Reforming securities and derivatives trading in the EU: from EMIR to MiFIR

The financial crisis has generated a deep revision of the regulation of securities and derivatives markets. In this paper, we critically examine the extent to which current reforms, such as the European Market Infrastructure Regulation and the proposed new Markets in Financial Instruments Directive and Regulation, will expand "public" securities and derivatives markets, while correspondingly reducing the scope of "private" markets (which broadly coincide with the "unregulated" over-the-counter markets). We also ask whether these reforms will on the whole reduce systemic risks and transaction costs of securities and derivatives trading in Europe. For these purposes, we formulate conjectures that are partly based on the experience of past reforms in the area of equity trading

Building a Document Corpus for Manufacturing Knowledge Retrieval

When faced with challenging technical problems, R&D personnel would often turn to technical papers to seek inspiration for a solution. The building of a corpus of such papers and the easy retrieval of relevant papers by the user in his query is an area that has not been systematically dealt with. This is an attempt to build such a corpus for manufacturing R&D personnel. Manufacturing Corpus Version 1 (MCV1) is an archive of more than 1400 relevant manufacturing engineering papers between 1998 and 2000. In this paper, the origins and motivation of building MCV1 is discussed. The innovative coding process which is specially designed for manufacturing companies will be presented. All other relevant issues, like coding policy, category codes and input documents, will be explained. Finally, two quality indicators which integrate all concerns about coding quality will be examined.Singapore-MIT Alliance (SMA

Global disease monitoring and forecasting with Wikipedia

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data such as social media and search queries are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with $r^2$ up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein and adjust novelty claims accordingly; revise title; various revisions for clarit

Machine learning methods for systemic risk analysis in financial sectors.

Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work.This research has been partially supported by grants from the National Natural Science Foundation of China (#U1811462, #71874023, #71771037, #71725001, and #71433001)

Where do electronic markets come from? Regulation and the transformation of financial exchanges

The practices of high-frequency trading (HFT) are dependent on automated financial markets, especially those produced by securities exchanges electronically interconnected with competing exchanges. How did this infrastructural and organizational state of affairs come to be? Employing the conceptual distinction between fixed-role and switch-role markets, we analyse the discourse surrounding the design and eventual approval of the Securities and Exchange Commission’s Regulation of Exchanges and Alternative Trading Systems (Reg ATS). We find that the disruption of the exchange industry at the hands of automated markets was produced through an interweaving of both technological and political change. This processual redefinition of the ‘exchange’, in addition, may provide a suggestive precedent for understanding contemporary regulatory crises generated by other digital marketplace platforms

An architectural framework for self-configuration and self-improvement at runtime

[no abstract