arXiv:2606.29734v1 Announce Type: new Abstract: Earnings announcements release two types of information sequentially: quantitative surprise (numeric earnings-per-share (EPS)/revenue versus analyst estimate) arrives first in press releases and financial news, processed by algorithmic traders within minutes; qualitative language (management tone, guidance, question-and-answer (Q&A) credibility) arrives 30-90 min later in the earnings conference call transcript (ECT), requiring human interpretation overnight. Financial economists have studied quantitative surprise for 50 years; natural language p
The proliferation of advanced NLP techniques and the increasing availability of granular financial data are making it possible to bridge the gap between quantitative and qualitative financial signals.
This research highlights the evolving sophistication of market analysis, where AI can extract actionable insights from both numerical and qualitative data, influencing trading strategies and market efficiency.
The speed and depth of financial information processing will increase significantly, potentially eroding arbitrage opportunities from human-interpreted qualitative signals and integrating them into algorithmic strategies.
- · Algorithmic traders with advanced NLP capabilities
- · Financial data analytics companies
- · AI-driven investment funds
- · Human financial analysts relying solely on qualitative insights
- · Traditional long-term investors without AI integration
Algorithmic trading will incorporate qualitative earnings signals much faster than human analysts.
The value of human interpretation of earnings calls will diminish, leading to a shift in analyst roles towards more strategic, long-term forecasting.
Market dynamics will become even more efficient, potentially reducing short-term alpha from earnings events and forcing a re-evaluation of information arbitrage.
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Read at arXiv cs.CL