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The ongoing conflict between Russia and Ukraine has significantly affected global financial markets, requiring investors to adapt their attitudes accordingly. This study aims to explore the transformation of investor attitudes towards financial markets within the context of the Russia–Ukraine conflict. By analysing monthly data spanning from January 2021 to March 2023, including Standard and Poor 500 index, Bitcoin historical price, gross national income, US consumer price index and investor attitude index, various models such as regression, the vector error correction model, generalized autoregressive conditional heteroskedasticity and the granger causality model are utilized. The findings reveal a noteworthy and positive impact of investor attitude on returns in both markets. Positive investor sentiment is linked to higher returns and enhanced market confidence. Particularly, investor attitude's influence is particularly pronounced during wartime, as global net income exerts a significant negative effect on both markets. These findings emphasize the necessity of evaluating the financial health of industrial companies prior to making investment decisions amidst periods of conflict. Furthermore, the results provide remarkable guidelines for policymakers and investors, underscoring the necessity of tracking the investor attitudes during wartime and acknowledging their potential impact on investment decision‐making in financial markets. [ABSTRACT FROM AUTHOR]

This editorial paper reviews the articles published in a special issue devoted to the application of active particle methods applied to the study of the collective dynamics of large systems of interacting entities in science and society. The applications presented in this special issue focus on the study of financial markets, cell dynamics in the context of cancer modeling, vehicle and crowd vehicle and crowd dynamics, and classical problems in the kinetic theory of active particles and swarm theory. A critical analysis is proposed to look forward to research. Perspectives with emphasis on the interaction of multiple social dynamics. [ABSTRACT FROM AUTHOR]

Contemporary capitalist societies use different institutions to manage economic risks. While different public welfare state and financial institutions (banks, capital markets) have been studied across coordinated and liberal market economies, the different worlds of private insurance institutions have been understudied. Building on new insurance data sets (1880–2017), we find that countries with a Maritime (USA, GBR, CAN) in contrast to the more backward Alpine (AUT, DEU, CHE) insurance tradition developed bigger life and nonlife insurance earlier, with less state-associated and reinsurance enterprises, but riskier investments steered toward financial markets. We argue that the larger and more "Maritime" the insurance sector, the more it made welfare states liberal and securities markets large. Insurance is thus a hidden factor for countries' varieties of capitalism and worlds of welfare. The recent convergence on the Maritime model, however, implies that the riskier and risk-individualizing type of private insurance has added to privatization and securitization trends everywhere. [ABSTRACT FROM AUTHOR]

• Adoption of sustainable investing exchange-traded funds is shaped by various factors. • Insurance companies and pension funds with positive effects. • Also supporting impact of stock markets, new technologies and financial development. • Lesser importance of the financial motives. • In case of mutual funds and institutional variables results are mixed. Despite the growing interest in various topics related to sustainable (ESG) investing, some issues remain understudied, such as the determinants of their utilization at the macro level. This paper contributes to the state of knowledge two-directionally. First, it shows the development of sustainable investing exchange-traded funds (ETFs) in European countries. Second, it traces the significant determinants of adopting sustainable investing/ESG ETFs in Europe. More specifically, we aim to determine which financial, technological, social or institutional factors have the most significant impact on the growth of levels and over-time dynamics of the sustainable investing ETFs. The empirical sample covers five European economies: France, Germany, Italy, Switzerland, and the United Kingdom and the period of the analysis is 2006–2020. Adopting panel regression analysis, we investigate twenty different variables that potentially impact the development of sustainable investing ETFs across examined countries. Our results confirm the positive effects of the development of insurance companies and pension funds for the assets of the ESG ETFs. Also, factors such as stock markets development, ICT adoption, financial access and literacy, and the prevalence of tertiary-level education demonstrate a positive impact on ESG ETFs. The role of the financial performance and tax levels resulted in being neutral, thus indicating the lesser importance of the financial motives in relation to the other factors. The results were mixed in the case of the mutual funds and institutional variables. [ABSTRACT FROM AUTHOR]

This study aims to discern the ontological assumptions of the financial regulatory paradigm to clarify the meaning of a paradigm shift and the significance of the macroprudential turn in global financial regulation. An ontological understanding of financial markets is a key requirement to understand the hard core assumptions of the financial regulatory paradigm because the conflation of distinct ontologies makes it impossible to maintain the internal coherence of the financial regulatory paradigm. In terms of the ontological assumptions of the financial regulatory paradigm, the macroprudential turn in global financial regulations after the 2008 financial crisis fell short of leading to a paradigm shift in global financial regulation since it brought about an explanatory rather than ontological change. Macroprudential ideas rejected the explanatory reductionism of existing microprudential regulation without abandoning its ontological individualism. [ABSTRACT FROM AUTHOR]

