Choice of Law: An Empirical Analysis

Sanga, Sarath

doi:10.2139/ssrn.2325706

Cited by 8 publications

(8 citation statements)

References 42 publications

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“…Beyond predicting U.S. Supreme Court decisions, our work contributes to a growing number of articles which either highlight or apply the tools of machine learning to some class of prediction problems in law or legal studies (e.g., [5], [33], [34], [35], [36], [37], [38], [39], [40]). We encourage additional applied machine learning research directed to these areas and new areas where the application of predictive analytics might be fruitful.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

A general approach for predicting the behavior of the Supreme Court of the United States

2017

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Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications.

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Section: Conclusion and Future Researchmentioning

confidence: 99%

A general approach for predicting the behavior of the Supreme Court of the United States

2017

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“…Due to their level of commitment and specifications, all joint ventures are governed by contracts which legally bind the parties by specific rules regarding cooperation and rights and obligations (Barnett, 2010;Luo, 2002). Furthermore, contracts mitigate the uncertainty of exchange hazards by describing legal actions in anticipation of future contingencies (Poppo and Zenger, 2002;Sanga, 2014;Williamson, 1996).…”

Section: Theoretical Background and Hypotheses: Joint Ventures And Culturementioning

confidence: 99%

Cultural communication style and international joint venture contract length

Vélez-Calle

Sosa

Large

2021

Int'l Jnl of Cross Cultural Management

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Purpose: Culturally bound communication styles affect the length of verbal and written messages. Legal contracts are no exception. This paper aims to explore the relationship between cultural communication style and the level of written detail in international joint venture (IJV) contracts. Using a database of actual IJV contracts, we empirically test the relationship between the parties’ cultural communication styles, cultural distance, and the textual length of contracts. We apply Edward T. Hall’s high- and low-context communication construct and find that contracts are longer when made between parties of low- and high-context cultures and shorter when both parties come from high-context cultures relative to when both parties are low context. Additionally, we find that the higher the cultural distance in terms of individualism versus collectivism between partners, the shorter the text of the contracts. The results highlight the influence of culture on contract text length and, as such, on contract negotiation and design costs. This article contributes to the culture and strategic alliance literature by going beyond the assumption that low-context culture contracts are longer and high-context culture contracts shorter by testing it empirically.

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“…Scholars in law and finance have often studied agreements drawn from EDGAR, including credit agreements, employment agreements, and merger agreements ( [16,17,18,19,20,21]). In this example, we will demonstrate how a researcher could quickly retrieve a sample of employment agreements and review them for the presence of non-compete and/or non-solicitation clauses.…”

Section: Identifying Non-compete Clauses In Executive Employment Agrementioning

confidence: 99%

OpenEDGAR: Open Source Software for SEC EDGAR Analysis

2018

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OpenEDGAR is an open source Python framework designed to rapidly construct research databases based on the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system operated by the US Securities and Exchange Commission (SEC). OpenEDGAR is built on the Django application framework, supports distributed compute across one or more servers, and includes functionality to (i) retrieve and parse index and filing data from EDGAR, (ii) build tables for key metadata like form type and filer, (iii) retrieve, parse, and update CIK to ticker and industry mappings, (iv) extract content and metadata from filing documents, and (v) search filing document contents. OpenEDGAR is designed for use in both academic research and industrial applications, and is distributed under MIT License at https://github.com/LexPredict/openedgar.

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Choice of Law: An Empirical Analysis

Cited by 8 publications

References 42 publications

A general approach for predicting the behavior of the Supreme Court of the United States

A general approach for predicting the behavior of the Supreme Court of the United States

Cultural communication style and international joint venture contract length

OpenEDGAR: Open Source Software for SEC EDGAR Analysis

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