High Frequency Trading (HFT) algorithms are automated feedback systems interacting with markets to maximize returns on investments. These systems have the potential to read different resolutions of market information at any given time, where Level I information is the minimal information about an equity--essentially its price--and Level II information is the full order book at that time for that equity. This paper presents a study of using Recurrent Neural Network (RNN) models to predict the spread of the DOW Industrial 30 index traded on NASDAQ, using Level I and Level II data as inputs. The results show that Level II data does not significantly improve the prediction of spread when predicting less than 100 millisecond into the future, while it is increasingly informative for spread predictions further into the future. This suggests that HFT algorithms should not attempt to make use of Level II information, and instead reallocate that computation power for improved trading performance, while slower trading algorithms may very well benefit from processing the complete order book.

Made available in DSpace on 2016-04-28T19:33:35Z (GMT). No. of bitstreams: 1 Ines Aparecida Teixeira.pdf: 7381264 bytes, checksum: 5804c00da067119a4cbcbdb869acebe8 (MD5) Previous issue date: 2012-05-16 / This research, which is about the informativity of textuality as a factor in the opinionated text redaction performed as an exercise in schools, from the restlessness caused by repetition of the same ideas in the expression of opinionated students, what we name here common sense. Also motivated by the evident importance given to the students' written production, whether as a practice in the classroom, whether in external examinations that assess the quality of basic education. The challenge of finding ways to improve the production of written opinions led to a revision of informativity among the textuality factors postulated by Beaugrande & Dressler (1981). This made it possible to understand how the new material exerts control in the selection of meaningful content of the texts and how, thus limiting the informativity or motivates communicability, to the extent that there are instances that indicate low, medium or high informativity. To measure the informativity of these orders, the sources of information expectations were organized in linguistic knowledge and conception of reality, constituting criteria for analysis in the research corpus. In four opinion writings texts produced in the school situation, deconstructed the microstructure to macrostructure, it was observed occurrences of the degrees of informativity. The results indicate that the texts that usually find common sense ideas are characteristic of low informativity, the texts of average informativity are models that guide the appropriate text, and the production of high informativity are difficult to interpret, in need of simplification to be understood . They are finding that open the way to improve the production of texts in school, since they allow a more precise evaluation of the quality of the thematic approach of opinion pieces / Esta pesquisa, que tem como tema a informatividade como fator de textualidade no texto opinativo realizado como exercício redacional nas escolas, partindo da inquietação provocada pela reiteração das mesmas ideias na expressão opinativa dos alunos, aquilo que nomeamos aqui de senso comum. Também motivados pela evidente importância dada às produções escritas dos alunos, sejam como prática em sala de aula, sejam em exames externos que avaliam a qualidade do ensino básico. O desafio de encontrar caminhos de aprimoramento para a produção escrita de opiniões motivou a revisão da informatividade entre os fatores de textualidade postulados por Beaugrande & Dressler (1981). Isso possibilitou entender como o material novo exerce controle na seleção do conteúdo significativo dos textos e como, assim, a informatividade limita ou motiva a comunicabilidade, na medida em que há ocorrências que indiquem baixa, média ou alta informatividade. Para mensurar essas ordens de informatividade, as fontes de expectativas de informação foram organizadas em conhecimento linguístico e concepção de realidade, constituindo-se critérios para a análise no corpus de pesquisa. Em quatro textos opinativos produzidos em situação escolar, desconstruídos da microestrutura para macroestrutura, observou-se os graus de ocorrências de informatividade. Os resultados indicam que os textos em que costumeiramente encontramos ideias do senso comum são característicos de baixa informatividade; os textos de média informatividade são modelos que norteiam o texto adequado; e as produções de alta informatividade são de difícil interpretação, precisando de simplificação para serem compreendidas. São constatações que abrem caminho para o aprimoramento da produção textual na escola, uma vez que permitem uma visão mais precisa para avaliar a qualidade da abordagem temática dos textos opinativos

This licentiate thesis consist of two separate research papers which concern two tangential topics – that of semantic information and that of information security. Both topics are approached by similar methods, i.e. with a concern about conceptual and definitional issues. In Paper I – concerning the concept of information, and a semantic conception thereof – the conceptual, and definitional, issues focus on one property, that of truthfulness. It is argued – against the veridicality thesis – that semantic information need not be truthful. In Paper II – concerning information security – it is argued that the current leading definitions (so-called ‘CIA’ definitions, which define information as secure if, and only if, the properties of confidentiality, integrity, and availability are retained) suffer from both actual and possible counter-examples, and lack an appropriate conceptual sense. On the basis of this criticism a new kind of definitions is proposed and argued for. / <p>QC 20161220</p> / SECURIT

Dynamical structure functions were developed as a partial structure representation of linear time-invariant systems to be used in the reconstruction of biological networks. Dynamical structure functions contain more information about structure than a system's transfer function, while requiring less a priori information for reconstruction than the complete computational structure associated with the state space realization. Early sufficient conditions for network reconstruction with dynamical structure functions severely restricted the possible applications of the reconstruction process to networks where each input independently controls a measured state. The first contribution of this thesis is to extend the previously established sufficient conditions to incorporate both necessary and sufficient conditions for reconstruction. These new conditions allow for the reconstruction of a larger number of networks, even networks where independent control of measured states is not possible. The second contribution of this thesis is to extend the robust reconstruction algorithm to all reconstructible networks. This extension is important because it allows for the reconstruction of networks from real data, where noise is present in the measurements of the system. The third contribution of this thesis is a Matlab toolbox that implements the robust reconstruction algorithm discussed above. The Matlab toolbox takes in input-output data from simulations or real-life perturbation experiments and returns the proposed Boolean structure of the network. The final contribution of this thesis is to increase the applicability of dynamical structure functions to more than just biological networks by applying our reconstruction method to wireless communication networks. The reconstruction of wireless networks produces a dynamic interference map that can be used to improve network performance or interpret changes of link rates in terms of changes in network structure, enabling novel anomaly detection and security schemes.

Network reconstruction is the process of recovering a unique structured representation of some dynamic system using input-output data and some additional knowledge about the structure of the system. Many network reconstruction algorithms have been proposed in recent years, most dealing with the reconstruction of strictly proper networks (i.e., networks that require delays in all dynamics between measured variables). However, no reconstruction technique presently exists capable of recovering both the structure and dynamics of networks where links are proper (delays in dynamics are not required) and not necessarily strictly proper.The ultimate objective of this dissertation is to develop algorithms capable of reconstructing proper networks, and this objective will be addressed in three parts. The first part lays the foundation for the theory of mathematical representations of proper networks, including an exposition on when such networks are well-posed (i.e., physically realizable). The second part studies the notions of abstractions of a network, which are other networks that preserve certain properties of the original network but contain less structural information. As such, abstractions require less a priori information to reconstruct from data than the original network, which allows previously-unsolvable problems to become solvable. The third part addresses our original objective and presents reconstruction algorithms to recover proper networks in both the time domain and in the frequency domain.