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The Use of Big Data in Finance
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The Use of Big Data in Finance

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: 16 September 2024 | Viewed by 10976

Special Issue Editor

Prof. Dr. Paulo Ferreira

E-Mail Website
Guest Editor
Department of Economic Sciences and Organizations, Portalegre Polytechnic University, 7300-555 Portalegre, Portugal
Interests: econophysics; financial markets; time series analysis; financial contagion; financial integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, we have huge sets of data available on financial assets, allowing for a wide range of analyses. Big data is a fast-growing field of research and is recognized as a very important framework in several areas, including finance, because it can describe complex datasets. This Special Issue is devoted to collecting original research on this hot topic, used to explain financial prices, returns, and volatility, but which could also be used for other purposes. Once we are able to achieve the recovery of great amounts of data, big data will allow us to analyze several phenomena, including different crises and the actual crisis caused by COVID-19. We invite you to contribute with your work.

Dr. Paulo Jorge Silveira Ferreira
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

  • Big data 
  • Financial markets 
  • Stock markets 
  • Econometrics 
  • Finance

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Published Papers (3 papers)

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Research

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17 pages, 1035 KiB  
Article
Models for Expected Returns with Statistical Factors
by José Manuel Cueto, Aurea Grané and Ignacio Cascos
J. Risk Financial Manag. 2020, 13(12), 314; https://doi.org/10.3390/jrfm13120314 - 8 Dec 2020
Cited by 5 | Viewed by 3322
Abstract
In this paper, we propose multifactor models for the pan-European Equity Market using a block-bootstrap method and compare the results with those of traditional inferential techniques. The new factors are built from statistical measurements on stock prices—in particular, coefficient of variation, skewness, and [...] Read more.
In this paper, we propose multifactor models for the pan-European Equity Market using a block-bootstrap method and compare the results with those of traditional inferential techniques. The new factors are built from statistical measurements on stock prices—in particular, coefficient of variation, skewness, and kurtosis. Data come from Reuters, correspond to nearly 2000 EU companies, and span from January 2008 to February 2018. Regarding methodology, we propose a non-parametric resampling procedure that accounts for time dependency in order to test the validity of the model and the significance of the parameters involved. We compare our bootstrap-based inferential results with classical proposals (based on F-statistics). Methods under assessment are time-series regression, cross-sectional regression, and the Fama–MacBeth procedure. The main findings indicate that the two factors that better improve the Capital Asset Pricing Model with regard to the adjusted R2 in the time-series regressions are the skewness and the coefficient of variation. For this reason, a model including those two factors together with the market is thoroughly studied. We also observe that our block-bootstrap methodology seems to be more conservative with the null of the GRS test than classical procedures. Full article
(This article belongs to the Special Issue The Use of Big Data in Finance)
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24 pages, 1171 KiB  
Article
Early Warning Signs of Financial Market Turmoils
by Nils Bertschinger and Oliver Pfante
J. Risk Financial Manag. 2020, 13(12), 301; https://doi.org/10.3390/jrfm13120301 - 30 Nov 2020
Cited by 1 | Viewed by 2505
Abstract
Volatility clustering and fat tails are prominently observed in financial markets. Here, we analyze the underlying mechanisms of three agent-based models explaining these stylized facts in terms of market instabilities and compare them on empirical grounds. To this end, we first develop a [...] Read more.
Volatility clustering and fat tails are prominently observed in financial markets. Here, we analyze the underlying mechanisms of three agent-based models explaining these stylized facts in terms of market instabilities and compare them on empirical grounds. To this end, we first develop a general framework for detecting tail events in stock markets. In particular, we introduce Hawkes processes to automatically identify and date onsets of market turmoils which result in increased volatility. Second, we introduce three different indicators to predict those onsets. Each of the three indicators is derived from and tailored to one of the models, namely quantifying information content, critical slowing down or market risk perception. Finally, we apply our indicators to simulated and real market data. We find that all indicators reliably predict market events on simulated data and clearly distinguish the different models. In contrast, a systematic comparison on the stocks of the Forbes 500 companies shows a markedly lower performance. Overall, predicting the onset of market turmoils appears difficult, yet, over very short time horizons high or rising volatility exhibits some predictive power. Full article
(This article belongs to the Special Issue The Use of Big Data in Finance)
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Review

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10 pages, 336 KiB  
Review
From Big Data to Econophysics and Its Use to Explain Complex Phenomena
by Paulo Ferreira, Éder J.A.L. Pereira and Hernane B.B. Pereira
J. Risk Financial Manag. 2020, 13(7), 153; https://doi.org/10.3390/jrfm13070153 - 13 Jul 2020
Cited by 5 | Viewed by 4088
Abstract
Big data has become a very frequent research topic, due to the increase in data availability. In this introductory paper, we make the linkage between the use of big data and Econophysics, a research field which uses a large amount of data and [...] Read more.
Big data has become a very frequent research topic, due to the increase in data availability. In this introductory paper, we make the linkage between the use of big data and Econophysics, a research field which uses a large amount of data and deals with complex systems. Different approaches such as power laws and complex networks are discussed, as possible frameworks to analyze complex phenomena that could be studied using Econophysics and resorting to big data. Full article
(This article belongs to the Special Issue The Use of Big Data in Finance)
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