This paper leverages turbulence theory from physics to examine the similarities and differences between financial market volatility and turbulent phenomena on a statistical physics level. By drawing analogies between the dynamics of financial markets and fluid turbulence, an innovative analytical framework has been developed to enhance our understanding of the complexity inherent in financial markets. The research methodology involves a comparative analysis of several national stock market indices and simulated turbulent velocity time series, with a particular focus on key statistical properties such as probability distributions, correlation structures, and power spectral densities. Furthermore, a financial market capital flow model has been established, and corresponding solutions have been proposed. Through computational simulations and data analysis, it was discovered that financial market volatility shares some statistical characteristics with turbulence, yet there are significant differences in the shape of probability distributions and the timescales of correlations. This indicates that although financial markets exhibit patterns similar to turbulence, as a multivariate-driven complex system, their behavioral patterns do not completely correspond to natural turbulence phenomena, highlighting the limitations of directly applying turbulence theory to financial market analysis. Additionally, the study explores the use of Bézier curves to simulate market volatility and, based on these analyses, formulates trading strategies that demonstrate practical applications in risk management. This research provides fresh perspectives for the fields of financial market theory and econophysics, offering new insights into the complexity of financial markets and the prevention and management of financial risks. [ABSTRACT FROM AUTHOR]

There is evidence that individuals engage in herding behavior in diverse settings, ranging from fashion choices to financial markets. By examining the exogenous shock caused by the outbreak of COVID‐19 and unique data measuring the footfalls in downtown Jönköping, Sweden, we empirically assess how individuals respond to the restrictions imposed by the government and to the behavior of others, that is, herding. To this end, a regression discontinuity analysis with time as the running variable is applied. We find a negative effect of the restriction on footfalls, indicating that individuals follow the imposed governmental restrictions. However, we also find indications that individuals imitate the actions of others and engage in herd behavior even when the cost of doing so is very high; that is, it could result in severe illness or even death. With the use the unique setting of Sweden, where there have been no enforced lockdowns, we contribute with knowledge related to how effective voluntary restrictions are and the influence of others on one's decision making. Although the nature of the studied sample limits the external generalizability of the results, it may offer guidance for future interventions in clearly delineated settings. [ABSTRACT FROM AUTHOR]

Given the importance of ICT diffusion in the development of the financial sector, this analysis is an effort to analyze the transmission channels between the two in high-income and middle and low-income economies over 2001–2019. We have used three variables, including the ICT index, individuals using the internet, and mobile subscribers, to represent ICT and three indices, including the financial development index, financial institution index, and financial market index, to make our results reliable and robust. We utilized a GMM method for conducting the empirical analysis. Generally, our results imply that ICT diffusion positively impacts financial development in high-income economies and negatively impacts middle and low-income economies. Our findings suggest that middle- and low-income-economy policymakers should follow the footprint of the high-income economies and increase the role of ICT in the financial sector for its development. [ABSTRACT FROM AUTHOR]

Financial markets are exposed to sharp price volatility, and defaults and errors, which results in high risks for relevant stakeholders. In such markets, success depends upon the quality and quantity of information available to assist the decision‐making. Artificial intelligence (AI), in this regard, can measure or predict systemic and systematic risk in the financial markets. This research study aims to highlight how the risks can be measured and controlled with the support and integration of modern AI and machine learning mechanisms. By performing a review‐based methodology, the study first presents an explanation of the models, which is followed by proposing a new AI‐based model. The model relies on several financial system's inputs, which include portfolio data, trade data, market data, financial reports, market condition, and sector‐wise data. The systemic and systematic risk is then assessed through a number of outputs that the AI algorithm will process, in the form of an interactive dashboard. The article further tests that model and presents benefits and implications. [ABSTRACT FROM AUTHOR]

This article mainly investigates whether the war in Ukraine-Russia induces uncertainties and how this affects the returns of world stock market indices and commodity prices. To do so, we use the Economic Policy Uncertainty index (EPU), 96 stock market indices and the price of 67 commodities worldwide over the period from January 2022 to April 2023. Our empirical results show that the war between Ukraine and Russia increases uncertainties, and which negatively affect the performance of global financial markets and lead to higher commodity prices. These negative shocks tend to be smaller the longer the war persists. We also observe that responses to war-induced uncertainties are stronger in Europe and America. Overall, we provide unique evidence of the impact of war on uncertainties and how this is transmitted to stock market performance and commodity prices. [ABSTRACT FROM AUTHOR]

