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Applications of Statistical Physics in Finance and Economics

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 13262

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Special Issue Editor

Prof. Dr. Jae Woo Lee

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Guest Editor

Department of Physics, Inha University, Incheon 22212, Republic of Korea
Interests: econophysics; complex systems; self-organized criticality; social physics; ecological networks

Special Issue Information

Dear Colleagues,

Statistical physics and complex science are applied to understand various problems in finance and economics and broaden the scope of understanding in these fields.

In the stock market, the distribution function of fluctuations and volatility of stock index shows a fat-tail distribution. Fluctuations in economic and financial time series represent fractal and multi-fractal structures. Heterogenous agents participating in the market sometimes act irrationally with limited information.

According to the global value chain, various products are imported or exported among countries to produce goods. An international trade network is formed while exchanging goods through the global trade system. Complex networks emerge in the stock market, foreign exchange, international trade, etc. Fluctuations in economic and financial time show a complex pattern and show a self-organized structure despite the absence of agents to coordinate the market. In the capitalist economic structure, the economic activities of agents show a natural inequality of wealth.

This Special Issue on “Applications of Statistical Physics in Finance and Economics” presents a platform where academic researchers can present methodologies, techniques, applications and experiments that aim to increase our understanding of econophysics and their emergent behaviors. The focus of this Special Issue is both on modelling and simulation techniques, but also on their practical application on various scenarios; as such, papers are welcome on a variety of topics including review, modelling, simulation, analysis, experimentation, and specific properties as defined above.

Prof. Dr. Jae Woo Lee
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

econophysics
stock market
wealth inequality
complex networks
product network
international trade network
value chain, multifractal

Published Papers (7 papers)

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Research

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24 pages, 3752 KiB

Open AccessArticle

Determinants of the European Sovereign Debt Crisis: Application of Logit, Panel Markov Regime Switching Model and Self Organizing Maps

by Jean-Pierre Allegret and Raif Cergibozan

Entropy 2023, 25(7), 1032; https://doi.org/10.3390/e25071032 - 08 Jul 2023

Viewed by 968

Abstract

The study aims to empirically identify the determinants of the debt crisis that occurred within the framework of 15 core EU member countries (EU-15). Contrary to previous empirical studies that tend to use event-based crisis indicators, our study develops a continuous fiscal stress index to identify the debt crises in the EU-15 and employs three different estimation techniques, namely self-organizing map, multivariate logit and panel Markov regime switching models. Our estimation results show first that the study correctly identifies the time and the length of the debt crisis in each EU-15-member country. Empirical results then indicate, via three different models, that the debt crisis in the EU-15 is the consequence of deterioration of both financial and macroeconomic variables such as nonperforming loans over total loans, GDP growth, unemployment rates, primary balance over GDP, and cyclically adjusted balance over GDP. Furthermore, variables measuring governance quality, such as voice and accountability, regulatory quality, and government effectiveness, also play a significant role in the emergence and the duration of the debt crisis in the EU-15. Full article

(This article belongs to the Special Issue Applications of Statistical Physics in Finance and Economics)

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23 pages, 3630 KiB

Open AccessArticle

Stock Index Spot–Futures Arbitrage Prediction Using Machine Learning Models

by Yankai Sheng and Ding Ma

Entropy 2022, 24(10), 1462; https://doi.org/10.3390/e24101462 - 13 Oct 2022

Cited by 1 | Viewed by 3510

Abstract

With the development of quantitative finance, machine learning methods used in the financial fields have been given significant attention among researchers, investors, and traders. However, in the field of stock index spot–futures arbitrage, relevant work is still rare. Furthermore, existing work is mostly retrospective, rather than anticipatory of arbitrage opportunities. To close the gap, this study uses machine learning approaches based on historical high-frequency data to forecast spot–futures arbitrage opportunities for the China Security Index (CSI) 300. Firstly, the possibility of spot–futures arbitrage opportunities is identified through econometric models. Then, Exchange-Traded-Fund (ETF)-based portfolios are built to fit the movements of CSI 300 with the least tracking errors. A strategy consisting of non-arbitrage intervals and unwinding timing indicators is derived and proven profitable in a back-test. In forecasting, four machine learning methods are adopted to predict the indicator we acquired, namely Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Long Short-Term Memory neural network (LSTM). The performance of each algorithm is compared from two perspectives. One is an error perspective based on the Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and goodness of fit (R²). Another is a return perspective based on the trade yield and the number of arbitrage opportunities captured. Finally, a performance heterogeneity analysis is conducted based on the separation of bull and bear markets. The results show that LSTM outperforms all other algorithms over the entire time period, with an RMSE of 0.00813, MAPE of 0.70 percent, R² of 92.09 percent, and an arbitrage return of 58.18 percent. Meanwhile, in certain market conditions, namely both the bull market and bear market separately with a shorter period, LASSO can outperform. Full article

(This article belongs to the Special Issue Applications of Statistical Physics in Finance and Economics)

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12 pages, 484 KiB

Open AccessArticle

A New Method for Determining the Embedding Dimension of Financial Time Series Based on Manhattan Distance and Recurrence Quantification Analysis

by Hanhuai Zhu and Jingjing Huang

Entropy 2022, 24(9), 1298; https://doi.org/10.3390/e24091298 - 14 Sep 2022

Cited by 2 | Viewed by 1459

Abstract

Identification of embedding dimension is helpful to the reconstruction of phase space. However, it is difficult to calculate the proper embedding dimension for the financial time series of dynamics. By this Letter, we suggest a new method based on Manhattan distance and recurrence quantification analysis for determining the embedding dimension. By the advantages of the above two tools, the new method can calculate the proper embedding dimension with the feature of stability, accuracy and rigor. Besides, it also has a good performance on the chaotic time series which has a high-dimensional attractors. Full article

