputs that carry estimation errors which could result in mismatch between expectations and actual outcomes. AI can be applied to every step of the investment workflow to enable portfolio managers with better confidence of their portfolio behavior and performance. So, why the slow pace of adoption? To address barriers, we held an event sponsored by the CFA Society Minnesota in partnership with the CFA Society Cleveland to demonstrate applications of AI and machine learning in the portfolio construction workflow. Articles below provide in depth reviews of these use cases. More details in this article and the video recording of the event here.

Credit: Chart produced by Cordell Tanny

Investment Portfolio Construction with Clustering Algorithms 

Cordell Tanny, CFA, FRM, FDP

Building a solid investment portfolio is no easy task. Traditional methods often lean on historical data to estimate future returns and how different investments behave relative to one another (correlations). However, changes in market conditions or shifts in how assets move in relation to one another can shake our portfolio’s stability, exposing it to unexpected risks. 

Machine learning methods such as hierarchical clustering can help build a portfolio that's not only grounded in understanding past behaviors but also resilient against the unpredictable ebbs and flows of the market. More here.

Large Language Models (LLMs) in Asset Management

Financial earnings reports and sentiment analysis consume a large portion of Analyst’s resources during earnings season. The deluge of text data requires fundamental teams to cover more with fewer people. We applied ChatGPT 3.5 Turbo to automate earnings reports and sentiment analysis.  This techniques provided a leverage of 20 to 1 for resources with high fidelity of results. More Here...


Practical Applications of AI in Investment Management 


The CFA Society of Minnesota in partnership with the CFA Society of Cleveland and the Digital HUB held an on-line and in-person event focused on practical applications of AI and ML in the investment management.  The Digital HUB provided subject matter experts demonstrating tools and code wrapped with a training package to get financial practitioners started in building their own investment products and services.  Read more here.


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