This paper uses the micro-cross-sectional data from the Chinese Household Finance Survey in 2017 to systematically investigate the influence of house purchasing intention on household participation in the financial market by adopting probit, tobit and bootstrap methods. First, house purchasing intention had a significant positive impact on household participation in the financial market. Second, family risk preference played a significant mediating effect in the relation stated above. The results of the paper revealed that the impact of housing market prosperity on participation in the financial market was not only through the stock of real estate in the housing market but also through house purchasing intention. [ABSTRACT FROM AUTHOR]

We consider a 1D continuous piecewise smooth map, which depends on seven parameters. Depending on the values of parameters, it may have up to six branches. This map was proposed by Matsuyama [Theor. Econ., 8, 623 (2013); Sec. 5]. It describes the macroeconomic dynamics of investment and credit fluctuations in which three types of investment projects compete in the financial market. We introduce a partitioning of the parameter space according to different branch configurations of the map and illustrate this partitioning for a specific parameter setting. Then we present an example of the bifurcation structure in a parameter plane, which includes periodicity regions related to superstable cycles. Several bifurcation curves are obtained analytically; in particular, the border-collision bifurcation curves of fixed points. We show that the point of intersection of two curves of this kind is an organizing center, which serves as the origin of infinitely many other bifurcation curves. [ABSTRACT FROM AUTHOR]

The predominant approach for studying volatility is through various GARCH specifications, which are widely utilized in model-based analyses. This study focuses on assessing the predictive performance of specific GARCH models, particularly the Markov-Switching GARCH (MS-GARCH). The primary objective is to determine the optimal number of regimes within the MS-GARCH framework that effectively captures the conditional variance of the Muscat Securities Market Index (MSMI). To achieve this, we employ the Akaike Information Criterion (AIC) to compare different MS-GARCH models, estimated via Maximum Likelihood Estimation (MLE). Our findings indicate that the chosen models consistently exhibit at least two regimes across various GARCH specifications. Furthermore, a validation using the Value at Risk (VaR) confirms the accuracy of volatility forecasts generated by the selected models. [ABSTRACT FROM AUTHOR]

Non-linear dynamical systems describe numerous real-world phenomena, ranging from the weather, to financial markets and disease progression. Individual systems may share substantial common information, for example patients' anatomy. Lately, deep-learning has emerged as a leading method for data-driven modeling of non-linear dynamical systems. Yet, despite recent breakthroughs, prior works largely ignored the existence of shared information between different systems. However, such cases are quite common, for example, in medicine: we may wish to have a patient-specific model for some disease, but the data collected from a single patient is usually too small to train a deep-learning model. Hence, we must properly utilize data gathered from other patients. Here, we explicitly consider such cases by jointly modeling multiple systems. We show that the current single-system models consistently fail when trying to learn simultaneously from multiple systems. We suggest a framework for jointly approximating the Koopman operators of multiple systems, while intrinsically exploiting common information. We demonstrate how we can adapt to a new system using order-of-magnitude less new data and show the superiority of our model over competing methods, in terms of both forecasting ability and statistical fidelity, across chaotic, cardiac, and climate systems. From the cardiac system of various human patients, to changes in sea surface temperature across different oceans, dynamical systems often exhibit many common characteristics. Here we develop a framework for jointly learning the dynamics of multiple interrelated systems while leveraging their shared traits. [ABSTRACT FROM AUTHOR]

This paper characterizes the probability of a market failure defined as the default of two or more globally systemically important banks (G-SIBs) in a small interval of time. The default probabilities of the G-SIBs are correlated through the possible existence of a market-wide stress event. The characterization employs a multivariate Cox process across the G-SIBs, which allows us to relate our work to the existing literature on intensity-based models. Various theorems related to market failure probabilities are derived, including the probability of a market failure due to two banks defaulting over the next infinitesimal interval, the probability of a catastrophic market failure, the impact of increasing the number of G-SIBs in an economy, and the impact of changing the initial conditions of the economy's state variables. We also show that if there are too many G-SIBs, a market failure is inevitable, i.e., the probability of a market failure tends to 1. [ABSTRACT FROM AUTHOR]

