Venus_and_Mars.pdf

Transcript

1. Jawad Addoum (Cornell) Vitaly Meursault 🌹 (Philly Fed) Justin Murfin (Cornell) “Debt Dictionaries” aka “Bonds are from Venus, Stocks are from Mars” Disclaimer: The views expressed here are those of the authors, and not the Federal Reserve
2. Motivation • Financial markets are segmented • Growing consensus re: segmentation across debt and equity markets • Lead-lag pricing: ◦ Kwan (1996), Addoum and Murfin (2020) • Distinct pricing kernels: ◦ Collin-Dufresne, Goldstein, and Martin (2001) ◦ Choi and Kim (2018) ◦ Van Binsbergen, Nozawa, Schwert (2023)
3. Why are debt and equity markets segmented? Limits to arbitrage plus… • Different information? • Different preferences? We propose: same information, • but different models of inference • and associated (risk neutral?) expectations
4. Hypothesis Do they have separate “investment cultures” emphasizing distinct information in their valuations? Debt and equity investors “are from different planets!” • Stock and bond investors are segregated across and within institutions • Different training and education background (American Community Survey) • Consume different media (Baron’s vs Euromoney) • Perhaps even social networks? Even the same investor group may engage in categorical thinking, using different models for bonds and stocks
5. Hypothesis Do they have separate “investment cultures” emphasizing distinct information in their valuations? Debt and equity investors “are from different planets!” In this paper, we • adjust for payoff differences implied by contingent claims model • test for the presence of distinct inference models for the same news
6. Hypothesis Do they have separate “investment cultures” emphasizing distinct information in their valuations? Debt and equity investors “are from different planets!” In this paper, we • adjust for payoff differences implied by contingent claims model • test for the presence of distinct inference models for the same news
7. Bonds are from Venus, Stocks are from Mars!
8. Test • 𝑓! text ≡ & 𝑅! and 𝑓" text ≡ ( 𝑅" Formally, to identify investment culture, we establish a mapping of textual information to value (we call it dictionary) Examine R# and R$ (adjusted for payoff differences) around earnings calls • Information is fixed • Investor cohorts and interpretation can vary • Loosely, 𝐻% : 𝑓! text = 𝑓"(text)
9. Why text? So that Vitaly🌹 has something to do Linguistics evidence on cultural variation in word meaning (Thompson, Roberts, Lupyan, 2020) Corporate culture comes from “implicature”– a mapping between utterances and interpretation based on shared experience or norms (Gorton and Zentefis, 2018) Resulting dictionaries are observable and interpretable
10. What do we find? We estimate 𝑓! text ≡ & 𝑅! and 𝑓" text ≡ ( 𝑅" , adjusting for payoff structure Out of sample, • debt dictionary dominates stock dictionary in explaining debt returns • stock dictionary dominates debt dictionary in explaining stock returns Cultural markers include • growth, innovation, tech for stocks • crises, liquidity, operations for debt
11. Asset dictionaries or investor dictionaries? • Culture hypothesis: investor cohorts possess different models • Payoff hypothesis: models are asset and payoff-specific Idealized experiment: Would a bond investor use a stock dictionary to value a stock? • No. Dictionary differences are not due to payoff differences. • For equity-like bonds (e.g. convex payoffs), bond investors use the same debt dictionary Contributions: • Text interpretation (sentiment) is market specific • Investor cultures can explain market segmentation
12. Methodology • We use three NLP models to estimate dictionaries • ...and three different stock-bond return pairs to account for scaling issues • Scaling to account for different “deltas” • Absolute values to account for “vegas” ◦ Absolute returns also more natural to identify dog-whistle topics as opposed to positive/negative language ◦ e.g. unlikely to see signed differences in news about “margins” Goal: estimate text to 𝑅 mappings after removing artifacts of payoff differences
13. Methodology– delta scaling 1. Scaled 𝑅 divided by average 𝑅 in the prior month: 𝑓#$%&'( ! text ≡ ( |𝑅#$%&'( ) | and 𝑓#$%&'( " text ≡ ( |𝑅#$%&'( " | Interpretation: how do equity- and debt-holders perceive return to firm value? 2. Model delevered equity returns vs raw bond returns 𝑓(&'*! ! text ≡ ( |𝑅(&'*! ) | and 𝑓" text ≡ ( |𝑅"| 3. Regression delevered equity returns vs raw bond returns 𝑓(&'*" ! text ≡ ( |𝑅(&'*" ) | and 𝑓" text ≡ ( |𝑅"| Multiply equity by a Merton model-implied hedge ratio (𝑑𝑅! /𝑑𝑅" via changes in the enterprise value of the firm) Multiply equity by a regression-implied hedge ratio (𝑑𝑅! /𝑑𝑅" suggested by leverage decile regressions) Interpretation: how do equity- and debt-holders perceive return to firm value claimed by bonds?
14. Data summary
15. NLP methodology Similarly, three NLP models • Bag of words + ridge regression • Multinomial Inverse regression • GTE embeddings + ridge regression Main specification: ensemble model combining three methods 🌹 All three models are consistent about basic facts
16. Hypothesis development: Dictionary difference Estimate 𝑓! text and 𝑓" text in-sample , (1) and (2) out-of-sample • 𝑅+ ! = 𝛽, 𝑓! text- + 𝛽. 𝑓" text+ + 𝜖+ • 𝑅+ " = 𝛾, 𝑓! text- + 𝛾. 𝑓" text+ + 𝜐+ Investor culture predictions: • 𝛽0 and 𝛾1 ≥ 0 • 𝛽1 and 𝛾0 ≈ 0
17. Explanatory power of stock and bond dictionaries (scaled |RS| and |RB|)
18. Explanatory power of stock and bond dictionaries (scaled |RS| and |RB|) • Unsurprisingly, cross-asset predictability evident, but... • We need a debt dictionary to explain bond response to new information, controlling for stock-based interpretation
19. Explanatory power of stock and bond dictionaries (model-adjusted |RS|, raw |RB|)
20. Explanatory power of stock and bond dictionaries (model-adjusted |RS|, raw |RB|) Results are robust to Merton model adjustment
21. Explanatory power of stock and bond dictionaries (regression-adjusted |RS|, raw |RB|) Results are robust to regression adjustment
22. Fixed effects and SUE • Difference not driven by firm FE (e.g. firms with volatile bonds talk about oil more) • Text matters, not headline number
