ISSN 1842-4562
Member of DOAJ
ISSUES
Volume 16 - 2021
Volume 15 - 2020
Volume 14 - 2019
Volume 13 - 2018
Volume 12 - 2017
Volume 11 - 2016
Volume 10 - 2015
Volume 9 - 2014
Volume 8 - 2013
Volume 7 - 2012
Volume 6 - 2011
Volume 5 - 2010
Volume 4 - 2009
Volume 3 - 2008
Volume 2 - 2007
Volume 1 - 2006

Journal Home > Volume 14, Issue 3 - September 30, 2019

JAQM Volume 14, Issue 3 - September 30, 2019




Contents


Entrepreneurial Orientation influence on Sustainable oriented Innovation in Manufacturing SMEs
Sebastian Ion CEPTUREANU

This paper analyses the influence of Entrepreneurial Orientation on Sustainable oriented Innovation.Entrepreneurial Orientation construct is based on five dimensions – Innovativeness, Risk taking, Proactiveness, Competitive aggressiveness and Autonomy, while Sustainable oriented Innovation considers three dimensions – process, organizational and product innovations. The conclusion is that Entrepreneurial Orientation positively impacts upon Sustainable oriented Innovation.

A Note on a Size-Biased Quasi Poisson-Lindley Distribution
Rama SHANKER, Kamlesh Kumar SHUKLA

In this paper coefficients of variation, skewness, kurtosis and index of dispersion of size-biased quasi Poisson-Lindley distribution (SBQPLD) have been discussed and their behaviors have been explained graphically for varying values of parameters. Some applications of SBQPLD have also been discussed.

Robust Bayesian Neural Networks for the Prediction of Financial Assets Returns
Sorin OPINCARIU, Stefan Alexandru IONESCU

The success of machine learning models and the vast amounts of financial that are automatically collected have led to an increased interest in applying the machine learning techniques in the prediction of the evolution of financial assets. Most machine learning are black boxes, there is no way to assess the model’s confidence in its results which is undesirable in risk sensitive domains like finance. In this paper we used the bayesian methodology as a principled way of building models with well calibrated uncertainty estimates. We discuss about the three types of uncertainty (structural, epistemic and random). We discuss on how one can construct bayesian extensions of one of the most successful black boxes: neural networks. While the literature focuses on the bayesian neural network with normal likelihood for general regression, we propose using a bayesian hierarchical model with a Student likelihood for financial regression. Doing so we account for the well-known stylized the non-gaussianity of the returns, making the regression more robust t outliers. We apply the robust bayesian neural network to the prediction of 10 time series and discussed the results obtained.