Africa must ensure that its development trajectories are sustainable. In pursuit of viable policy pathways, this study examines the eco‐impact of the energy mix (Te) and financial development (FD) in selected African countries. The Te path is defined using the emission‐intensity paradigm, where positive trends indicate a strong dependence on fossil fuels, while negative trends suggest a shift toward greener energy sources. The FD impact is disaggregated to recognize the separate effects of market‐based (FM) and institution‐based (FI) development. This study utilized land‐use metrics across major greenhouse gas (GHG) sources, drawing data from a panel of 33 countries spanning from 1982 to 2021. It employs a novel instrumental‐variables approach for fitting panel‐data models, addressing cross‐sectional dependence issues and incorporating heterogeneous slope coefficients using the mean‐group estimator. Empirical findings reveal that positive trends in Te have a significant and positive impact on total GHG emissions (GHGe), whereas FM has a negative and significant effect on GHGe, suggesting its active role in mitigating climate impacts. The disaggregation of GHGe highlights the distinct impacts on CO2 (CO2e), nitrous oxide (N2Oe) and methane (CH4e) emissions. Economic affluence positively impacts CH4e, while positive trends in Te primarily increase CO2e. FI significantly reduces N2Oe, while FM notably decreases CO2e. Furthermore, FI has a significant negative impact on the emission intensity of Te, indicating its role in promoting green energy transition. These findings underscore the necessity for nuanced policy interventions that integrate FD with environmental objectives, promote green energy transition and address land‐use challenges. [ABSTRACT FROM AUTHOR]

Measuring systemic risk plays an important role in financial risk management to control systemic risk. By means of a vine copula grouped-CoES method, this paper aims to measure the systemic risk of Chinese financial markets. The empirical study indicates that the banking industry has a low risk and a strong ability to resist risks, but also contributes the most of the systemic risk. On the other hand, insurance companies and securities have high ES but low Δ CoES, indicating their low risk tolerance and small contribution to the systemic risk. Furthermore, this study employs a sliding window in Monte Carlo simulation to forecast systemic risk. The findings of this paper suggest that different types of financial industries should adopt different systemic risk measures. [ABSTRACT FROM AUTHOR]

In this paper, we propose to consider the dependence structure of the trade/no trade categorical sequence of individual illiquid stocks returns. The framework considered here is wide as constant and time-varying zero returns probability are allowed. The ability of our approach in highlighting illiquid stock's features is underlined for a variety of situations. More specifically, we show that long-run effects for the trade/no trade categorical sequence may be spuriously detected in presence of a non-constant zero returns probability. Monte Carlo experiments, and the analysis of stocks taken from the Chilean financial market, illustrate the usefulness of the tools developed in the paper. [ABSTRACT FROM AUTHOR]

Purpose: This study is the first to investigate the causal relationship between Bitcoin and equity price returns by sectors. Previous studies have focused on aggregated indices such as S&P500, Nasdaq and Dow Jones, but this study uses mixed frequency and disaggregated data at the sectoral level. This allows the authors to examine the nature, direction and strength of causality between Bitcoin and equity prices in different sectors in more detail. Design/methodology/approach: This paper utilizes an Unrestricted Asymmetric Mixed Data Sampling (U-AMIDAS) model to investigate the effect of high-frequency Bitcoin returns on a low-frequency series equity returns. This study also examines causality running from equity to Bitcoin returns by sector. The sample period covers United States (US) data from 3 Jan 2011 to 14 April 2023 across nine sectors: materials, energy, financial, industrial, technology, consumer staples, utilities, health and consumer discretionary. Findings: The study found that there is no causality running from Bitcoin to equity returns in any sector except for the technology sector. In the tech sector, lagged Bitcoin returns Granger cause changes in future equity prices asymmetrically. This means that falling Bitcoin prices significantly influence the tech sector during market pullbacks, but the opposite cannot be said during market rallies. The findings are consistent with those of other studies that have established that during market pullbacks, individual asset prices have a tendency to decline together, whereas during market rallies, they have a tendency to rise independently. In contrast, this study finds evidence of causality running from all sectors of the equity market to Bitcoin. Practical implications: The findings have significant implications for investors and fund managers, emphasizing the need to consider the asymmetric causality between Bitcoin and the tech sector. Investors should avoid excessive exposure to both Bitcoin and tech stocks in their portfolio, as this may lead to significant drawdowns during market corrections. Diversification across different asset classes and sectors may be a more prudent strategy to mitigate such risks. Originality/value: The study's findings underscore the need for investors to pay close attention to the frequency and disaggregation of data by sector in order to fully understand the true extent of the relationship between Bitcoin and the equity market. [ABSTRACT FROM AUTHOR]

