Performance Evaluation of Prediction Models Under Multiple Criteria : An application on crude oil prices volatility forecasting models

Workshop 2015 -Advances in DEA Theory and Applications (December 1-2, 2015)With the increasing number of quantitative models available to forecast the crude oil prices and its volatility, the assessment of the relative performance of competing models becomes a critical task. So far, competing forecasting models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria – a situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, we proposed a multidimensional framework based on Data Envelopment Analysis models to rank order competing forecasting models.The workshop is supported by JSPS (Japan Society for the Promotion of Science), Grant-in-Aid for Scientific Research (B), #25282090, titled “Studies in Theory and Applications of DEA for Forecasting Purpose.本研究はJSPS科研費 基盤研究(B) 25282090の助成を受けたものです

A dynamic performance evaluation of distress prediction models

YesSo far, the dominant comparative studies of competing distress prediction models (DPMs) have been restricted to the use of static evaluation frameworks and as such overlooked their performance over time. This study fills this gap by proposing a Malmquist Data Envelopment Analysis (DEA)-based multi-period performance evaluation framework for assessing competing static and dynamic statistical DPMs and using it to address a variety of research questions. Our findings suggest that (1) dynamic models developed under duration-dependent frameworks outperform both dynamic models developed under duration-independent frameworks and static models; (2) models fed with financial accounting (FA), market variables (MV), and macroeconomic information (MI) features outperform those fed with either MVMI or FA, regardless of the frameworks under which they are developed; (3) shorter training horizons seem to enhance the aggregate performance of both static and dynamic models

DEA scores’ confidence intervals with past-present and past-present-future based resampling

In data envelopment analysis (DEA), input and output values are subject to change for several reasons. Such variations differ in their input/output items and their decision-making units (DMUs). Hence, DEA efficiency scores need to be examined by considering these factors. In this paper, we propose new resampling models based on these variations for gauging the confidence intervals of DEA scores. The first model utilizes past-present data for estimating data variations imposing chronological order weights which are supplied by Lucas series (a variant of Fibonacci series). The second model deals with future prospects. This model aims at forecasting the future efficiency score and its confidence interval for each DMU. We applied our models to a dataset composed of Japanese municipal hospitals.http://www.grips.ac.jp/list/facultyinfo/tone_kaoru

A new classifier based on the reference point method with application in bankruptcy prediction

The finance industry relies heavily on the risk modelling and analysis toolbox to assess the risk profiles of entities such as individual and corporate borrowers and investment vehicles. Such toolbox includes a variety of parametric and nonparametric methods for predicting risk class belonging. In this paper, we expand such toolbox by proposing an integrated framework for implementing a full classification analysis based on a reference point method, namely in-sample classification and out-of-sample classification. The empirical performance of the proposed reference point method-based classifier is tested on a UK data set of bankrupt and nonbankrupt firms. Our findings conclude that the proposed classifier can deliver a very high predictive performance, which makes it a real contender in industry applications in banking and investment. Three main features of the proposed classifier drive its outstanding performance, namely its nonparametric nature, the design of our RPM score-based cut-off point procedure for in-sample classification, and the choice of a k-nearest neighbour as an out-of-sample classifier which is trained on the in-sample classification provided by the reference point method-based classifier.PostprintPeer reviewe

A complex networks based analysis of jump risk in equity returns:An evidence using intraday movements from Pakistan stock market

International audienceWe employ a multi-stage methodology combining complex network analytics and financial risk modelling to unveil the correlation structures amongst the price jump risks of companies forming the KSE-100 index in Pakistan. We identify the most influential companies in terms of jump risk, and identify communities — clusters of companies with similar price movement characteristics or with highly correlated price jumps. We find that equities in Pakistan stock market experience jumps in different time periods that are correlated to varying degrees within and across industries resulting in 19 different communities, four of which are strongly connected. While Oil & Gas, Cement and Banking sectors exhibit a significant representation of firms in communities, the automobile industry, however, seems to play an important role in risk propagation. These results provide an interesting insight to investors and other stakeholders from an emerging market viewpoint identifying the major sectors driving the volatility of KSE-100 index

The Long-Run Performance following Convertible Debt Offerings: Does The Design Matter?

International audienceThis paper examines the impact of convertible debt design on the long-run stock price performance of the issuing firms in France. More specifically, we divide French convertible bonds (CBs) into three categories; namely, debt-like, mixed, and equity-like CBs, based on their total conversion probability, which integrates the possibility of early exercise of the call feature. In line with previous empirical studies, our results show that French CB issuers experience a substantial increase in their stock price profitability before the offering followed by significant under-performance over the three year post-issue event window. However, the breakdown of our sample into three groups of CBs depending on their design reveals, on one hand, a strong evidence of stock price run-up before the offering only for equity-like and mixed CBs. On the other hand, the post-issue performance is worse only for equity-like issuers, indicating that the post-issue performance is poorer the more the convertible debt issuer's stock is over-valued prior to the offering. This finding is consistent with the market timing hypothesis