1{2 "protocol": "heldout_v1",3 "built": {4 "timestamp": "2026-11-03T23:27:49+00:00",5 "code_version": "87c73cd-dirty",6 "host": {7 "platform": "macOS-27.0-arm64-arm-64bit",8 "python": "3.12.13",9 "machine": "arm64",10 "node": "MacBookPro-de-simon-pierre-2.local"11 }12 },13 "finance_docs": [14 {15 "doc_id": "jof_12439",16 "title": "Financial Contracting and Organizational Form: Evidence from the Regulation of Trade Credit",17 "doi": "10.1111/jofi.12439",18 "year": 2017.0,19 "n_tables": 6,20 "n_refs": 40,21 "degraded_literature": "environment affects the boundary of the firm and the scope for trade with external parties (e.g., Coase (1937), Williamson (1973), Grossman and Hart (1986)). Empirical evidence of a link between the contracting environment and the organizational form of the firm and its supply chain has been largely limited to observational case studies or industrylevel analyses (Bresnahan and Levin (2013), Lafontaine and Slade (2013)). In line with this view, Murfin and Njoroge (2015) document that the smallest decile of Compustat firms use by far the most trade credit. However, it is not uncommon to also observe credit flowing from small, constrained suppliers to large corporations with access to international capital markets.3 Indeed, Murfin and Njoroge (2015) document that firms in the top two size deciles are also net trade credit borrowers. This result suggests that the vertical integration stemming from the Agreement is costly (consistent with Baker, Gibbons, and Murphy (2001)).8 Many relational contracting models (e.g., Baker, Gibbons, and Murphy (2002)) predict that relationships are more resilient (i.e., can be more easily sustained by the threat of termination) when they are more exclusive, in the sense that the outside options of the parties are low. Consistent with this idea, we find that the negative effects of the Agreement on the likelihood of observing trade are significantly mitigated for suppliers that have more exclusive relationships with the Superstore, that is, suppliers that sell mostly to the Superstore, and for suppliers that have a large product market share (both of which we measure using prereform data).9 In these cases, the relationship is valuable for the supplier and the Superstore, respectively (as in McMillan and Woodruff (1999) and Giannetti, Burkart, and Ellingsen (2011)). Finally, we argue that in our setting, the evidence is most consistent with models of trade credit in which suppliers use long-maturity terms to guarantee product quality (Smith (1987), Long, Malitz, and Ravid (1993), and Kim and Shin (2012)). Related empirical papers include Baker and Hubbard (2004), who study how the introduction of a monitoring technology influences the decision to vertically integrate, Fresard, Hoberg, and Phillips (2014) and Seru (2014), who link vertical integration to innovation, and Chen et al. (2013), who investigate organizational form in the mutual fund industry. Acemoglu, Johnson, and Mitton (2009) and Macchiavello (2012) examine the relationship between the contracting environment and vertical integration in a large cross section of firms in different countries. Barrot (2016) examines the effects of a similar regulation that reduced the maturity of trade credit extended by French trucking firms. Similarly, Murfin and Njoroge (2015) show that financially constrained firms reduce investment when forced to extend longer maturity trade credit. These stark differences in policy outcomes likely stem from differences in the industrial organization of the trucking and discount retail industries and from the relative costs of vertical integration.11 The demand for French trucking services is highly competitive and largely inelastic (Barrot (2016)), while the Chilean discount retail sector is highly concentrated with more elastic demand.",22 "published_intro_words": 1619,23 "sha256": "16a5dc8018daa19a4073dbd046dc496777080d561ce5fd9913d6d6da5bc5bb6d"24 },25 {26 "doc_id": "jof_12777",27 "title": "Limited Investment Capital and Credit Spreads",28 "doi": "10.1111/jofi.12777",29 "year": 2019.0,30 "n_tables": 8,31 "n_refs": 39,32 "degraded_literature": "In turn, specialized trading desks are allocated a pool of capital to finance trading activity, but this capital is not easily replenished on short notice due, for example, to agency problems within the firm (He and Xiong (2013)). The spirit of this idea is at the heart of theories of limits to arbitrage (Shleifer and Vishny (1997), Kyle and Xiong (2001)), slow moving capital (Duffie (2010)), and financial intermediary-based asset pricing (He and Krishnamurthy (2013)). In the absence of such data, much of the existing work in the field has linked high-level measures of capital, like intermediary leverage, to asset price dynamics (Adrian, Etula, and Muir (2014), He, Kelly, and Manela (2017)).1 In this paper, I attempt to overcome these hurdles using a proprietary data set of nearly one billion credit default swap (CDS) positions that cover the entire U.S. In an integrated market, spreads should not respond to shocks that are specific to firm A\u2019s sellers (Gabaix, Krishnamurthy, and Vigneron (2007)). I compute seller (or buyer) capital shocks based on changes in the value of their CDS portfolio, which for institutional reasons also correspond to CDS portfolio margin payments (Duffie, Scheicher, and Vuillemey (2015)). This is a signature prediction of most theories of slow moving capital (e.g., Duffie and Strulovici (2012)). (2013)).",33 "published_intro_words": 1705,34 "sha256": "126d05da9833d5f755e9bd390990525c6cdbef1110f374612ba849dc30f9ad40"35 }36 ],37 "out_of_domain": {38 "id": "wp29",39 "path": "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/methodes/autonomous-research-claude-code",40 "kind": "latex",41 "note": "UQO WP29, author's own manuscript, methods/CS-flavoured; verified results"42 }43}