An asset pricing model with two types of chartists and fundamentalists and trend followers is considered, it is driven by a two-dimensional piecewise linear discontinuous map with three subsystems. There are great differences in the dynamic behaviour between expected offset (the expectations of trend followers offset the difference between the expectations of Type 1 traders in bull and bear markets) and expected non-offset. It is proven that there is no chaos in the dynamic of system with expected offset. However, chaos may exist in the dynamic of system with expected non-offset. We present a systematic approach to the problem of analysing the bifurcation phenomena associated with the appearance/disappearance of cycles, which may be related to several bifurcations. The multi-stability regions in parameter plane and related basins of multi-attractors in phase space are investigated. This paper aims to uncover the endogenous law of the unpredictability and excessive volatility in financial markets. [ABSTRACT FROM AUTHOR]

Purpose: Financial mathematics is one of the most rapidly evolving fields in today's banking and cooperative industries. In the current study, a new fractional differentiation operator with a nonsingular kernel based on the Robotnov fractional exponential function (RFEF) is considered for the Black–Scholes model, which is the most important model in finance. For simulations, homotopy perturbation and the Laplace transform are used and the obtained solutions are expressed in terms of the generalized Mittag-Leffler function (MLF). Design/methodology/approach: The homotopy perturbation method (HPM) with the help of the Laplace transform is presented here to check the behaviours of the solutions of the Black–Scholes model. HPM is well known for its accuracy and simplicity. Findings: In this attempt, the exact solutions to a famous financial market problem, namely, the BS option pricing model, are obtained using homotopy perturbation and the LT method, where the fractional derivative is taken in a new YAC sense. We obtained solutions for each financial market problem in terms of the generalized Mittag-Leffler function. Originality/value: The Black–Scholes model is presented using a new kind of operator, the Yang-Abdel-Aty-Cattani (YAC) operator. That is a new concept. The revised model is solved using a well-known semi-analytic technique, the homotopy perturbation method (HPM), with the help of the Laplace transform. Also, the obtained solutions are compared with the exact solutions to prove the effectiveness of the proposed work. The different characteristics of the solutions are investigated for different values of fractional-order derivatives. [ABSTRACT FROM AUTHOR]

Implied volatility is known to have a string structure (smile curve) for a given time to maturity and can be captured by the B-spline. The parameters characterizing the curves can change over time, which complicates the modeling of the implied volatility surface. Although machine learning models could improve the in-sample fitting, they ignore the structure in common over time and might have poor prediction power. In response to these challenges, we propose a two-step procedure to model the dynamic implied volatility surface (IVS). In the first step, we construct the bivariate tensor-product B-spline (BTPB) basis to approximate cross-sectional structures, under which the surface can be represented by a vector of coefficients. In the second step, we allow for the time-dependent coefficients and model the dynamic coefficients with the tree-based method to provide more flexibility. We show that our approach has better performance than the traditional linear models (parametric models) and the tree-based machine learning methods (nonparametric models). The simulation study confirms that the tensor-product B-spline is able to capture the classical parametric model for IVS given different sample sizes and signal-to-noise ratios. The empirical study shows that our two-step approach outperforms the traditional parametric benchmark, nonparametric benchmark, and parametric benchmark with time-varying coefficients in predicting IVS for the S&P 500 index options in the US market. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of estimating high-dimensional covariance matrices in financial markets, where traditional sparsity assumptions often fail due to the interdependence of stock returns across sectors. We present an innovative element-aggregation method that aggregates matrix entries to estimate covariance matrices. This method is designed to be applicable to both sparse and non-sparse matrices, transcending the limitations of sparsity-based approaches. The computational simplicity of the method's implementation ensures low complexity, making it a practical tool for real-world applications. Theoretical analysis then confirms the method's consistency and effectiveness with its convergence rate in specific scenarios. Additionally, numerical experiments validate the method's superior algorithmic performance compared to conventional methods, as well as the reduction in relative estimation errors. Furthermore, empirical studies in financial portfolio optimization demonstrate the method's significant risk management benefits, particularly its ability to effectively mitigate portfolio risk even with limited sample sizes. [ABSTRACT FROM AUTHOR]

Using mean and variance causality analysis, this study examines the volatility relationship between Turkish and G7 stock markets. Weekly return data from May 29, 2009, to June 6, 2023, is utilized for the analysis. The Hong mean and variance causality analysis method is employed as the methodology. Based on the results of the study, Turkey and Japan's stock markets have a significant mean causality relationship. Moreover, the variance causality analysis demonstrates a strong relationship between Turkey and stock markets of Canada, France, Germany, Japan, and the United States. The findings contribute to portfolio diversification strategies and highlight the importance of understanding the dynamics of international financial markets. [ABSTRACT FROM AUTHOR]

