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Risks
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Financial Networks in Fintech Risk Management II
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Financial Networks in Fintech Risk Management II

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (30 May 2021) | Viewed by 17324

Special Issue Editor

Prof. Dr. Paolo Giudici

E-Mail Website
Guest Editor
Department of Economics and Management, University of Pavia, 27100 Pavia, Italy
Interests: financial data science; graphical models; network models; financial networks; systemic risk; financial risk management; fintech risk management; explainable artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

FinTech (Financial Technology) means “technology-enabled financial innovation”. Examples of FinTech innovations are peer to peer lending, robot advisory, and blockchain innovative and crypto payments. While FinTech innovations are competitive and bring higher financial inclusion, along with improved user experience, they also increase risks and, particularly, financial risks (credit, market, systemic, cyber, and operational risks).

There is a strong need to improve the competitiveness of FinTech innovations, creating a common regulatory approach across all countries that can make FinTech innovation sustainable. This can help to encourage innovations in the financial industry, in the application of big data, artificial intelligence, and blockchain technologies, while authorities and researchers assess their risks.

I believe that FinTech innovations themselves generate the data which can be leveraged to build appropriate FinTech risk management models. FinTech innovations generate “alternative” data: peer-to-peer financial and social transactions among the users. This source of data can be usefully analyzed, by means of network models. to complement and/or substitute banking and financial data employed in traditional risk management models, leading to more explainable results and more accurate predictions, eventually leading to more trust in FinTech innovations.

This Special Issue aims to collect original papers that contribute to the development of new fintech risk management models, based on the modeling of alternative data by means of network models.

This issue is a continuation of the previous Special Issue “Financial Networks in Fintech Risk Management”.

Prof. Dr. Paolo Giudici
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. Risks 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 1800 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

  • Fintech risk management
  • Peer to Peer data
  • Network models
  • Explainable artificial intelligence
  • Predictive accuracy

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Published Papers (4 papers)

