Turning EDGAR Filings into Alpha: Comparing LLMs, Dictionary-Based Methods, and Machine Learning

CFA Society Hong Kong is pleased to launch its first Quantitative Continuing Education Lunchtime Talk of the year with a timely session on how unstructured text can be transformed into investment insight.

Corporate filings submitted through the U.S. SEC EDGAR system — including 10-K, 10-Q, and 8-K reports — contain rich information on company fundamentals, management tone, business risks, strategic developments, and market-moving events. However, converting this large volume of text into consistent, investable signals remains a major challenge for both fundamental and quantitative investors.

In this session, Yunan Liu, PhD, Director of Quantitative Research at ExtractAlpha, will discuss how different approaches can be used to engineer features and generate alpha signals from EDGAR filings. The talk will compare three major methodologies: traditional dictionary-based techniques, non-LLM machine learning models, and large language model approaches. Attendees will gain a practical understanding of how these methods differ in terms of interpretability, scalability, contextual understanding, and real-world investment applications.

A key theme of the presentation will be whether LLMs can deliver better results than conventional methods even when extracting similar types of information from the same filings. While dictionary-based methods are efficient and transparent, they may miss nuance and context. Traditional machine learning models can detect useful patterns, but often require more manual design and tuning. By contrast, LLMs may offer a more flexible and context-aware way to interpret financial language, with potential benefits for both systematic signal generation and more customized discretionary research.

The session will be relevant not only for quantitatively oriented practitioners, but also for portfolio managers, analysts, and investment professionals who want to better understand how AI and natural language processing are being applied in modern investment research. It offers a practical lens on one of the most important questions in financial data science today: can better text understanding lead to better alpha?

Remarks

Registration and Payment Policies

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• The full registration fee will be charged for no-shows or for any enrollments submitted after the deadline of 27 Apr, 2026.

Cancellation, Refunds, and Substitutions

• A non-refundable administration fee of HK$50 will be applied to any cancellation of a confirmed enrollment made on or before 27 Apr, 2026. All cancellation requests must be formally submitted in writing, and confirmation must be received via email from CFA Society Hong Kong to be considered valid.
• For cancellations received on or before 27 Apr, 2026, a refund of the event fee will be issued, deducting the HK$50 administration fee per person. For payments made via credit card, the refund process will be handled directly through the bank, with a processing period of 4 to 6 weeks. The refunded amount will be credited back to the original payment card, and participants are advised to check their statements accordingly.
• No refunds will be provided for any cancellation requests received after 27 Apr, 2026.
• Substitutions are permitted, participants must notify the Society in advance. If the substitute is not a Society member, the applicable non-member rate will be charged to reflect membership benefits.

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Other Details and Policies

• This event is recognized as eligible under the CFA Societies Global Passport Program.
• The event will be conducted exclusively in English.
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