This paper studies wage-price dynamics in the context of a disequilibrium macro model where there are two Phillips curves (PC's), a wage PC and price PC. We adapt this framework but introduce a Kaldorian delay effect in wage dynamics whereby output and sales predate wage dynamics. Along Keynesian lines aggregate demand drives output, but output is also impacted from the supply side, through Minsky - Kindleberger financial conditions, enhancing inflation through bottlenecks and supply constraints. Worsening financial conditions, resulting from central banks raising interest rates to counteract inflation, may actually accelerate the inflation rate through affecting the supply side. This occurs when there are financially generated bottlenecks and external non-stationary variables, such as fossil energy and food costs, acting as exogenous drivers of the inflation rate. These features are first studied in a disequilibrium intertemporal dynamic macroeconomic model but then empirically explored in a mixed Vector Error Correction Model (VECM) with a mix of stationary and non-stationary variables. Nonlinearities in the dynamics and regime switching are shown to be essential in disequilibrium macrodynamics, here first studied analytically and then econometrically, exploring the transitory and persistent effects of shocks. [ABSTRACT FROM AUTHOR]

Recent advances in the field of machine learning have yielded novel research perspectives in behavioural economics and financial markets microstructure studies. In this paper we study the impact of individual trader leaning characteristics on markets using a stock market simulator designed with a multi-agent architecture. Each agent, representing an autonomous investor, trades stocks through reinforcement learning, using a centralized double-auction limit order book. This approach allows us to study the impact of individual trader traits on the whole stock market at the mesoscale in a bottom-up approach. We chose to test three trader trait aspects: agent learning rate increases, herding behaviour and random trading. As hypothesized, we find that larger learning rates significantly increase the number of crashes. We also find that herding behaviour undermines market stability, while random trading tends to preserve it. [ABSTRACT FROM AUTHOR]

This article tackles the question of whether financialisation is present in the Chinese economy by analysing two key transformations of the country's financial system. The first was a state-led reform process through which the Chinese financial system introduced market practices, similarly to the rest of the economy. The second was a market-led process, reflected in the emergence and rise of shadow banking, which originates from within financial markets with the aim of bypassing loan restrictions. The article shows that despite the two transformations and the enormous growth of finance during the past four decades, the underlying character of the Chinese financial system exhibits remarkable continuity. Namely, it remains bank based – albeit partially liberalised – with a predominant role for bank credit and a strong presence for the state. The relational and government-controlled structures of Chinese finance have not been replaced by arm's length and private mechanisms. On these grounds, it is premature to consider the Chinese economy to be financialised. [ABSTRACT FROM AUTHOR]

Prediction of the stock market can play a vital role in everyone's life to attain sustainable growth. This can also lead to an attractive profit by making the proper choices in the financial stock market. The stock market prediction is a big challenge which requires extensive advanced tools and techniques to analyze the future and present data. The modern stock market institutions provide better self-trading and investment options, enabling regular people and traders to enter the market through numerous online applications and websites that are available via smart devices. Financial institutions are investing more and more in talent development so that investors may make the maximum money. There are a multitude of reasons for this, including volatility of market and a collection of other interrelated and independent parameters that control the market value of a different stock. Because of these factors, it is excessively difficult for any specialist in financial markets to accurately predict the market's rise and collapse. This research paper proposed an artificial neural network (ANN) technique which combines with fuzzy logic to predict the stock market for short duration with high accuracy. First, historical and real-time data are used to classify and gather information for prediction. Then the short-duration prediction is done with a high accuracy of 96% and the error margin for a small instance is 2.3%. Through training, testing and validation it is clear that for sustainable short duration prediction, ANN integrated with fuzzy is a good choice. [ABSTRACT FROM AUTHOR]

The financial market has randomness, and the prediction of the financial market is an important task in the financial market. In traditional financial market prediction models, the prediction results are often unsatisfactory. So it needs to introduce new models for financial analysis. To solve this problem, this paper analyzed a financial market trend prediction model based on LSTM (Long Short-Term Memory) NN (Neural Network) algorithm, and conducted an empirical analysis on the Shanghai stock index dataset. This paper first introduced the LSTM NN algorithm, and then divided it into training set, test set and comparison set according to the data characteristics. At last, this paper used the data preprocessing method to verify the LSTM NN algorithm. The experimental results showed that the LSTM NN algorithm analyzed in this paper can effectively improve the generalization ability of financial market trend prediction models while ensuring the prediction accuracy. Through experimental analysis, this paper found that the average accuracy rate of using LSTM NN algorithm was 2.25% higher than that of using traditional NN algorithm. This research is primarily aimed at developing effective methods for predicting stock market trends in the continuously evolving Chinese securities market. The core objective is to empower investors with precise guidance by enabling them to make well-informed investment decisions. Achieving accurate predictions holds the potential to significantly impact economic operations in a positive way. Therefore, this research direction is of paramount importance, offering substantial value both in academic exploration and practical application. [ABSTRACT FROM AUTHOR]

