Taiwan Security Market is a typical order-driven market. The electronic trading system of Taiwan Security Market launched in 1998 significantly reduces the trade matching time (the current matching time is around 20 seconds) and promptly provides updated online trading information to traders. In this study, we establish an online transaction simulation system which can be applied to predict trade prices and study market efficiency. Models are established for the times and volumes of the newly added bid/ask orders on the match list. Exponentially weighted moving average (EWMA) method is adopted to update the model parameters. Match prices are predicted dynamically based on the EWMA updated models. Further, high frequency bid/ask order data are used to find the supply and demand curves as well as the equilibrium prices. Differences between the transaction prices and the equilibrium prices are used to investigate the efficiency of Taiwan Security Market. Finally, EWMA and cusum control charts are used to monitor the market efficiency. In empirical study, we analyze the intra-daily (April, 2005) high frequency match data of Uni-president Enterprises Corporation and Formosa Plastics Corporation.

Incluir a Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM) como ag?ncia financiadora. / Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2017-12-14T16:46:41Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) patrick_aurelio_luiz_ribeiro.pdf: 6159348 bytes, checksum: 5978e3ca5ff417ce94712c998e8c5c8a (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2018-01-03T12:20:58Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) patrick_aurelio_luiz_ribeiro.pdf: 6159348 bytes, checksum: 5978e3ca5ff417ce94712c998e8c5c8a (MD5) / Made available in DSpace on 2018-01-03T12:20:58Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) patrick_aurelio_luiz_ribeiro.pdf: 6159348 bytes, checksum: 5978e3ca5ff417ce94712c998e8c5c8a (MD5) Previous issue date: 2017 / Na modalidade de ensino a dist?ncia, os Ambientes Virtuais de Aprendizagem (AVAs) s?o elementos fundamentais no processo de ensino e aprendizagem, atrav?s da disponibiliza??o de conte?dos e ?reas de discuss?o e comunica??o entre os atores do processo. Entretanto, tais ambientes, na sua maioria, caracterizam-se pelo fato de serem est?ticos, abordando m?todos pedag?gicos gen?ricos atrav?s dos quais estudantes com caracter?sticas e Estilos de Aprendizagem (EAs) diferentes buscam o conhecimento. Dessa maneira, ? importante que sejam levados em considera??o os EAs de cada estudante como forma de tornar a aprendizagem mais eficaz. Question?rios psicom?tricos na maioria das vezes s?o utilizados para que as caracter?sticas de aprendizagem do estudante sejam identificadas, por?m nem sempre tais question?rios apresentam resultados precisos quanto ao EAs de determinado estudante. Assim, faz-se necess?ria a utiliza??o de outras t?cnicas de detec??o, haja vista que uma identifica??o precisa ? capaz de melhorar o processo de aprendizagem por meio de escolhas de estrat?gias pedag?gicas melhores. Diante disso, surge a necessidade de utiliza??o de sistemas inteligentes que se adaptem ?s caracter?sticas de aprendizagem do estudante, utilizando como pressupostos as experi?ncias vivenciadas por ele e as an?lises estat?sticas dessas experi?ncias. Isso pode ser feito atrav?s de avalia??es dos EAs apresentados pelo estudante, em que a partir dos resultados um novo modelo de aprendizagem do estudante ? definido para que o conte?do seja disponibilizado de acordo com esse modelo. Nesse intuito a presente abordagem objetivou identificar e corrigir os EAs do estudante por meio da utiliza??o do conceito de M?dia M?vel Exponencialmente Ponderada no processo de decis?o sobre a aplica??o do refor?o de maneira a ajustar o Modelo do Estudante (ME), de modo que os resultados obtidos, ap?s a realiza??o do teste estat?stico n?o-param?trico de Mann-Whitney, mostraram-se significativamente melhores do que os resultados apresentados por Dor?a (2012), cujo trabalho foi refer?ncia para o desenvolvimento desta proposta. / Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017. / In Distance Learning, Learning Management Systems (LMS) are extremely important elements in teaching and learning process, because they can offer content and spaces of discussion and comunication between people who are part of that process. However they are static and do not consider students? Learning Styles (LS) to show the content, they just use the same pedagogical methods for all learners. It is important to consider students? Learning Styles because this can make the learning process more efective. Most of the time people use Psychometric Instruments to detect students? preferences, but sometimes the outcomes of those methods are not precise. Because of this other techniques of detection of LS can be used to identify precisely the student?s LS and consequently to choose better pedagogical strategies than when are used manual techniques of detection of LS. For this reason intelligent systems which adapt to students? learning characteristics get importance since they use experiences and statistical analysis over these experiences to be adaptive. It can be done based on learner?s Learning Styles that are adjusted by a part of the system, then these new LS are used by another part of the system to select a pedagogical strategy which fit to student?s characteristics. Thus, this work presents an approach which aimed to identify and to correct the Learning Styles of the learner using for this the Exponentially Weighted Moving Average (EWMA) concept. This concept was used to decide if reinforcement signs have to be used to make the student?s modeling. This approach was tested and the outcomes were submitted to non parametric test Mann-Whitney which pointed they were significantly better than the results of Dor?a (2012), whose work was the base of the work presented here.

<p>The multivariate cumulative sum (MCUSUM) and the multivariate exponentially weighted moving average (MEWMA) control charts are the two leading methods to monitor a multivariate process. This thesis focuses on the MEWMA control chart. Specifically, using the Markov chain method, we study in detail several aspects of the run length distribution both for the on- and off- target cases. Regarding the on-target run length analysis, we express the probability mass function of the run length distribution, the average run length (ARL), the variance of run length (V RL) and higher moments of the run length distribution in mathematically closed forms. In previous studies, with respect to the off-target performance for the MEWMA control chart, the process mean shift was usually assumed to take place at the beginning of the process. We extend the classical off-target case and introduce a generalization of the probability mass function of the run length distribution, the ARL and the V RL. What Prabhu and Runger (1996) proposed can be derived from our new model. By evaluating the off-target ARL values for the MEWMA control chart, we determine the optimal smoothing parameters by using the partition method that provides an easy algorithm to find the optimal smoothing parameters and study how they respond as the process mean shift time changes. We compare the ARL performance of the MEWMA control chart with that of the multivariate Shewhart control chart to see whether the MEWMA chart is still effective in detecting a small mean shift as the process mean shift time changes. In order to apply the model to semiconductor manufacturing processes, we use a bivariate normal distribution to generate sample data and compare the MEWMA control chart with the multivariate Shewhart control chart to evaluate how the MEWMA control chart behaves when a delayed mean shift happens. We also apply the variation transmission model introduced by Lawless et al. (1999) to the semiconductor manufacturing process and show an extension of the model to make our application to semiconductor manufacturing processes more realistic. All the programming and calculations were done in R</p> / Master of Science (MS)