Financial Valuation and Econometrics

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Economics and Finance".

Deadline for manuscript submissions: 31 March 2024 | Viewed by 2214

Special Issue Editor

Dr. Marius Sikveland
E-Mail Website
Guest Editor
UiS Business School, University of Stavanger (UiS), Stavanger, Norway
Interests: financial performance; valuation; commodity prices

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the broad field of “Financial Valuation and Econometrics”. It aims to explore the latest advancements, theories, and applications in the field, providing a comprehensive understanding of how econometric techniques can help us understand financial valuation.

Dr. Marius Sikveland
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 submissions that pass pre-check are 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. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). 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.

Keywords

  • sustainability and valuation—the impact of ESG, green revenue, etc., on the value of firms;asset pricing models and climate-related factors
  • financial valuation
  • machine learning
  • financial ratios: valuation and prediction
  • financial modeling and forecasting
  • risk management and valuation

Published Papers (2 papers)

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Research

13 pages, 896 KiB  
Article
On Comparing and Assessing Robustness of Some Popular Non-Stationary BINAR(1) Models
J. Risk Financial Manag. 2024, 17(3), 100; https://doi.org/10.3390/jrfm17030100 - 28 Feb 2024
Viewed by 323
Abstract
Intra-day transactions of stocks from competing firms in the financial markets are known to exhibit significant volatility and over-dispersion. This paper proposes some bivariate integer-valued auto-regressive models of order 1 (BINAR(1)) that are useful to analyze such financial series. These models were constructed [...] Read more.
Intra-day transactions of stocks from competing firms in the financial markets are known to exhibit significant volatility and over-dispersion. This paper proposes some bivariate integer-valued auto-regressive models of order 1 (BINAR(1)) that are useful to analyze such financial series. These models were constructed under both time-variant and time-invariant conditions to capture features such as over-dispersion and non-stationarity in time series of counts. However, the quest for the most robust BINAR(1) models is still on. This paper considers specifically the family of BINAR(1)s with a non-diagonal cross-correlation structure and with unpaired innovation series. These assumptions relax the number of parameters to be estimated. Simulation experiments are performed to assess both the consistency of the estimators and the robust behavior of the BINAR(1)s under mis-specified innovation distribution specifications. The proposed BINAR(1)s are applied to analyze the intra-day transaction series of AstraZeneca and Ericsson. Diagnostic measures such as the root mean square errors (RMSEs) and Akaike information criteria (AICs) are also considered. The paper concludes that the BINAR(1)s with negative binomial and COM–Poisson innovations are among the most suitable models to analyze over-dispersed intra-day transaction series of stocks. Full article
(This article belongs to the Special Issue Financial Valuation and Econometrics)
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14 pages, 1387 KiB  
Article
Predicting the Profitability of Directional Changes Using Machine Learning: Evidence from European Countries
J. Risk Financial Manag. 2023, 16(12), 520; https://doi.org/10.3390/jrfm16120520 - 18 Dec 2023
Cited by 1 | Viewed by 1139
Abstract
In this paper, we follow the suggestions of past literature to further explore the prediction of the profitability direction by employing different machine learning algorithms, extending the research in the European setting and examining the effect of profits mean reversion for the prediction [...] Read more.
In this paper, we follow the suggestions of past literature to further explore the prediction of the profitability direction by employing different machine learning algorithms, extending the research in the European setting and examining the effect of profits mean reversion for the prediction of profitability. We provide evidence that simple algorithms like LDA can outperform classification trees if the data used are preprocessed correctly. Moreover, we use nested cross-validation and show that sample predictions can be obtained without using the classic train–test split. Overall, our prediction results are in line with previous studies, and we also found that cash flow-based measures like Free Cash Flow and Operating Cash Flow can be predicted more accurately compared to accrual-based measures like return on assets or return on equity. Full article
(This article belongs to the Special Issue Financial Valuation and Econometrics)
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