How ASKB helps investors accelerate learnings during earnings season

KEY TAKEAWAYS

From preparing for earnings to understanding market reaction, this article looks at how ASKB helps investment professionals move from questions to insights using Bloomberg’s interconnected data and analytics.

As earnings season unfolds, investors face an increasingly compressed timeline to absorb new information, validate investment theses and identify the signals that matter most. Company filings, transcripts, news, consensus estimates and research all arrive in rapid succession, making it difficult to separate noise from meaningful developments.

ASKB, Bloomberg’s conversational AI interface, is designed to help clients navigate that complexity. Built on Bloomberg’s trusted data and content, ASKB enables users can move faster through earnings season by analyzing company results, creating visualizations, tracking key catalysts and market reactions while maintaining transparency into the underlying sources.

During a recent webinar how ASKB can streamline each stage of the earnings workflow using Porsche as an example.

Preparing before earnings

Preparation begins well before results are released. Rather than manually reviewing multiple documents, ASKB can summarize key corporate events, highlight strategic updates and surface the developments most likely to influence upcoming earnings.

For example, ASKB can be used to summarize Porsche’s annual general meeting, identify confirmation of full-year guidance, the company’s long-term strategy, and management’s focus on profitability. Every insight is linked back to  its original  source, allowing users to validate the information before incorporating it into their analysis.

ASKB can also surface relevant Bloomberg proprietary content like Bloomberg Intelligence research, alongside company materials, giving users additional context around Porsche’s strategy and the analysts’ investment thesis.

Validating an investment thesis

Preparing for earnings often means testing whether an investment thesis still holds. ASKB’s Thesis Health Evaluator pre-built workflow compares an existing thesis against new company announcements, financial results, news and research to identify where assumptions remain intact and where conditions may have changed.

ASKB provides pre-built workflows that assemble the information investors typically gather before earnings, including recent results, consensus expectations, company guidance, key catalysts and business drivers.

Building a comprehensive earnings preview

In this example, the team used ASKB’s pre-built “Prepare for Earnings” workflow to analyze Porsche. Ahead of earnings, investors typically review consensus across key metrics, recent news and sell-side research to understand how upcoming results may compare with their own estimates and how guidance could evolve. The workflow helps accelerate that process by bringing those inputs together into an earnings preview.

For Porsche, the workflow looked at the prior quarter’s performance, including its reported operating margin, alongside full-year guidance and next-quarter consensus estimates. It also surfaced key headwinds, including performance in China and tariffs, and highlighted the company’s upcoming capital markets day as an important catalyst.

Importantly, users can move beyond summarized insights. ASKB grounds every response in high-quality, trusted data and includes transparent attribution to original research documents and news sources,  enabling deeper analysis without leaving the conversation.

Customizing research workflows

No two investors approach research in exactly the same way. Users can start with Bloomberg-built examples or use conversational AI to create custom, reusable workflows tailored to their sector, strategy and way of working.

In this automotive example, users can  further customize default workflow to include vehicle deliveries by model, geographic sales trends and derive metrics such as average revenue per vehicle. ASKB generates additional analysis using conversational language instructions, reducing the need for manual query building.

The conversation can continue naturally, allowing users to request additional calculations, visualizations or comparisons as new questions emerge.

Understanding market reaction

On earnings day, ASKB can help explain why a stock moved by connecting price action with reported results, guidance changes, analyst commentary and peer performance.

In this example, the user can utilize a workflow that summarizes Porsche’s earnings-day share price performance, links the move to reported results and provides supporting Bloomberg Intelligence and broker research, creating a starting point for deeper investigation.

Because ASKB is also available on mobile, investorsreviewing earnings developments at their desk to picking up the conversation on their phones or tablets.

Looking ahead

Bloomberg continues to expand ASKB with new and enhanced capabilities that support a broader range of investment research workflows. Recent and ongoing developments include expanded research experiences across areas such as macroeconomics, earnings and central banks, along with greater flexibility to customize, share and automate recurring research.

By bringing together Bloomberg’s trusted data, domain expertise and conversational AI, ASKB helps investment professionals streamline research, surface relevant insights more efficiently and spend more time evaluating investment opportunities.

Request a demo to learn more about ASKB and its latest capabilities.