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Abstract:
What can empirical data tell us about the United States Tax Court? What sections of the Internal Revenue Code are most frequently cited and what recent tax legislation has sparked the most change in the Tax Court’s jurisprudence? Contributing to the growing extant literature on citation networks, this Article presents an analysis of the citation practices of the United States Tax Court between 1990 and 2008. While previous citation studies appropriately rely upon case-to-case citations as their unit of analysis, we modify this approach to focus on statutory citations, which represent the best proxy for the underlying content of the decisions in the field of tax law. Building upon prior citation scholarship and leveraging techniques from computer science and informatics, we collected and analyzed more than 11,000 decisions and 244,000 statutory citations authored by the United States Tax Court between 1990 and 2008. Our analysis includes both a static and time-series analysis for the most cited Internal Revenue Code sections. We also offer a network analysis and subsequent analytics focusing both on the static reporting of which Code sections are most often cited and the dynamic patterns of change in citation practices over time. Although this data retrieval itself answers the call for greater empiricism in tax scholarship, we also argue that our findings support the idea that there are effectively two Codes: the legislated Code, made up of the sections that are the basis of legislative action but only infrequently make it into Tax Court opinions, and the litigated Code, made up of the sections most frequently cited by Tax Court judges. We then discuss the implications of this argument, as well as opportunities for further research.

Empirical Legal Studies, United States Tax Court, Judicial Decision Making, Computational Legal Studies, Network Analysis and Law, Network Dynamics, Text Parsing of Legal Documents, Information Visualization, Citation Network, Tax Litigation

Abstract:
Citation networks are a cornerstone of network research and have been important to the general development of network theory. Citation data have the advantage of constituting a well-defined set where the nature of nodes and edges is reasonably well specified. Much interesting and important work has been done in this vein, with respect to not only academic but also judicial citation networks. For example, previous scholarship focuses upon broad citation patterns, the evolution of precedent, and time-varying change in the likelihood that communities of cases will be cited. As research of judicial citation and semantic networks transitions from a strict focus on the structural characteristics of these networks to the evolutionary dynamics behind their growth, it becomes even more important to develop theoretically coherent and empirically grounded ideas about the nature of edges and nodes. In this paper, we move in this direction on several fronts. We compare several network representations of the corpus of United States Supreme Court decisions (1791-2005). This corpus is not only of seminal importance, but also represents a highly structured and largely self-contained body of case law. As constructed herein, nodes represent whole cases or individual 'opinion units' within cases. Edges represent either citations or semantic connections. As our broader goal is to better understand American common law development, we are particularly interested in the union, intersect and compliment of these various citation networks as they offer potential insight into the long-standing question of whether 'law is a seamless web'? We believe the characterization of law’s interconnectedness is an empirical question well suited to the tools of computer science and applied graph theory. While much work still remains, the analysis provided herein is designed to advance the broader cause.

computational legal studies, computer programming and law, network analysis, judicial citation networks, law as a complex system, evolutionary graph theory, computational linguistics and law, semantic analysis, supreme court citations, evolution of law

Abstract:
There are fundamental differences between citation networks and other classes of graphs. In particular, given that citation networks are directed and acyclic, methods developed primarily for use with undirected social network data may face obstacles. This is particularly true for the dynamic development of community structure in citation networks. Namely, it is neither clear when it is appropriate to employ existing community detection approaches nor is it clear how to choose among existing approaches. Using simulated data, we attempt to clarify the conditions under which one should use existing methods and which of these algorithms is appropriate in a given context. We hope this paper will serve as both a useful guidepost and an encouragement to those interested in the development of more targeted approaches for use with longitudinal citation data.

community detection, evolutionary graph theory, computational legal studies, citation network, direct acyclic graphs, graph theory, network analysis and law, judicial citation network, network dynamics

Abstract:
Acyclic digraphs arise in many natural and artificial processes. Among the broader set, dynamic citation networks represent a substantively important form of acyclic digraphs. For example, the study of such networks includes the spread of ideas through academic citations, the spread of innovation through patent citations, and the development of precedent in common law systems. The specific dynamics that produce such acyclic digraphs not only differentiate them from other classes of graphs, but also provide guidance for meaningful distance measures for these networks. We apply our sink based distance measure and the single-linkage hierarchical clustering algorithm to the first quarter century of decisions of the United States Supreme Court. Despite applying the simplest distance measure and a straight forward clustering algorithm, qualitative analysis reveals that accurate clusterings are produced by this scheme.

citation networks, acyclic digraphs, dynamic network analysis, judicial citations, patent citations, distance measures, community detection, clustering

Abstract:
The United States Code is a body of documents that collectively comprises the statutory law of the United States. In this short paper, we investigate the properties of the network of citations contained within the Code - most notably its degree distribution. Acknowledging the text contained within each of the Code's section nodes, we adjust our interpretation of the nodes to control for section length. Though we find a number of interesting properties in these degree distributions, we demonstrate that a power law distribution is not an appropriate model for this system.

United States Code, Citation Network, Computational Legal Studies, Skewed Distributions, Degree Distribution

Abstract:
We present a new profitable trading and risk management strategy with transaction cost for an adaptive equally weighted portfolio. Moreover, we implement a rule-based expert system for the daily financial decision making process by using the power of spectral analysis. We use several key components such as principal component analysis, partitioning, memory in stock markets, percentile for relative standing, the first four normalized central moments, learning algorithm, switching among several investments positions consisting of short stock market, long stock market and money market with real risk-free rates. We find that it is possible to beat the proxy for equity market without short selling for S&P 500-listed 168 stocks during the 1998-2008 period and Russell 2000-listed 213 stocks during the 1995-2007 period. Our Monte Carlo simulation over both the various set of stocks and the interval of time confirms our findings.

portfolio risk management, algorithmic trading, out-of-sample prediction, long memory in stocks, adaptive learning algorithm, market timing, principal component analysis, simulation