6,209 research outputs found
Causality in Quantiles and Dynamic Stock Return-Volume Relations
This paper investigates the causal relations between stock return and volume based on quantile regressions. We first define Granger non-causality in all quantiles and propose testing non-causality by a sup-Wald test. Such a test is consistent against any deviation from non-causality in distribution, as opposed to the existing tests that check only noncausality in certain moment. This test is readily extended to test non-causality in different quantile ranges, and the testing results enable us to identify the quantile range for which causality is relevant. In the empirical studies of 3 major stock market indices, we find that, while the conventional test suggests no causality in mean, there are strong evidences that lagged volume Granger causes return in all but some middle quantiles. In particular, the causal effects have opposite signs at lower and upper quantiles and are stronger at more extreme quantiles. These relations form (symmetric) V shapes across quantiles. They also show that the dispersion of the return distribution increases with volume so that volume has a positive effect on return volatility. It is also shown that the quantile causal effects of lagged return on volume are mainly negative.Granger non-causality in quantiles, quantile causal effect, quantile regression, return-volume relation, sup-Wald test
Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data
It is well known that recognizers personalized to each user are much more
effective than user-independent recognizers. With the popularity of smartphones
today, although it is not difficult to collect a large set of audio data for
each user, it is difficult to transcribe it. However, it is now possible to
automatically discover acoustic tokens from unlabeled personal data in an
unsupervised way. We therefore propose a multi-task deep learning framework
called a phoneme-token deep neural network (PTDNN), jointly trained from
unsupervised acoustic tokens discovered from unlabeled data and very limited
transcribed data for personalized acoustic modeling. We term this scenario
"weakly supervised". The underlying intuition is that the high degree of
similarity between the HMM states of acoustic token models and phoneme models
may help them learn from each other in this multi-task learning framework.
Initial experiments performed over a personalized audio data set recorded from
Facebook posts demonstrated that very good improvements can be achieved in both
frame accuracy and word accuracy over popularly-considered baselines such as
fDLR, speaker code and lightly supervised adaptation. This approach complements
existing speaker adaptation approaches and can be used jointly with such
techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201
Three Tramp Dacetine Ants in Taiwan
Trabalho de projeto do mestrado em Economia (Economia Financeira), apresentado à Faculdade de Economia da Universidade de Coimbra.Neste trabalho, as taxas forward foram utilizadas para prever os valores futuros da
Estrutura de Prazo das Taxas de Juro, em diferentes pontos desta estrutura, e em diferentes
contextos do sistema financeiro, e abrange o perÃodo que vai do final de 2004 ao final de
2014. As taxas spot e forward foram construidas a partir do modelo de Nelson, Siegel e
Svensson (1994), e para a anlisar a relação existente entre estes dois tipos de taxas,
recorreu-se o método de cointegração proposto por Johansen (1988, 1991). Para perÃodos
mais curtos, foram construÃdas taxas forward instantâneas, que antecipam as taxas spot
instantâneas a distâncias que vão de 1 a 10 dias. Para perÃodos mais longos, foram
construÃdas taxas forward com prazo de 1 mês, que antecipam as taxas spot com o mesmo
prazo, a distâncias que vão de 1 a 12 meses. Nas taxas instantâneas, verificou-se que existe
cointegração entre todas as taxas forward e as taxas spot que antecipam, nas estimações
que abrangem a totalidade da amostra, e para alguns casos quando se divide a amostra em
sub-perÃodos. Nas taxas mensais, pelo contrário, apenas em alguns casos foi constatada a
existência de cointegração, quer para a totalidade do perÃodo quer para os sub-perÃodos. De
seguida, foi estimado o Modelo de Correção dos Erros proposto por Johansen (1988,
1991), e recorreu-se à analise da função impulso-resposta, para as taxas cointegradas. As
taxas mensais apresentaram sempre um comportamento mais instável, quando comparadas
com as taxas instantâneas. Entretanto, com a divisão do perÃodo, as taxas instantâneas
apresentaram um comportamento instável, principalmente para o sub-perÃodo 2012-2014
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