Papers for

investment analysts

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Aligned and unique text in earnings reports predict stock returns

Measuring What is Repeated and Novel in Earnings Disclosures Using Optimal Transport

Abstract: We introduce an optimal transport framework to decompose the textual content of earnings press releases and conference calls into aligned (shared) and unaligned (unique) components, and use these as predictors of stock returns around earnings announcements. Using over 105,000 document pairs from 2008 to 2023, we find that the aligned portions of both disclosures explain announcement-day returns with high statistical significance. The unaligned component of conference calls, which captures forward-looking content absent from press releases, provides substantial incremental explanatory power, shedding light on why conference calls are central to market reactions to earnings news. Extending to post-announcement returns, investors appear to overreact to shared press release content while underreacting to the unique information conveyed in conference calls.

Fri 2 OctComputational Engineering, Finance, and Science

The gist

Companies share financial updates through press releases and conference calls. The authors developed a method to separate shared information from unique content in these documents. They found that the shared parts strongly explain stock price changes on announcement days, while unique content in conference calls, often about future plans, also predicts stock moves. Investors tend to react too much to shared news and miss some of the unique insights from calls after announcements.

Open → 2610.04094v1

Model predicts supply chain risks impact on semiconductor finances

Predicting the Financial Impact of Supply Chain Risk for Major AI-Related Semiconductor Firms: A Heterogeneous Graph Patch Transformer Approach

Abstract: Modern semiconductor production relies on a globally distributed, multi-tier supply chain in which financial stress at one firm spreads with a delay and eventually affects the revenue, inventory, and profitability of the companies that design AI chips. Most firms see only their direct partners, and prior predictive research has mainly targeted market-based risk measures, so few tools forecast how supply chain stress will appear in reported financials. In this study, we propose a heterogeneous graph patch transformer that forecasts these quarterly changes one and two quarters ahead. Learning from a 15,186-company network over 60 quarters, the proposed model fuses quarterly fundamentals with macro-trade, event, and disaster signals through learned gates, carries risk across supplier, customer, ownership, and headquarters relations through typed, direction-specific propagation, and encodes the propagated histories with patch-based tokenization. In preliminary experiments on 116 focal semiconductor firms, the proposed model achieves the lowest error on every target at both horizons, and its profitability advantage widens at the two-quarter horizon. These forecasts can help supply chain managers and investors act before disruptions appear in reported financials.

Sat 26 SeptMachine LearningComputational Engineering, Finance, and Science

The gist

Semiconductor companies rely on complicated networks of suppliers and customers worldwide. When one company has financial problems, it can take time before these problems show up in the finances of related companies. The authors created a new kind of computer model that looks at this network and predicts how financial trouble will appear in a company's reported financial data one or two quarters later. Their model studied a large network of over 15,000 companies for 15 years and performed better than other approaches at guessing future financial changes. This tool can help managers and investors prepare for supply chain problems before they show up in official reports.

Open → 2609.32741v1