This article examines the historically contentious relationship between the financial market and the public as discussed in academic literature, financial journalism and prescriptive how-to invest handbooks during the late 19th and early 20th centuries. Although financial markets thrive off active public participation, speculating at stock and commodity exchanges has been a sanctioned ritual reserved for a privileged minority. We argue that the financial establishment's intent to control market access through financial entry-barriers (such as exchange membership fees and margin requirements) has been part of a bigger story we need to understand: a history of delegitimating uninitiated 'lay speculators' through the construction of exclusionary narratives about unfit amateur investors and morally corrupt publics. We conceptualize this process as an ongoing and delicate boundary-making exercise contributing to a market participation discourse that has been characterized by a set of reductive binaries, such as those of insider–outsider, professional–amateur and speculator–gambler. We show, however, that attempts to delineate popular participation in financial markets through these binaries have been complicated by the idea that besides being a force of market instability and collective irrationality, the public was a largely untapped source of liquidity. We argue that today's discourse on public participation in financial markets resuscitates these simplified narratives and propose a more nuanced view of non-professional market participants being both destabilizers and liquidity-providers. [ABSTRACT FROM AUTHOR]

Our study offers insights into the role of investor behavior during periods of turmoil and its impact on the financial sphere. Interestingly, this study aims to establish the influence of investor heterogeneity, investor sentiment, and corporate investor behavior on the dynamics, investment strategies, and financial stability of financial markets. The included studies focus mostly on crises observed during the last decade with diverse origins, distinct mechanisms, and valuable lessons. This study highlights that investor behavior and financial crises play central roles in financial market dynamics, driving investor decisions, strategies, and, consequently, financial stability. These contributions are valuable for academics, practitioners, policymakers, and regulators in this field. [ABSTRACT FROM AUTHOR]

Continuous‐time diffusion models with jumps, especially the jump intensity coefficient, can depict the impact of sudden and large shocks to financial markets. It is possible to disentangle, from the discrete observations, the contributions given by the jumps and those by the diffusion part through threshold functions. Based on this threshold technique, we employ non‐parametric local linear threshold estimator for the unknown jump intensity function of a semimartingale with jumps. The asymptotic normality of our estimator is provided in the presence of finite activity jumps under certain regular conditions. The finite‐sample performance for the underlying estimator has been shown through a Monte Carlo experiment and an empirical analysis on high frequency returns of indexes in the USA and China. [ABSTRACT FROM AUTHOR]

Various neural network architectures are often used to forecast movements in financial markets. Most research in quantitative analytics in finance uses interval financial data as this reduces the raw tick big data, but the averaging can lose key behaviour patterns. This work presents an alternative novel method to reduce raw tick data whilst retaining important information for training, as demonstrated with intraday trading using the EURO/USD currency pair. This time series reduction method focuses on short periods preceding significant movements in financial features and allows the most popular neural network architectures to be applied using less powerful but more readily available computer resources. It is shown that the proposed data preprocessing method for machine learning and other AI‐techniques successfully reduced the size of the selected dataset covering a three‐year period (2018–2021) by 275 times. [ABSTRACT FROM AUTHOR]

While prosecuting its invasion of Ukraine, Russia has relied heavily on nuclear threats, turning the war in Ukraine into a dangerous nuclear crisis with profound implications for the global nuclear order and its two constitutive systems of nuclear deterrence and nuclear restraint. These two interconnected systems, each aiming to manage nuclear possession and reduce the risk of nuclear use, are at once complimentary and contradictory. While tensions between these systems are not new, the war in Ukraine exacerbates them in unprecedented ways. The system of nuclear deterrence seems to be proving its worth by inducing restraint on Russia and NATO, while the system of restraint is undermined by demonstrating what happens to a country not protected by nuclear deterrence. The latter lesson is particularly vivid given Ukraine's decision to forgo a nuclear option in 1994 in exchange for security assurances from nuclear powers. Russia's use of nuclear threats as an enabler for escalation and the specter of Russian tactical nuclear use against Ukraine goes well beyond its declared nuclear doctrine. The outcome of the war in Ukraine thus has critical importance for deciding the value of nuclear weapons in global security architecture and for resolving the conundrum between the systems of deterrence and restraint. [ABSTRACT FROM AUTHOR]

