Most comments, however, consist of economic or legal arguments. As such, we also utilize the positive and negative dictionaries developed in [ 43 ] by training on K filings annual reports of publicly traded companies to the U. Security and Exchange Commission. Lastly, we utilize the litigiousness and uncertainty dictionaries from [ 43 ] to further characterize the text content with additional normalized word counts. If a document has high litigiousness score, then the document has a propensity for legal contest.

Thus, we calculate four features sentiment with informal writing, financial sentiment, litigiousness, and uncertainty that are dictionary-based; note that all dictionaries are chosen because they were developed for text generated in similar domains and contexts, which addresses a major challenge in dictionary-based analysis [ 44 ]. The last text measure that we compute is the focus or specificity of the rule or comment. Topic models assume that all documents share the same topic set, but each document exhibits a different mixture of those topics.

Due to space constraints, the reader is referred to [ 45 ] for details of this generative model. We provide a formal definition of the joint distribution defined by LDA using the notation in [ 45 ]. The topic assignments for the d th document are z d , where z d , n is the topic assignment for the n th word in document d. The observed words for document d are w d , where w d , n is the n th word in document d , which is an element from the fixed vocabulary. Extensive work in computer science and applied statistics has led to fast algorithms capable of analyzing extremely big text archives.

For complete statistical and algorithmic details on the topic model, see [ 45 , 49 ] and references therein. In our empirical work, the topic model was estimated jointly for all proposed and final rules, with 25 topics chosen through cross-validation. A second topic model was estimated on the corpus of comments, with 50 topics chosen through cross-validation.

Using the LDA model, to quantitatively measure the spread of discourse in a single document d , we define focus as 1 where. In other contexts such as economics, focus is known as the normalized Herfindahl index and is bounded by 0 and 1. This is an intuitive and useful variable, since it captures information akin to the 2nd moment i.

High values for focus mean that the document is concentrated on a specific topic or issue, and has low diversity in the topics of discussion, whereas lower values of focus mean multiple topics are discussed within the same document. In principle, other measures of spread like entropy or variance could be used. The above definition of focus was chosen, since it is bounded and higher values denote higher concentration.

This definition captures the absolute distance in the rule-topic probabilities, assuming that each probability is assigned a point mass. The hazard function for the Cox proportional hazard model takes the form. Next, let Y i denote the observed time either censoring time or event time for subject i. Let C i be the indicator that the time corresponds to an event i. Then, one obtains the partial likelihood to estimate the parameters of interest. Even though the response variable is a count of comments submitted for a given rule, the negative binomial distributional assumption is preferred over the Poisson distribution due to overdispersion, that is, the variance of this variable is much larger than its mean see Table 1.

This zip file contains raw and processed data for the comments and rules, as well as R code to reproduce the main results presented in this paper. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Following the financial crisis, governments around the world passed laws that marked the beginning of new period of enhanced regulation of the financial industry. Introduction Financial crises induce large societal costs in the form of direct bailouts or through slower economic growth as a consequence of firm and household debt reduction [ 1 ].

Download: PPT. Table 1. Summary statistics for proposed and finalized rules, and the public commentary.

Table 2. Ten most frequent words for different dictionary-based text measures. Fig 1. Number of comments received by the CFTC for proposed rules over time. Fig 2. Sentiment of comments submitted by interest groups for proposed rule 75 FR , which introduced a number of new requirements for registration, disclosure, recordkeeping, financial reporting, minimum capital, and other operational standards with respect to retail foreign exchange forex transactions. Table 4. Regression results testing whether proposed rules were shorter and more litigious during the first days following passage of the Dodd-Frank Act.

Multivariate analysis To more formally test the patterns found above, we estimate a Cox proportional hazards model, where the event time is measured in days post-Dodd-Frank to rule proposal. Table 5. Cox proportional hazards model where the event time is number of days from the Dodd-Frank Act becoming law to rule proposal. Table 6. Negative binomial regression model estimation results, where the dependent variable is the number of comments submitted for a given rule.

Table 7. Cox proportional hazards model where the event time is number of days from rule proposal to rule finalization. Table 8. OLS regression explaining the amount change between the final and proposed rule. Discussion Here we synthesize the full breadth of results presented above with respect to the overall rule-making process and in particular the potential strategies of the government and public.