23. Bonds are from Venus, Stocks are from Mars! Takeaway so far:
24. Payoffs… … or culture?
25. Payoffs or culture? If payoff differences drive dictionary differences, dictionary differences should grow with payoff differences Payoff differences are higher • for firms further from default • for firms with lower hedge ratios • for firms with higher |vega| (sensitivity to 𝜎/ ) Split sample by these cuts, and look at 𝛽, and 𝛾. • 𝑅+ ! = 𝛽, 𝑓! text- + 𝛽. 𝑓" text+ + 𝜖+ • 𝑅+ " = 𝛾, 𝑓! text- + 𝛾. 𝑓" text+ + 𝜐+
26. Payoffs or culture? Far from default bond payoffs respond to different news than stock payoffs Close to default bond payoffs become responsive to equity payoff-relevant news If our dictionaries are payoff-based, 𝛾# /𝛽$ should be the highest here If our dictionaries are payoff-based, 𝛾# /𝛽$ should be the lowest here
27. Payoffs or culture? • Right-to-left, debt becomes more like equity (closer to default) • If anything, dictionary differences are more pronounced for equity-like bonds! Far from default bond payoffs respond to different news than stock payoffs Close to default bond payoffs become responsive to equity payoff-relevant news If our dictionaries are payoff-based, 𝛾# /𝛽$ should be the highest here If our dictionaries are payoff-based, 𝛾# /𝛽$ should be the lowest here
28. Payoffs or culture? Highter HR firm bonds are more equity-like (have more similar sensitivity to firm value) If payoffs determine dictionaries, HR Q1 𝛾# /𝛽$ should be larger than HR Q4 𝛾# /𝛽$ The evidence is opposite to what the payoff story suggests
29. Payoffs or culture? Highter |vega| firm bonds are more equity-like (respond to asset volatility more similarly) If payoffs determine dictionaries, Vega Q1 𝛾# /𝛽$ should be larger than Vega Q4 𝛾# /𝛽$ The evidence is inconsistent with payoff story
30. Junior bonds • Getting separation from delta/hedge ratio and vega is difficult in the wild • Proposed natural experiment: distressed firms with junior bonds • Estimate 𝑉 / , 𝜎/ and 𝑑𝑉0123
31. Junior bonds • Example: MGM Resorts (fka MGM Mirage), 2009 • Stock loses 98% of value (to $1.89) • Junior-most debt falls to $12
32. Junior bonds Equity-like “concave up” bonds are priced with bond dictionaries
33. Payoffs… … or culture? No! Yes!
34. NLP insights
35. What does bond culture care about? • Scattertext – plot words important to bonds, but not stocks and vice-versa • Structural topic model (STM) – find topics associated with a covariate (reactions by bonds by not stocks) • LLM to summarize topics
36. What does bond culture care about? (Scattertext) First, for each doc 𝑖, calculate: ExtremeReaction+ = 1 Stocks react, bonds don4t , if 𝑃5.75 Δ+ 1 < Δ+ 1 −1 Bonds react, stocks don4t , if 𝑃5.75 Δ+ 1 < Δ+ 1 0, otherwise where: • Δ % & = 𝑓' text − 𝑓( 𝑡𝑒𝑥𝑡 • 𝑓' text ≡ 1 𝑅)*+,-. / • 𝑓( text ≡ 1 𝑅)*+,-. ( • 𝑃0 Δ % & : p-th percentile of Δ % & within FF12 industry 𝑗 Second, plot word frequency rank in each ExtremeReaction corpus
37. Scattertext
38. Scattertext: Bond words
39. Scattertext: Stock words
40. Structural Topic Modeling + LLM • Topic models estimate k topics (probabilities over words) from which documents are built • Structural topic modeling allows covariance of topics with ExtremeReaction ◦ Imbues analysis with opportunity for statistical inference • We identify 100 topics, alongside top words • Of 100, 82 are (statistically) significantly more related to one or the other corpus
41. Structural Topic Modeling + LLM • We then give LLM the top 10 words for each topic • ...and which ExtremeReaction group the topic was associated with (”1” or ”2”) • ...and 10 themes (Technology and innovation, Profitability, Operations, ...) Ask LLM: What do topics represent thematically, and which themes are most important to each group? ~10,000 unique words -> 100 topics -> 10 themes
42. What drives investment cultures? (STM)
43. What drives investment cultures? (LLM summary) “Group 1 [stocks] covers dimensions related to Growth, Technology and innovation, Profitability/margins/earnings, Projections/forecasts/estimates, and Geography more extensively.” “Group 2 [bonds] covers dimensions related to Inputs/raw materials, Operations/production, Crises/economic challenges, Debt and liquidity, Macroeconomic factors, and Sectoral topics more extensively.”
44. NLP insights Conclusion
45. Conclusion • Bonds and stocks have different dictionaries • Response to information may be cohort specific • Consistent with culture? • Can this explain segmentation, variation in factors across markets? • Mechanism: debt topics resonate with payoff story. Do payoffs cause culture?
46. Fin! Thank you!
47. Fin! Thank you! @oldmadegood