Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKittawit Autchariyapanitkulen_US
dc.contributor.authorSomsak Chanaimen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.contributor.authorThierry Denoeuxen_US
dc.description.abstract© Springer International Publishing Switzerland 2014. We consider an inference method for prediction based on belief functions in quantile regression with an asymmetric Laplace distribution. Specifically, we apply this method to the capital asset pricing model to estimate the beta coefficient and measure volatility under various market conditions at given levels of quantile. Likelihood-based belief functions are calculated from historical data of the securities in the S&P500 market. The results give us evidence on the systematic risk, in the form of a consonant belief function specified from the asymmetric Laplace distribution likelihood function given recorded data. Finally, we use the method to forecast the return of an individual stock.en_US
dc.subjectComputer Scienceen_US
dc.titlePredicting stock returns in the capital asset pricing model using quantile regression and belief functionsen_US
dc.typeBook Seriesen_US
article.title.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
article.volume8764en_US Mai Universityen_US de Technologie de Compiegneen_US
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.

Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.