### Treasury Issues Report and Recommendations on Capital Markets

Methods All statistical analysis was performed in R version 3. Text-based measures We calculate a number of text-based features through word counts with different dictionaries, as reported in Table 1. The hazard function for the Cox proportional hazard model takes the form Next, let Y i denote the observed time either censoring time or event time for subject i. Supporting information.

S1 File. Data and analysis code. References 1. International Monetary Fund. Global Financial Stability Report. How bad was it? The costs and consequences of the —09 financial crisis.

Staff Papers. View Article Google Scholar 3. Opinion: A new approach to financial regulation. Proceedings of the National Academy of Sciences. View Article Google Scholar 4. The New York Times. Only a Hint of Roosevelt in Financial Overhaul; Johnson S, Kwak J. Vintage; U S Congress. Crotty J. Structural causes of the global financial crisis: a critical assessment of the new financial architecture.

Cambridge Journal of Economics. View Article Google Scholar 8.

## The Post-Reform Guide to Derivatives and Futures by Gordon F. Peery - iqegumybiwyf.ml

A Bias Towards Business? Assessing Interest Group Influence on the U. The Journal of Politics. View Article Google Scholar 9. Yackee SW. Journal of Public Administration Research and Theory. View Article Google Scholar Carrigan C, Coglianese C. The politics of regulation: From new institutionalism to new governance. Annual Review of Political Science. Clerks or kings? Partisan alignment and delegation to the US bureaucracy. McGarity TO. Some thoughts on deossifying the rulemaking process. Duke Lj. Potter RA. Big Data and the regulation of banking and financial services.

Bank Financial Services Policy Report. Big data and the regulation of financial markets. IEEE; Data science and political economy: application to financial regulatory structure. Cuellar MF. Rethinking Regulatory Democracy. Administrative Law Review. Shapiro S. When will they listen? Public comment and highly salient regulations.

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Mertcatus Center Working Paper. American Politics Research. Nelson D, Yackee SW. Levina E, Bickel P. In: Computer Vision, ICCV CEOs; Swap Reporting: Who and When? Stroock Special Bulletin. Niskanen WA. Bureaucracy and representative government. Transaction Publishers; Golden MM.

What motivates bureaucrats? Columbia University Press; Carpenter DP. The forging of bureaucratic autonomy: Reputations, networks, and policy innovation in executive agencies, Princeton University Press; Brehm J, Gates S. Donut shops and speed traps: Evaluating models of supervision on police behavior. American Journal of Political Science.

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Obar JA. Net Neutrality Closing the Technocratic Divide? International Journal of Communication. USA Today. Record 9 million comments flood FCC on net neutrality; Coglianese C. Enhancing public access to online rulemaking information. R Core Team. Text Mining Infrastructure in R. Journal of Statistical Software. New York: Springer; Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th international conference on World Wide Web. ACM; Loughran T, McDonald B. When is a liability not a liability?

Textual analysis, dictionaries, and Ks. The Journal of Finance. Liu B. Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge University Press; Blei DM. Probabilistic topic models. Communications of the ACM. Variational inference for Dirichlet process mixtures. The latest alarm was triggered by a fake tweet saying that the White House was bombed, prompting a U. In U. At a CFTC hearing last week, the potential dangers became even more apparent. The CFTC staff is not up to the task of monitoring computerized trading programs that use algorithms and powerful technology and send trades in milliseconds on a real-time basis, Shilts added.

Proponents of high-frequency trading argue that the technology creates more efficient markets. They cite research showing improved price discovery and market liquidity, and reduced price discrepancies.

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Market players can manage risk more efficiently, and spreads between the bid and ask prices are narrower, they claim. With no clear verdict on how high-frequency trading should be policed, regulators and the industry remain on alert as technology continues its rapid advance. A favored tool of hedge funds and other institutional traders, high-frequency trading accounted for more than 60 percent of all futures volume in on U.

James Overdahl, an adviser to the Futures Industry Association, said the exchanges have been upgrading their systems to keep up with the surge in activity. They are tracking not just high-speed trade flow but also the frantic messaging on cancellations and reorders generated by high-speed trading, said the former economist with the U.

The IntercontinentalExchange is cracking down on excessive messaging, charging fees when traders exceeded limits. By rapidly sending order messages, then quickly canceling them, traders can create an appearance of market liquidity. But HFT critics want more, demanding an end to what they consider disruptive practices.