Econophysics Applications to Financial Markets

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

Deadline for manuscript submissions: closed (24 April 2020) | Viewed by 22655

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VALORIZA—Research Center for Endogenous Resource Valorization; Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
Interests: econophysics; financial markets; time series analysis; financial contagion; financial integration
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Special Issue Information

Dear Colleagues,

Financial markets are recognized as complex systems, considering the way they behave, how agents interact, and their dynamics. For example, the existence of crises and bubbles and mainly the fact that much of them remain unexplained means that the study of financial markets is never complete. Moreover, the field’s impact on the real economy accentuates the importance of continuously studying financial markets. Despite the fact that physicists’ interest is not new, it seems that in the 1990s interest grew, with many researchers applying statistical physics methods to financial data. The term Econophysics was originally applied by H. Eugene Stanley, with the objective of calling attention to the high number or papers that at the date were already published. Since then, the number of publications has increased significantly, and Econophysics is now recognized as an independent research area. In a context where so much about financial markets remains unexplained, where technological advances make financial markets more available (inclusively creating new financial services), where financial markets are, in general, more interconnected, it remains important to keep the focus on the study of those markets. Thus, in this Special Issue, it is proposed to examine and attempt to understand the behavior of financial markets, based on the use of methods coined with Econophysics.

Dr. Paulo Ferreira
Guest Editor

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Published Papers (5 papers)

