Special Issue "Financial Econometrics and Machine Learning"

A special issue of International Journal of Financial Studies (ISSN 2227-7072).

Deadline for manuscript submissions: 31 December 2023 | Viewed by 3166

Special Issue Editors

Dr. Sahbi Farhani
E-Mail Website
Guest Editor
Higher Institute of Finance and Taxation of Sousse (ISFFS), University of Sousse, Sousse, Tunisia
Interests: energy economics; sustainability; environmental degree of pollution; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Ali Nasir
E-Mail Website
Guest Editor
Department of Economics, Leeds Business School, University of Leeds, Leeds, UK
Interests: economics and finance

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to identify challenges and solutions for machine learning that contribute to financial econometrics with methods that find functional forms of models in a manner that includes artificial intelligence. It covers papers on complex topics dealing with:

  • Prediction for financial forecasting;
  • Asset pricing, corporate finance, international finance, options and futures, risk management, and stress testing for financial institutions;
  • Single-equation multiple regression, simultaneous equation regression, and panel data analysis, among others;
  • Computer technology in financial research (different computer languages and programming techniques used for empirical research in finance);
  • Simulation, machine learning, big data, and financial payments.

A part of this SI will be devoted to the top selected papers that come to be presented in The 1st Conference of the Association for Quantitative Economic Research “AQuER Conf’22”, March 24–26, 2022, Hammamet, Tunisia.

Co-chairs: Prof. Dr. Slim Ben Youssef and Dr. Sahbi Farhani (www.aquer.org).

Dr. Sahbi Farhani
Dr. Muhammad Ali Nasir
Guest Editors

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. International Journal of Financial Studies 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) 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 forecasting
  • asset pricing
  • corporate finance
  • international finance
  • risk management
  • computer technology in financial research
  • simulation, machine learning, big data, and financial payments

Published Papers (3 papers)

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Research

Article
Improving Returns on Strategy Decisions through Integration of Neural Networks for the Valuation of Asset Pricing: The Case of Taiwanese Stock
Int. J. Financial Stud. 2022, 10(4), 99; https://doi.org/10.3390/ijfs10040099 - 27 Oct 2022
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Abstract
Most of the growth forecasts in analysts’ evaluation reports rely on human judgment, which leads to the occurrence of bias. A back-propagation neural network (BPNN) is a financial technique that learns a multi-layer feedforward network. This study aims to integrate BPNN and asset [...] Read more.
Most of the growth forecasts in analysts’ evaluation reports rely on human judgment, which leads to the occurrence of bias. A back-propagation neural network (BPNN) is a financial technique that learns a multi-layer feedforward network. This study aims to integrate BPNN and asset pricing models to avoid artificial forecasting errors. In terms of evaluation, financial statements and investor attention were used in this case study, demonstrating that modern analysts should incorporate the evaluation advantages of big data to provide more reasonable and rational investment reports. We found that assessments of revenue, index returns, and investor attention suggest that stock prices are prone to undervaluation The levels of risk-taking behaviors were used in the classification of robustness analysis. This study showed that when betas range from 1% to 5%, both risk-taking levels of investors can hold buying strategies for the long term. However, for lower risk-taking preferences, only when the change exceeds 10 percent, the stock price is prone to overvaluation, indicating that investors can sell or adopt a more cautious investment strategy. Full article
(This article belongs to the Special Issue Financial Econometrics and Machine Learning)
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Article
A Deep Learning Approach to Dynamic Interbank Network Link Prediction
Int. J. Financial Stud. 2022, 10(3), 54; https://doi.org/10.3390/ijfs10030054 - 12 Jul 2022
Viewed by 806
Abstract
Lehman Brothers’ failure in 2008 demonstrated the importance of understanding interconnectedness in interbank networks. The interbank market plays a significant role in facilitating market liquidity and providing short-term funding for each other to smooth liquidity shortages. Knowing the trading relationship could also help [...] Read more.
Lehman Brothers’ failure in 2008 demonstrated the importance of understanding interconnectedness in interbank networks. The interbank market plays a significant role in facilitating market liquidity and providing short-term funding for each other to smooth liquidity shortages. Knowing the trading relationship could also help understand risk contagion among banks. Therefore, future lending relationship prediction is important to understand the dynamic evolution of interbank networks. To achieve the goal, we apply a deep learning framework model of interbank lending to an electronic trading interbank network for temporal trading relationship prediction. There are two important components of the model, which are the Graph convolutional network (GCN) and the Long short-term memory (LSTM) model. The GCN and LSTM components together capture the spatial–temporal information of the dynamic network snapshots. Compared with the Discrete autoregressive model and Dynamic latent space model, our proposed model achieves better performance in both the precrisis and the crisis period. Full article
(This article belongs to the Special Issue Financial Econometrics and Machine Learning)
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Article
Cryptocurrencies Intraday High-Frequency Volatility Spillover Effects Using Univariate and Multivariate GARCH Models
Int. J. Financial Stud. 2022, 10(3), 51; https://doi.org/10.3390/ijfs10030051 - 08 Jul 2022
Viewed by 855
Abstract
Over the past years, cryptocurrencies have drawn substantial attention from the media while attracting many investors. Since then, cryptocurrency prices have experienced high fluctuations. In this paper, we forecast the high-frequency 1 min volatility of four widely traded cryptocurrencies, i.e., Bitcoin, Ethereum, Litecoin, [...] Read more.
Over the past years, cryptocurrencies have drawn substantial attention from the media while attracting many investors. Since then, cryptocurrency prices have experienced high fluctuations. In this paper, we forecast the high-frequency 1 min volatility of four widely traded cryptocurrencies, i.e., Bitcoin, Ethereum, Litecoin, and Ripple, by modeling volatility to select the best model. We propose various generalized autoregressive conditional heteroscedasticity (GARCH) family models, including an sGARCH(1,1), GJR-GARCH(1,1), TGARCH(1,1), EGARCH(1,1), which we compare to a multivariate DCC-GARCH(1,1) model to forecast the intraday price volatility. We evaluate the results under the MSE and MAE loss functions. Statistical analyses demonstrate that the univariate GJR-GARCH model (1,1) shows a superior predictive accuracy at all horizons, followed closely by the TGARCH(1,1), which are the best models for modeling the volatility process on out-of-sample data and have more accurately indicated the asymmetric incidence of shocks in the cryptocurrency market. The study determines evidence of bidirectional shock transmission effects between the cryptocurrency pairs. Hence, the multivariate DCC-GARCH model can identify the cryptocurrency market’s cross-market volatility shocks and volatility transmissions. In addition, we introduce a comparison of the models using the improvement rate (IR) metric for comparing models. As a result, we compare the different forecasting models to the chosen benchmarking model to confirm the improvement trends for the model’s predictions. Full article
(This article belongs to the Special Issue Financial Econometrics and Machine Learning)
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