AI Consulting

Revolutionizing Credit Investing and Portfolio Optimization

Dec 5, 2023

In the fiercely competitive realm of credit investing, an international investment bank embarked on a transformative journey to redefine its systematic fixed-income strategies. Our partner wanted to pivot from traditional methodologies to an AI-driven approach, aiming to conquer the complexities of today's credit markets. Strategic collaboration with Sigmoidal sought to harness AI's potential to revolutionize their investment processes.

What was the business objective?

Our partnership aimed to redefine their approach to systematic bond investing by integrating AI technology. Our partner, faced with the daunting task of sifting through vast and complex datasets, needed the bank's traditional investment methodologies revised in the fast-paced and data-driven world of credit markets. The primary challenge was extracting actionable insights to identify profitable investment opportunities and optimize portfolio management.

To address these, the bank sought Sigmoidal expertise to leverage the advanced capabilities of AI for a more dynamic and insightful investment strategy. The focus was to transform the bank's approach from a conventional, linear methodology to an AI-enabled, multipronged process. The ultimate objective was to position the bank as an innovator in credit investing, setting new benchmarks in systematic fixed-income strategies by marrying technological prowess with financial expertise.


How did we accomplish it?

Sigmoidal’s first step was revolutionizing their security selection process by strategically implementing AI and machine learning technologies. This allowed us to dive deep into expansive datasets, extracting rich insights that traditional methods could not reach - resulting in a cutting-edge bond ranking system infused with predictive analytics, providing a more accurate and dynamic market understanding.

Our next focus was on portfolio optimization. Here, AI models played a critical role, meticulously shaping investment strategies that were both robust and responsive to market dynamics. These models were critical in refining decisions around buying and selling, ensuring that every move was backed by comprehensive data analysis. This approach enhanced investment decisions and adeptly filled data gaps, paving the way for uncovering optimal trading opportunities.

We integrated Natural Language Processing (NLP) and avant-garde machine learning techniques, such as topological data analysis, to further expand the bank's research capabilities. This integration widened the scope and depth of market research, pushing the envelope in predictive analytics. By doing so, we were able to offer a broader, more nuanced perspective on market trends and investment opportunities, setting a new standard in the field of credit investment strategy and systematic fixed-income approaches.


The Results

Our collaborative venture with the international investment bank led to a significant evolution in their investment outcomes:

  • Superior Portfolio Optimization: Integrating sophisticated AI-driven optimization techniques yielded more stable portfolios and demonstrated outstanding performance metrics. This shift departed from conventional portfolio management, ushering in an era where data-driven precision and machine learning ingenuity converge to create optimized investment outcomes.
  • Effective Bond Trading: A remarkable advancement was achieved by accurately estimating real-time bond prices and liquidity data. This advancement was pivotal in enabling the bank to make more informed, timely, and practical trading decisions. By leveraging AI's predictive capabilities, the bank could navigate the complex bond market with newfound agility and accuracy, capitalizing on opportunities and mitigating risks with unprecedented efficiency.

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Technologies used

Portfolio Optimization Models: Models tailored for strategic portfolio management.

Real-Time Pricing Tools: For accurate, real-time bond pricing and liquidity assessment.

Natural Language Processing: For efficient analysis of financial texts and market trends.

Machine Learning Models: Including topological data analysis and market research.

Savings for the client


Improvement in Alpha Generation.

1280 hours

Estimated time reduction in manual analysis saved per month.

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