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Research

13 pages, 483 KiB  
Article
The Determinants of the U.S. Consumer Sentiment: Linear and Nonlinear Models
by Marwane El Alaoui, Elie Bouri and Nehme Azoury
Int. J. Financial Stud. 2020, 8(3), 38; https://doi.org/10.3390/ijfs8030038 - 1 Jul 2020
Cited by 10 | Viewed by 3631
Abstract
We examined the determinants of the U.S. consumer sentiment by applying linear and nonlinear models. The data are monthly from 2009 to 2019, covering a large set of financial and nonfinancial variables related to the stock market, personal income, confidence, education, environment, sustainability, [...] Read more.
We examined the determinants of the U.S. consumer sentiment by applying linear and nonlinear models. The data are monthly from 2009 to 2019, covering a large set of financial and nonfinancial variables related to the stock market, personal income, confidence, education, environment, sustainability, and innovation freedom. We show that more than 8.3% of the total of eigenvalues deviate from the Random Matrix Theory (RMT) and might contain pertinent information. Results from linear models show that variables related to the stock market, confidence, personal income, and unemployment explain the U.S. consumer sentiment. To capture nonlinearity, we applied the switching regime model and showed a switch towards a more positive sentiment regarding energy efficiency, unemployment rate, student loan, sustainability, and business confidence. We additionally applied the Gradient Descent Algorithm to compare the errors obtained in linear and nonlinear models, and the results imply a better model with a high predictive power. Full article
(This article belongs to the Special Issue Econophysics Applications to Financial Markets)
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13 pages, 1114 KiB  
Article
Evidence of Intraday Multifractality in European Stock Markets during the Recent Coronavirus (COVID-19) Outbreak
by Faheem Aslam, Wahbeeah Mohti and Paulo Ferreira
Int. J. Financial Stud. 2020, 8(2), 31; https://doi.org/10.3390/ijfs8020031 - 26 May 2020
Cited by 63 | Viewed by 6729
Abstract
This study assesses how the coronavirus pandemic (COVID-19) affects the intraday multifractal properties of eight European stock markets by using five-minute index data ranging from 1 January 2020 to 23 March 2020. The Hurst exponents are calculated by applying multifractal detrended fluctuation analysis [...] Read more.
This study assesses how the coronavirus pandemic (COVID-19) affects the intraday multifractal properties of eight European stock markets by using five-minute index data ranging from 1 January 2020 to 23 March 2020. The Hurst exponents are calculated by applying multifractal detrended fluctuation analysis (MFDFA). Overall, the results confirm the existence of multifractality in European stock markets during the COVID-19 outbreak. Furthermore, based on multifractal properties, efficiency varies among these markets. The Spanish stock market remains most efficient while the least efficient is that of Austria. Belgium, Italy and Germany remain somewhere in the middle. This far-reaching outbreak demands a comprehensive response from policy makers to improve market efficiency during such epidemics. Full article
(This article belongs to the Special Issue Econophysics Applications to Financial Markets)
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9 pages, 497 KiB  
Article
Efficiency of the Brazilian Bitcoin: A DFA Approach
by Derick Quintino, Jessica Campoli, Heloisa Burnquist and Paulo Ferreira
Int. J. Financial Stud. 2020, 8(2), 25; https://doi.org/10.3390/ijfs8020025 - 20 Apr 2020
Cited by 5 | Viewed by 3771
Abstract
Bitcoin’s evolution has attracted the attention of investors and researchers looking for a better understanding of the efficiency of cryptocurrency markets, considering their prices and volatility. The purpose of this paper is to contribute to this understanding by studying the degree of persistence [...] Read more.
Bitcoin’s evolution has attracted the attention of investors and researchers looking for a better understanding of the efficiency of cryptocurrency markets, considering their prices and volatility. The purpose of this paper is to contribute to this understanding by studying the degree of persistence of the Bitcoin measured by the Hurst exponent, considering prices from the Brazilian market, and comparing with Bitcoin in USD as a benchmark. We applied Detrended Fluctuation Analysis (DFA), for the period from 9 April 2017 to 30 June 2018, using daily closing prices, with a total of 429 observations. We focused on two prices of Bitcoins resulting from negotiations made by two different Brazilian financial institutions: Foxbit and Mercado. The results indicate that Mercado and Foxbit returns tend to follow Bitcoin dynamics and all of them show persistent behavior, although the persistence in slightly higher for the Brazilian Bitcoin. However, this evidence does not necessarily mean opportunities for abnormal profits, as aspects such as liquidity or transaction costs could be impediments to this occurrence. Full article
(This article belongs to the Special Issue Econophysics Applications to Financial Markets)
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27 pages, 357 KiB  
Article
Financial Variables, Market Transactions, and Expectations as Functions of Risk
by Victor Olkhov
Int. J. Financial Stud. 2019, 7(4), 66; https://doi.org/10.3390/ijfs7040066 - 4 Nov 2019
Cited by 2 | Viewed by 3882
Abstract
This paper develops methods and a framework of financial market theory. We model financial markets as a system of agents which perform market transactions with other agents under the action of numerous expectations. Agents’ expectations are formed of economic and financial variables, market [...] Read more.
This paper develops methods and a framework of financial market theory. We model financial markets as a system of agents which perform market transactions with other agents under the action of numerous expectations. Agents’ expectations are formed of economic and financial variables, market transactions, the expectations of other agents, and other factors that impact financial markets. We use the risk ratings of agents as their coordinates and approximate a description of financial variables, market transactions, and expectations of numerous separate agents by density functions of aggregated agents in the economic domain. The motion of separate agents in the economic domain due to a change of agents’ risk rating produces collective financial flows of variables, transactions, and expectations. We derive equations on collective financial variables, market transactions, expectations, and their flows in the economic domain. These flows define the evolution of financial markets. As an example, we present a simple model with linear dependence between disturbances of volume and the cost of transactions on one hand, and disturbances of expectations that determine transactions on the other hand. Our model describes harmonique oscillations of these disturbances with numerous frequencies and allows an explicit form for fluctuations of price and return to be derived. These relations show a direct dependence between price, return, and volume perturbations. Full article
(This article belongs to the Special Issue Econophysics Applications to Financial Markets)
12 pages, 1632 KiB  
Article
Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies
by Natália Costa, César Silva and Paulo Ferreira
Int. J. Financial Stud. 2019, 7(3), 51; https://doi.org/10.3390/ijfs7030051 - 15 Sep 2019
Cited by 16 | Viewed by 3301
Abstract
In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We [...] Read more.
In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation. Full article
(This article belongs to the Special Issue Econophysics Applications to Financial Markets)
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