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Innovative Financial Econometrics and Machine Learning
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Innovative Financial Econometrics and Machine Learning

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

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 6818

Special Issue Editor

Prof. Dr. Theo Berger

E-Mail Website
Guest Editor
1. Faculty of Business Studies and Economics, University of Bremen, 28359 Bremen, Germany
2. Statistics and Data Analytics, University of Applied Science Harz, 38855 Wernigerode, Germany
Interests: financial econometrics; explainable machine learning

Special Issue Information

Dear Colleagues,

The focus of this Special Issue “Innovative Financial Econometrics and Machine Learning” is the use of machine learning and econometric techniques in the field of data science by financial institutions. This includes explainable machine learning, fraud detection, forecasting, risk modelling, and data driven risk management.

We are interested in theoretical and empirical articles on the application of novel techniques in financial econometrics, machine learning, natural language processing, deep learning, and outlier detection with applications to portfolio management, fraud detection, risk measurement and risk management.

Prof. Dr. Theo Berger
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

  • financial econometrics
  • explainable machine learning
  • forecasting
  • fraud detection
  • risk modelling

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Published Papers (2 papers)

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Research

25 pages, 938 KiB  
Article
Stochastic Conditional Duration Model with Intraday Seasonality and Limit Order Book Information
by Tomoki Toyabe and Teruo Nakatsuma
J. Risk Financial Manag. 2022, 15(10), 470; https://doi.org/10.3390/jrfm15100470 - 17 Oct 2022
Cited by 1 | Viewed by 1516
Abstract
It is a widely known fact that the intraday seasonality of trading intervals for financial transactions such as stocks is short at the beginning of business hours and long in the middle of the day. In this paper, we extend the stochastic conditional [...] Read more.
It is a widely known fact that the intraday seasonality of trading intervals for financial transactions such as stocks is short at the beginning of business hours and long in the middle of the day. In this paper, we extend the stochastic conditional duration (SCD) model to capture the pattern of intraday trading intervals and propose a new Markov chain Monte Carlo method to estimate this intraday seasonality simultaneously. To efficiently generate the Monte Carlo sample, we used a hybrid of the Gibbs/Metropolis–Hastings (MH) sampling scheme and also applied generalized Gibbs sampling. In addition to capturing this intraday seasonality, this paper also considers limit order book information. Three-day tick data for three stocks obtained from Nikkei NEEDS are used for estimation, and model selection is performed on smooth parameters, Weibull distribution and Gamma distribution. The typical intraday regularity of frequent trading immediately after the start of trading is confirmed, and the spread of the limit order book information is also found to affect the trading time interval. Full article
(This article belongs to the Special Issue Innovative Financial Econometrics and Machine Learning)
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19 pages, 750 KiB  
Article
A Machine Learning Framework towards Bank Telemarketing Prediction
by Stéphane Cédric Koumétio Tékouabou, Ştefan Cristian Gherghina, Hamza Toulni, Pedro Neves Mata, Mário Nuno Mata and José Moleiro Martins
J. Risk Financial Manag. 2022, 15(6), 269; https://doi.org/10.3390/jrfm15060269 - 16 Jun 2022
Cited by 10 | Viewed by 4800
Abstract
The use of machine learning (ML) methods has been widely discussed for over a decade. The search for the optimal model is still a challenge that researchers seek to address. Despite advances in current work that surpass the limitations of previous ones, research [...] Read more.
The use of machine learning (ML) methods has been widely discussed for over a decade. The search for the optimal model is still a challenge that researchers seek to address. Despite advances in current work that surpass the limitations of previous ones, research still faces new challenges in every field. For the automatic targeting of customers in a banking telemarketing campaign, the use of ML-based approaches in previous work has not been able to show transparency in the processing of heterogeneous data, achieve optimal performance or use minimal resources. In this paper, we introduce a class membership-based (CMB) classifier which is a transparent approach well adapted to heterogeneous data that exploits nominal variables in the decision function. These dummy variables are often either suppressed or coded in an arbitrary way in most works without really evaluating their impact on the final performance of the models. In many cases, their coding either favours or disfavours the learning model performance without necessarily reflecting reality, which leads to over-fitting or decreased performance. In this work, we applied the CMB approach to data from a bank telemarketing campaign to build an optimal model for predicting potential customers before launching a campaign. The results obtained suggest that the CMB approach can predict the success of future prospecting more accurately than previous work. Furthermore, in addition to its better performance in terms of accuracy (97.3%), the model also gives a very close score for the AUC (95.9%), showing its stability, which would be very unfavourable to over-fitting. Full article
(This article belongs to the Special Issue Innovative Financial Econometrics and Machine Learning)
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