A Review of Forecasting Techniques

This work examines recent publications in forecasting in various fields, these include: wind power forecasting; electricity load forecasting; crude oil price forecasting; gold price forecasting energy price forecasting etc. In this review, categorization of the processes involve in forecasting are divided into four major steps namely: input features selection; data pre-processing; forecast model development and performance evaluation. The various methods involve are discussed in order to provide the overall view about possible options for development of forecasting system. It is intended that the classification of the steps into small categories with definitions of terms and discussion of evolving techniques will provide guidance for future forecasting sytem designers

Consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. <br/

Semantic Image Segmentation via Deep Parsing Network

This paper addresses semantic image segmentation by incorporating rich information into Markov Random Field (MRF), including high-order relations and mixture of label contexts. Unlike previous works that optimized MRFs using iterative algorithm, we solve MRF by proposing a Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which enables deterministic end-to-end computation in a single forward pass. Specifically, DPN extends a contemporary CNN architecture to model unary terms and additional layers are carefully devised to approximate the mean field algorithm (MF) for pairwise terms. It has several appealing properties. First, different from the recent works that combined CNN and MRF, where many iterations of MF were required for each training image during back-propagation, DPN is able to achieve high performance by approximating one iteration of MF. Second, DPN represents various types of pairwise terms, making many existing works as its special cases. Third, DPN makes MF easier to be parallelized and speeded up in Graphical Processing Unit (GPU). DPN is thoroughly evaluated on the PASCAL VOC 2012 dataset, where a single DPN model yields a new state-of-the-art segmentation accuracy.Comment: To appear in International Conference on Computer Vision (ICCV) 201

An empirical analysis on the credit scoring and the intermediary role of financing guarantee institutions of China's car loans

By the end of 2018, China's car ownership has reached 240 million, an increase of 10.51% over 2017, which leads to the increase of automobile financial services and hence the associated automobile credit risks. In order to transfer risks, financial institutions increasingly are choosing to issue auto loans through financing guarantee companies. Therefore, the industry pays more attention to the credit scoring, as it acts as the main risk control measure of auto financing guarantee companies. This leads to the study of the role the financing guarantee company plays and how effective the credit rating is as a risk control mechanism. The purpose is to investigate whether the auto financing guarantee company plays a mediating role by providing credit score. The empirical approach is as follows: a two-stage regression method is used to control or eliminate the influence of personal characteristics and other third-party credit ratings. Through which, we firstly test whether the credit score of an auto financing guarantee company contains additional information besides personal characteristics and third-party credit scores. Second, we test whether additional information of auto financing guarantee company can significantly explain the post-loan performance of whether default or non-default. The conclusions show that even after controlling the third-party credit score and personal characteristics, the credit scoring system of auto financing guarantee companies still has a significant explanation on the performance of post-loan default. In other words, it plays an intermediary role by providing credit evaluation services, which has a direct decision reference for the financial institutions that ultimately provide credit. Based on this, this study puts forward corresponding management enhancement and loan risk management suggestions.No final de 2018, a propriedade automóvel na China atingiu 240 milhões, um aumento de 10.51% sobre 2017, o que leva ao aumento dos serviços financeiros automóvel e, portanto, dos riscos de crédito automóvel associados. Para mitigar riscos, as instituições financeiras optam, cada vez mais, por conceder empréstimos automóvel através de empresas de garantia. Por conseguinte, a indústria presta mais atenção à pontuação do crédito, uma vez que esta atua como a principal medida de controlo do risco das empresas de garantia de financiamento-automóvel. Isto conduz ao estudo do papel desempenhado pela empresa de garantia de financiamento e da eficácia da sua notação de crédito como mecanismo de controlo dos riscos. Com base no sistema de notação de crédito da T’s e num total de 119.798 registos de empréstimos, este estudo examina o poder explicativo da notação de crédito das empresas de garantia de financiamento automóvel no incumprimento dos mutuários e as funções mediadoras destas empresas. Utiliza-se um método de regressão em dois estágios para controlar ou eliminar a influência de características pessoais e outros ratings, testando primeiro se a notação de crédito de uma empresa de garantia contém informações adicionais e testando, depois, se as informações adicionais da empresa de garantia podem explicar significativamente o desempenho do mutuário pós-empréstimo, As conclusões mostram que, mesmo após controlar a notação de crédito de terceiros e as características pessoais, o sistema de notação de crédito das empresas de garantia tem uma explicação significativa no desempenho do mutuário pós-empréstimo. Ou seja, ele desempenha um papel mediador, fornecendo serviços de avaliação de crédito que têm influência direta na decisão das instituições financeiras que, finalmente, fornecem crédito. Correspondentemente, esta investigação apresenta sugestões de melhoramento da gestão do risco de crédito

Sustainable Supply Chain Management

The book is a collection of studies dedicated to different perspectives of three dimensions or pillars of the sustainability of supply chain and supply chain management - economic, environmental, and social - and other aspects related to performance evaluation, optimization, and modelling of and for sustainable supply chain management, and thus presents another valuable contribution to sustainable development and sustainable way of life

Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing

We used a large sample of 188,652 properties, which represented 4.88% of the total housing stock in Catalonia from 1994 to 2013, to make a comparison between different real estate valuation methods based on artificial neural networks (ANNs), quantile regressions (QRs) and semi-log regressions (SLRs). A literature gap in regard to the comparison between ANN and QR modelling of hedonic prices in housing was identified, with this article being the first paper to include this comparison. Therefore, this study aimed to answer (1) whether QR valuation modelling of hedonic prices in the housing market is an alternative to ANNs, (2) whether it is confirmed that ANNs produce better results than SLRs when assessing housing in Catalonia, and (3) which of the three mass appraisal models should be used by Spanish banks to assess real estate. The results suggested that the ANNs and SLRs obtained similar and better performances than the QRs and that the SLRs performed better when the datasets were smaller. Therefore, (1) QRs were not found to be an alternative to ANNs, (2) it could not be confirmed whether ANNs performed better than SLRs when assessing properties in Catalonia and (3) whereas small and medium banks should use SLRs, large banks should use either SLRs or ANNs in real estate mass appraisal