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 período 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 períodos mais curtos, foram construídas taxas forward instantâneas, que antecipam as taxas spot instantâneas a distâncias que vão de 1 a 10 dias. Para períodos mais longos, foram construídas 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-períodos. Nas taxas mensais, pelo contrário, apenas em alguns casos foi constatada a existência de cointegração, quer para a totalidade do período quer para os sub-períodos. 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 período, as taxas instantâneas apresentaram um comportamento instável, principalmente para o sub-período 2012-2014