---
title: Debt Dictionaries
url: https://www.ml-quant.com/papers/repec/fip-fedpwp-103784/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-16
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:fip:fedpwp:103784
source_url: https://econpapers.repec.org/RePEc:fip:fedpwp:103784
featured: 2026-10-16
citations: unknown
topic: Asset Pricing & Factors
---


# Debt Dictionaries

Stock and bond investors extract different value signals from earnings calls, with bondholders emphasizing operations and risk while equityholders focus on growth and innovation.

- Source: https://econpapers.repec.org/RePEc:fip:fedpwp:103784
- Identifier: RePEc:fip:fedpwp:103784
- Released: 2026-10-16
- First featured: Quant Letter No. 135 (2026-10-16): https://www.ml-quant.com/issues/2026-10-16/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Asset Pricing & Factors
- Authors: Jawad Addoum, Vitaly Meursault, Justin Murfin

## Related

- [Linguistic Passiveness in Earnings Calls and Stock Returns](https://www.ml-quant.com/papers/arxiv/2610.19087/): Passive-voice fraction in earnings calls predicts temporary mispricings for good news and longer-run underperformance for bad news, independent of standard sentiment measures.
- [Measuring What is Repeated and Novel in Earnings Disclosures Using Optimal Transport](https://www.ml-quant.com/papers/arxiv/2610.04094/): Decomposing earnings press releases and conference calls into shared and unique textual components shows that unique call content predicts returns better than repeated material.
- [Exogenous Risk, Hedging Pressure, and Risk Premia in Agricultural Commodity Markets](https://www.ml-quant.com/papers/repec/ags-aaea26-404411/): Traders place 15% weight on USDA crop reports relative to private priors when forming price expectations, with this anchoring weight rising when private analyst disagreement increases.
- [Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model](https://www.ml-quant.com/papers/arxiv/2512.16251/): The Consensus-Bottleneck Asset Pricing Model uses a neural network to mimic analyst reasoning, showing how investor beliefs influence asset prices and enhancing long-term predictions for U.S. stocks.
- [Deep IV Factor Models](https://www.ml-quant.com/papers/ssrn/5283770/): The Deep Implied Volatility Factor Model, combining neural networks and linear regression, is proposed for estimating the daily Implied Volatility surface of individual stock options, improving performance around earnings announcements.
- [Search and Prices](https://www.ml-quant.com/papers/ssrn/4925143/): A new peer momentum strategy, based on firms co-searched by investors on the SEC EDGAR server, yields an annualized return alpha of 17, outperforming the shared-analyst peer momentum.