Special Issue "UoM Meeting 2018: International PhD meeting in Economics"

A special issue of Forecasting (ISSN 2571-9394).

Deadline for manuscript submissions: closed (31 October 2018)

Special Issue Editor

Guest Editor
Dr. Theodore Panagiotidis

Department of Economics, University of Macedonia, Thessaloniki, Greece
Website | E-Mail
Interests: econometrics; time series econometrics; macroeconometrics; financial econometrics; macro-finance

Special Issue Information

Dear Colleagues,

We cordially invite you to contribute to our Special Issue of Forecasting on the theme of "Advances in Time Series and Forecasting".

The main goal of this Special Issue, “Special Issue of UoM Meeting 2018”, is to advance new science and methodologies in the fields of time series and forecasting in economics and finance.

UoM Meeting 2018 (International PhD meeting in Economics) seeks to provide a discussion forum for researchers, educators and PhD students regarding the latest ideas in the foundations, theory, models and applications of time series and forecasting. The focus will be on applications in economics and finance.

Topics of interest for this Special Issue include, but are not limited to:

  • Causality analysis
  • Time series analysis and forecasting
  • Economic and econometric forecasting
  • Advanced methods and on-line learning in time series
  • High dimension and complex/big data forecasting
  • Forecasting in finance and macroeconomics

Dr. Theodore Panagiotidis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

Open AccessArticle Oil Market Efficiency under a Machine Learning Perspective
Forecasting 2018, 1(1), 157-168; https://doi.org/10.3390/forecast1010011
Received: 17 September 2018 / Revised: 10 October 2018 / Accepted: 11 October 2018 / Published: 13 October 2018
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Abstract
Forecasting commodities and especially oil prices have attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market, attempting to forecast the
[...] Read more.
Forecasting commodities and especially oil prices have attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market, attempting to forecast the West Texas Intermediate oil prices under a machine learning framework. In doing so, we compile a dataset of 38 potential explanatory variables that are often used in the relevant literature. Next, through a selection process, we build forecasting models that use past oil prices, refined oil products and exchange rates as independent variables. Our empirical findings suggest that the Support Vector Machines (SVM) model coupled with the non-linear Radial Basis Function kernel outperforms the linear SVM and the traditional logistic regression (LOGIT) models. Moreover, we provide evidence that points to the rejection of even the weak form of efficiency in the oil market. Full article
(This article belongs to the Special Issue UoM Meeting 2018: International PhD meeting in Economics)
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