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Complexity in Finance

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 2329

Special Issue Editor

School of Finance, Nankai University, Tianjin 300350, China
Interests: empirical asset pricing; complexity system in finance; fin-tech; financial big data analysis; behavioral finance

Special Issue Information

Dear Colleagues,

Financial markets are regarded as complex systems comprising large numbers of interactive and adaptive agents. The increasing scale of financial markets and the resulting financial crisis highlight the importance of understanding the complex evolution dynamics of financial systems as well as providing appropriate risk management measures. Indeed, the mechanisms of complex financial systems are difficult to model and have an unpredictable human behavior  that sets them aside from complex natural and technological systems . Recent development of information technology makes it possible to record and restore data sets with a large volume, high frequency, and large dimensions. Notably, examining and investigating how complex systems behave in the field of finance through integrating big data with machine learning and other Fin-tech techniques provide new insights, and thus attract more attention from researchers. This Special Issue aims to present a collection of high-quality articles that provide new insights and advances regarding the complexity in financial markets. The research methods used in these studies can be based on machine learning, natural language processing and textual analysis, agent-based modelling, and traditional econometric models integrated with financial big data. Other empirical or theoretical approaches to complexity in financial markets will also be considered.

Potential topics include, but are not limited to, the following:

  • Big data fractality and multifractality in financial markets;
  • Complex financial systems;
  • Crisis and financial markets;
  • Digital finance;
  • Efficient market hypothesis and asset pricing;
  • Information theory and financial markets;
  • Interactions between financial assets;
  • Market dynamics and agent-based modelling;
  • Natural language processing and textual analysis;
  • Social media networks and financial markets;
  • Systemic risks.

Dr. Xiao Li
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. Entropy 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 2600 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

  • complex systems
  • financial markets
  • financial markets dynamics
  • financial risk management and modelling
  • Fin-tech
  • financial big data analysis
  • machine leaning

Published Papers (2 papers)

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Research

23 pages, 841 KiB  
Article
Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation
by Martin Heßler, Tobias Wand and Oliver Kamps
Entropy 2023, 25(9), 1265; https://doi.org/10.3390/e25091265 - 26 Aug 2023
Cited by 1 | Viewed by 1039
Abstract
Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understanding the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis, which solves significant memory and computing time [...] Read more.
Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understanding the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis, which solves significant memory and computing time limitations to extract crisis information from a correlation metric. Therefore, we focus on the recently investigated S&P500 mean market correlation in a period of roughly 20 years that includes the dot-com bubble, the global financial crisis, and the Euro crisis. The analysis is performed two-fold: first, in retrospect on the whole dataset and second, in an online adaptive manner in pre-crisis segments. The online sensitivity horizon is roughly determined to be 80 up to 100 trading days after a crisis onset. A detailed comparison to global economic events supports the interpretation of the mean market correlation as an informative macroeconomic measure by a rather good agreement of change point distributions and major crisis events. Furthermore, the results hint at the importance of the U.S. housing bubble as a trigger of the global financial crisis, provide new evidence for the general reasoning of locally (meta)stable economic states, and could work as a comparative impact rating of specific economic events. Full article
(This article belongs to the Special Issue Complexity in Finance)
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21 pages, 2886 KiB  
Article
Memory Effects, Multiple Time Scales and Local Stability in Langevin Models of the S&P500 Market Correlation
by Tobias Wand, Martin Heßler and Oliver Kamps
Entropy 2023, 25(9), 1257; https://doi.org/10.3390/e25091257 - 24 Aug 2023
Cited by 2 | Viewed by 881
Abstract
The analysis of market correlations is crucial for optimal portfolio selection of correlated assets, but their memory effects have often been neglected. In this work, we analyse the mean market correlation of the S&P500, which corresponds to the main market mode in principle [...] Read more.
The analysis of market correlations is crucial for optimal portfolio selection of correlated assets, but their memory effects have often been neglected. In this work, we analyse the mean market correlation of the S&P500, which corresponds to the main market mode in principle component analysis. We fit a generalised Langevin equation (GLE) to the data whose memory kernel implies that there is a significant memory effect in the market correlation ranging back at least three trading weeks. The memory kernel improves the forecasting accuracy of the GLE compared to models without memory and hence, such a memory effect has to be taken into account for optimal portfolio selection to minimise risk or for predicting future correlations. Moreover, a Bayesian resilience estimation provides further evidence for non-Markovianity in the data and suggests the existence of a hidden slow time scale that operates on much slower times than the observed daily market data. Assuming that such a slow time scale exists, our work supports previous research on the existence of locally stable market states. Full article
(This article belongs to the Special Issue Complexity in Finance)
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