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Research

25 pages, 2662 KiB  
Article
Smart Beta Allocation and Macroeconomic Variables: The Impact of COVID-19
by Matteo Foglia, Maria Cristina Recchioni and Gloria Polinesi
Risks 2021, 9(2), 34; https://doi.org/10.3390/risks9020034 - 4 Feb 2021
Cited by 3 | Viewed by 3827
Abstract
Smart beta strategies across economic regimes seek to address inefficiencies created by market-based indices, thereby enhancing portfolio returns above traditional benchmarks. Our goal is to develop a strategy for re-hedging smart beta portfolios that shows the connection between multi-factor strategies and macroeconomic variables. [...] Read more.
Smart beta strategies across economic regimes seek to address inefficiencies created by market-based indices, thereby enhancing portfolio returns above traditional benchmarks. Our goal is to develop a strategy for re-hedging smart beta portfolios that shows the connection between multi-factor strategies and macroeconomic variables. This is done, first, by analyzing finite correlations between the portfolio weights and macroeconomic variables and, more remarkably, by defining an investment tilting variable. The latter is analyzed with a discriminant analysis approach with a twofold application. The first is the selection of the crucial re-hedging thresholds which generate a strong connection between factors and macroeconomic variables. The second is forecasting portfolio dynamics (gain and loss). The capability of forecasting is even more evident in the COVID-19 period. Analysis is carried out on the iShares US exchange traded fund (ETF) market using monthly data in the period December 2013–May 2020, thereby highlighting the impact of COVID-19. Full article
(This article belongs to the Special Issue Financial Networks in Fintech Risk Management II)
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9 pages, 479 KiB  
Article
Why to Buy Insurance? An Explainable Artificial Intelligence Approach
by Alex Gramegna and Paolo Giudici
Risks 2020, 8(4), 137; https://doi.org/10.3390/risks8040137 - 14 Dec 2020
Cited by 33 | Viewed by 5759
Abstract
We propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost [...] Read more.
We propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost predictive classification algorithm. Our proposed method can be embedded into a technologically-based insurance service (Insurtech), allowing to understand, in real time, the factors that most contribute to customers’ decisions, thereby gaining proactive insights on their needs. We prove the validity of our model with an empirical analysis that was conducted on data regarding purchases of insurance micro-policies. Two aspects are investigated: the propensity to buy an insurance policy and the risk of churn of an existing customer. The results from the analysis reveal that customers can be effectively and quickly grouped according to a similar set of characteristics, which can predict their buying or churn behaviour well. Full article
(This article belongs to the Special Issue Financial Networks in Fintech Risk Management II)
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14 pages, 2071 KiB  
Article
Least Quartic Regression Criterion to Evaluate Systematic Risk in the Presence of Co-Skewness and Co-Kurtosis
by Giuseppe Arbia, Riccardo Bramante and Silvia Facchinetti
Risks 2020, 8(3), 95; https://doi.org/10.3390/risks8030095 - 8 Sep 2020
Cited by 4 | Viewed by 3449
Abstract
This article proposes a new method for the estimation of the parameters of a simple linear regression model which is based on the minimization of a quartic loss function. The aim is to extend the traditional methodology, based on the normality assumption, to [...] Read more.
This article proposes a new method for the estimation of the parameters of a simple linear regression model which is based on the minimization of a quartic loss function. The aim is to extend the traditional methodology, based on the normality assumption, to also take into account higher moments and to provide a measure for situations where the phenomenon is characterized by strong non-Gaussian distribution like outliers, multimodality, skewness and kurtosis. Although the proposed method is very general, along with the description of the methodology, we examine its application to finance. In fact, in this field, the contribution of the co-moments in explaining the return-generating process is of paramount importance when evaluating the systematic risk of an asset within the framework of the Capital Asset Pricing Model. We also illustrate a Monte Carlo test of significance on the estimated slope parameter and an application of the method based on the top 300 market capitalization components of the STOXX® Europe 600. A comparison between the slope coefficients evaluated using the ordinary Least Squares (LS) approach and the new Least Quartic (LQ) technique shows that the perception of market risk exposure is best captured by the proposed estimator during market turmoil, and it seems to anticipate the market risk increase typical of these periods. Moreover, by analyzing the out-of-sample risk-adjusted returns we show that the proposed method outperforms the ordinary LS estimator in terms of the most common performance indices. Finally, a bootstrap analysis suggests that significantly different Sharpe ratios between LS and LQ yields and Value at Risk estimates can be considered more accurate in the LQ framework. This study adds insights into market analysis and helps in identifying more precisely potentially risky assets whose extreme behavior is strongly dependent on market behavior. Full article
(This article belongs to the Special Issue Financial Networks in Fintech Risk Management II)
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Graphical abstract

17 pages, 613 KiB  
Article
Tail Risk Transmission: A Study of the Iran Food Industry
by Fatemeh Mojtahedi, Seyed Mojtaba Mojaverian, Daniel F. Ahelegbey and Paolo Giudici
Risks 2020, 8(3), 78; https://doi.org/10.3390/risks8030078 - 20 Jul 2020
Cited by 1 | Viewed by 3504
Abstract
This paper extends the extreme downside correlation (EDC) and extreme downside hedge (EDH) methodology to model the interdependence in the sensitivity of assets to the downside risk of other financial assets under severe firm-level and market conditions. The model is applied to analyze [...] Read more.
This paper extends the extreme downside correlation (EDC) and extreme downside hedge (EDH) methodology to model the interdependence in the sensitivity of assets to the downside risk of other financial assets under severe firm-level and market conditions. The model is applied to analyze both systematic and systemic exposures in the Iranian Food Industry. The empirical application investigates (1) which company is the safest for investors to diversify their investment, and (2) which companies are the “transmitters” and “receivers” of downside risk. We study the return series of 11 companies and the Food Industry index publicly listed on the Tehran Stock Exchange. The data covers daily close prices from 2015–2020. The result shows that Mahram Manufacturing is the safest to hedge equity risk, and Glucosan and Behshahr Industries are the riskiest, while Gorji Biscuit is central to risk transmission, and Pegah Fars Diary is the main “receiver” of risk in turbulent times. Full article
(This article belongs to the Special Issue Financial Networks in Fintech Risk Management II)
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