(This article belongs to the Special Issue Applications of Statistical Physics in Finance and Economics)

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22 pages, 446 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Efficiency of the Moscow Stock Exchange before 2022

by Andrey Shternshis, Piero Mazzarisi and Stefano Marmi

Entropy 2022, 24(9), 1184; https://doi.org/10.3390/e24091184 - 25 Aug 2022

Cited by 4 | Viewed by 1928

Abstract

This paper investigates the degree of efficiency for the Moscow Stock Exchange. A market is called efficient if prices of its assets fully reflect all available information. We show that the degree of market efficiency is significantly low for most of the months from 2012 to 2021. We calculate the degree of market efficiency by (i) filtering out regularities in financial data and (ii) computing the Shannon entropy of the filtered return time series. We developed a simple method for estimating volatility and price staleness in empirical data in order to filter out such regularity patterns from return time series. The resulting financial time series of stock returns are then clustered into different groups according to some entropy measures. In particular, we use the Kullback–Leibler distance and a novel entropy metric capturing the co-movements between pairs of stocks. By using Monte Carlo simulations, we are then able to identify the time periods of market inefficiency for a group of 18 stocks. The inefficiency of the Moscow Stock Exchange that we have detected is a signal of the possibility of devising profitable strategies, net of transaction costs. The deviation from the efficient behavior for a stock strongly depends on the industrial sector that it belongs to. Full article

(This article belongs to the Special Issue Applications of Statistical Physics in Finance and Economics)

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26 pages, 6258 KiB

Open AccessArticle

Asymmetric Fractal Characteristics and Market Efficiency Analysis of Style Stock Indices

by Chao Xu, Jinchuan Ke, Zhikai Peng, Wen Fang and Yu Duan

Entropy 2022, 24(7), 969; https://doi.org/10.3390/e24070969 - 13 Jul 2022

Cited by 8 | Viewed by 1475

Abstract

As a typical complex system, the stock market has attracted the attention of scholars and investors to comprehensively understand its fractal characteristics and analyze its market efficiency. Firstly, this paper proposes an asymmetric, detrended fluctuation analysis based on overlapping sliding windows (OSW-A-MFDFA). It reduces the generation of fluctuation errors, and the calculation results are more robust and reliable. The advantage of the OSW-A-MFDFA is that it not only can reveal the multifractal characteristics of time series clearly, but also can further accurately analyze the asymmetry of fractal characteristics under different trends. Secondly, this paper focuses on the variation in the width difference and height difference of the multifractal spectrum under different trends. Finally, based on multifractality, this paper proposes a comprehensive indicator MED that can be used to measure market efficiency, which is characterized by traversing all fluctuation orders. The application revealed many interesting findings in style stock indices. Style stock indices have asymmetric multifractal characteristics, and there are significant differences in the fractal spectrum of different style assets. Moreover, the market efficiency of style stock indices is time-varying, which can be reasonably explained from the perspective of the adaptive market hypothesis. Full article

(This article belongs to the Special Issue Applications of Statistical Physics in Finance and Economics)

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26 pages, 1333 KiB

Open AccessArticle

Exponentially Weighted Multivariate HAR Model with Applications in the Stock Market

by Won-Tak Hong and Eunju Hwang

Entropy 2022, 24(7), 937; https://doi.org/10.3390/e24070937 - 06 Jul 2022

Viewed by 1213

Abstract

This paper considers a multivariate time series model for stock prices in the stock market. A multivariate heterogeneous autoregressive (HAR) model is adopted with exponentially decaying coefficients. This model is not only suitable for multivariate data with strong cross-correlation and long memory, but also represents a common structure of the joint data in terms of decay rates. Tests are proposed to identify the existence of the decay rates in the multivariate HAR model. The null limiting distributions are established as the standard Brownian bridge and are proven by means of a modified martingale central limit theorem. Simulation studies are conducted to assess the performance of tests and estimates. Empirical analysis with joint datasets of U.S. stock prices illustrates that the proposed model outperforms the conventional HAR models via OLSE and LASSO with respect to residual errors. Full article

(This article belongs to the Special Issue Applications of Statistical Physics in Finance and Economics)

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Review

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15 pages, 1135 KiB

Open AccessReview

Emergence of Inequality in Income and Wealth Dynamics

by Changhee Cho, Jihun Park, Biseko Juma Mafwele, Quang Anh Le, Hye Jin Park and Jae Woo Lee

Entropy 2023, 25(8), 1129; https://doi.org/10.3390/e25081129 - 27 Jul 2023

Viewed by 1477

Abstract

Increasing wealth inequality is a significant global issue that demands attention. While the distribution of wealth varies across countries based on their economic stages, there is a universal trend observed in the distribution function. Typically, regions with lower wealth values exhibit an exponential distribution, while regions with higher wealth values demonstrate a power-law distribution. In this review, we introduce measures that effectively capture wealth inequality and examine wealth distribution functions within the wealth exchange model. Drawing inspiration from the field of econophysics, wealth exchange resulting from economic activities is likened to a kinetic model, where molecules collide and exchange energy. Within this framework, two agents exchange a specific amount of wealth. As we delve into the analysis, we investigate the impact of various factors such as tax collection, debt allowance, and savings on the wealth distribution function when wealth is exchanged. These factors play a crucial role in shaping the dynamics of wealth distribution. Full article

(This article belongs to the Special Issue Applications of Statistical Physics in Finance and Economics)

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