Hedge fund analysts are constantly bombarded with overwhelming amounts of unstructured data—SEC filings, sell-side reports, brokerage analyses, and alternative data—making it challenging to efficiently extract meaningful insight. This is especially true during earnings season when hedge fund analysts work overtime just to keep up. Additionally, while tools like Alphasense and ChatGPT by OpenAI, show promise in processing thousands of pages of information, they fall short in handling unstructured data often producing hallucinations. Hallucinations manifest in various ways, but in the financial world, they primarily occurs in two forms. The first involves incorrect numerical calculations, while the second entails fabricating information that isn’t based on actual data or sources. In this post, we’ll discuss our approach to handling numerical hallucinations. In our next post, we’ll delve into our advanced RAG architecture and how we address the hallucination of fabricated information.

Numerical Hallucinations: What They Are and How They Occur

Numerical hallucinations happen in LLMs (Large Language Models) because the models are trained on vast amounts of text data that can include mistakes, outdated information, and inconsistencies. These models are designed to recognize patterns in language rather than ensure precise numbers, and lack real-time access to current data. Additionally, they might misunderstand the context of numerical data or confuse different financial metrics, thus affecting the rest of the analysis. This combination of factors leads to the generation of incorrect or fabricated numbers, known as numerical hallucinations. Such numerical inaccuracy in generated responses, like summaries, are detrimental to the analysts who rely on them for high-stakes decision-making.

This is the problem that we address.

The Linq Alpha’s Advantage: Integrating Probabilistic and Deterministic Models

A frequent question we get is, “Why is Linq Alpha more accurate than other services like AlphaSense or ChatGPT by OpenAI?” Simply put, our technology employs both probabilistic and deterministic models in tandem to leverage their strengths, combat hallucinations, and provide accurate generations.

Probabilistic models, like LLMs, excel in handling unstructured data and natural language queries. They offer flexibility and adaptability, making them ideal for analyzing data such as earnings transcripts and SEC filings. However, when processing data with heavily numerical content, probabilistic models can sometimes introduce a level of randomness in their output. To address this, each query we receive undergoes a multi-layered calculation and retrieval process. Our deterministic model ensures that financial performance metrics, such as the calculation of adjusted EBITDA, Diluted EPS, total P&L (profit and loss) and its segmented breakdown are computed with precision, avoiding numerical inaccuracies. In essence, a financial query is initially processed by probabilistic models to handle its unstructured data and followed by deterministic models for accurate calculations and data retrieval. This combined approach results in a comprehensive and reliable answer, depicted as Linq Alpha.

Real-World Implication

In practice, Linq Alpha significantly reduces the workload for hedge fund analysts by accurately extracting and calculating key financial performance metrics from earnings transcripts, ensuring reliable insights. For example, our solution can engage in complex financial modeling and handle multiple variables while calculating for Economic Value Added (EVA), Cash Conversion Cycle (CCC), and etc. Where probabilistic models may fall short, Linq Alpha's deterministic model meticulously handles advanced calculations and ensures the generated financial output are robust and reliable.

Conclusion

Linq Alpha provides highly accurate LLM-based financial analysis and summary; eliminating numerical hallucinations and transforming data interpretation into actionable insight. At Linq, we are committed to pushing the boundaries of AI in finance. Leveraging probabilistic and deterministic models we can provide a tailored solution for hedge fund analysts and most importantly- answers that matter.

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