Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    When It Rains It Drains: Psychological Distress and Household Net Worth
    (Elsevier, 21/07/2022) Balloch A; Engels C; Philip D
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    Shortability and Asset Pricing Model: Evidence from The Hong Kong Stock Market
    (Elsevier BV, 2017-12) Bai M; Li X-M; Qin Y; Alexander, C
    This study explores how the violation of free short selling assumption affects the performance of CAPM and the Fama-French three-factor model, as existing studies show that short-sales constraints affect asset pricing of the stocks. Using data from the Hong Kong Stock Market which has unique regulations on short selling, we conduct both time-series and cross-sectional regression analyses to evaluate the performance of the two models under the short-sales-constraints and the no-constraints market environment. The two models perform much worse in the former environment than in the latter, indicating a significant impact of the short sales constraints on the explanatory power of the models. We then augment the two models with a shortability-mimicking factor. Our results show that the factor has a significant power in explaining both time-series and cross-sectional variation in the size-B/M portfolio returns. The addition of the factor to the two models considerably increases their overall performance.
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    Modular neural network modelling for long-range prediction of an evaporator
    (2000) Russell NT; Bakker HH; Chaplin RI
    This paper presents the development of a modular neural network model of a three-effect, falling-film evaporator. The model comprises a number of sub-networks each modelling a specific element of the overall system. The modular structure was employed in order to provide benefits in terms of improved model training and performance. The performance of the modular neural model is demonstrated for long-range prediction by comparing it with process data, an analytical simulation and a linear ARX model. The results show that the modular neural model can satisfactorily predict over a horizon of arbitrary length and is suited for implementation within a predictive control scheme. Benefits in terms of model flexibility and interpretability are also discussed. (C) 2000 Elsevier Science Ltd. All rights reserved.
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    Factors affecting Chinese short-term international students’ cross-cultural adaptation in psychology, learning, and life
    (Hindawi, 20/08/2022) Zhang H; Li M
    Although Chinese international students’ cross-cultural adaptation has achieved intense research, factors in developing Chinese SISs’ cross-cultural adaptation remain under-researched. This study examined the factors through a survey of Chinese SISs’ transitional adaptation in psychology, life, and learning. Mixed-method research was conducted: a survey of 155 SISs from a top Chinese university undertaking study across 16 countries and in-depth interviews with 15 SISs. Results indicate that knowledge of the host country and university, language proficiency, sense of participation, and engagement are the crucial factors in developing Chinese SISs’ cross-cultural adaptation. These factors reveal significant correlations with the students’ adaptive performances in psychology, life, and learning. However, the factor of duration indicates no significant correlation with students’ cross-cultural adaptation, which demonstrates an inconsistency with the previous studies. The findings of this study highlight the need for developing Chinese SISs’ sense of engagement, enhancing the language training, and building up the knowledge of the host cultures previous to the study abroad.
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    Numerical bifurcation theory for high-dimensional neural models
    (BioMed Central, 25/07/2014) Laing CR
    Numerical bifurcation theory involves finding and then following certain types of solutions of differential equations as parameters are varied, and determining whether they undergo any bifurcations (qualitative changes in behaviour). The primary technique for doing this is numerical continuation, where the solution of interest satisfies a parametrised set of algebraic equations, and branches of solutions are followed as the parameter is varied. An effective way to do this is with pseudo-arclength continuation. We give an introduction to pseudo-arclength continuation and then demonstrate its use in investigating the behaviour of a number of models from the field of computational neuroscience. The models we consider are high dimensional, as they result from the discretisation of neural field models—nonlocal differential equations used to model macroscopic pattern formation in the cortex. We consider both stationary and moving patterns in one spatial dimension, and then translating patterns in two spatial dimensions. A variety of results from the literature are discussed, and a number of extensions of the technique are given.
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    Using data-driven and process mining techniques for identifying and characterizing problem gamblers in New Zealand
    (RTU Press, 2016-12) Suriadi S; Susnjak T; Ponder-Sutton A; Watters P; Schumacher CR