Do people's attitudes toward the (a)symmetry of an outcome distribution affect their choices? Financial investors seek return distributions with frequent small returns but few large ones, consistent with leading models of choice in economics and finance that assume right-skewed preferences. In contrast, many experiments in which decision-makers learn about choice options through experience find the opposite choice tendency, in favor of left-skewed options. To reconcile these seemingly contradicting findings, the present work investigates the effect of skewness on choices in experiencebased decisions. Across seven studies, we show that apparent preferences for left-skewed outcome distributions are a consequence of those distributions having a higher value in most direct outcome comparisons, a "frequent-winner effect." By manipulating which option is the frequent winner, we show that choice tendencies for frequent winners can be obtained even with identical outcome distributions. Moreover, systematic choice tendencies in favor of right-or left-skewed options can be obtained by manipulating which option is experienced as the frequent winner. We also find evidence for an intrinsic preference for right-skewed outcome distributions. The frequent-winner phenomenon is robust to variations in outcome distributions and experimental paradigms. These findings are confirmed by computational analyses in which a reinforcement-learning model capturing frequent winning and intrinsic skewness preferences provides the best account of the data. Our work reconciles conflicting findings of aggregated behavior in financial markets and experiments and highlights the need for theories of decision-making sensitive to joint outcome distributions of the available options. [ABSTRACT FROM AUTHOR]

This work surveys the use of directed weighted graphs in conducting comparative static analyses. The paper discusses the implementation of a computer-aided process for building spreadsheet-based graph models for inter- and intra-regional financial flows. The graph-theoretic techniques are programmed to enable the interactive visualization and analysis of financial data using Wolfram technologies (i.e., Mathematica software v. 11.3 or later, Wolfram player v. 12 or later). The paper describes the workflow for several interactive visualizations applicable to financial networks. The author provides four programs, written in the Wolfram language, that customize input–output financial models by combining the Manipulate command with built-in Mathematica functions and functions of the IGraph package (IGraph/M). The study is a tutorial article for the generation of a suite of visual schemes that provide patterns, practices, and roadmaps of the financial markets across the globe. [ABSTRACT FROM AUTHOR]

Climate change-induced pan-financial market and the contagion of systemic financial risks are becoming important issues in the financial sector. The paper measures the temperature difference in terms of the degree and direction of deviation of the actual temperature relative to the average temperature of the same historical period. Based on the high-dimensional time-series variable LASSO-VAR-DY framework, we construct a pan-financial market volatility correlation network consisting of 112 Chinese listed companies in banking, insurance, securities, real estate, traditional energy, and new energy, use eigenvector centrality to measure the systematic risk of each firm, and then empirically test the effect of temperature difference on systematic risk under pan-financial market scenario. The results of the study show that (ⅰ) There is a significant difference among the systemic risk of financial sectors such as banking, insurance, and securities in the financial market pan-financial market scenario and the systemic risk when the financial market pan-financial market is not taken into account;(ⅱ) Higher temperature significantly exacerbates systemic financial risk, while colder temperature significantly mitigates systemic risk, but both have an asymmetric effect on systemic risk, and there is sectoral heterogeneity.(ⅲ) From the dynamic evolutionary characteristics, there are significant differences in the response of systemic financial risk to positive and negative temperature shocks;(iv) The results of the systemic risk variance decomposition indicate that the temperature change contributes more to the variance of systemic risk in the banking and securities sectors in pan-financial market;(ⅴ) The contagion source of financial systemic risk shows an obvious path of leaping and changing characteristics, and the contagion source of systemic risk (source of impact) shows the evolution law of "bank → real estate → new energy → temperature difference," which means that the temperature difference has become the contagion source of systemic financial risk. This study provides a reference for preventing and resolving systemic risks under pan-financial market scenario and provides a basis for improving the current macroprudential regulatory framework. [ABSTRACT FROM AUTHOR]

This study employs a bivariate EGARCH model to examine the Taiwan Futures Exchange's regular and after-hours trading, focusing on the critical aspects of spillover and expiration effects, as well as volatility clustering and asymmetry. The objective of this study is to observe the impact on the trading sessions in Taiwan by the influences of the European and American markets, focusing on the essential roles of the price discovery function and risk disclosure effectiveness of the regular hours trading. This research is imperative considering the increasing interconnectedness of global financial markets and the need for comprehensive risk assessment for investment strategies. It also examines the hedging behavior of after-hours traders, thereby aiming to contribute to pre-investment analysis by future investors. This examination is vital for understanding the dynamics of after-hours trading and its influence on market stability. Results indicate price continuity between both trading sessions, with regular trading often determining after-hours price ranges. Consequently, after-hours price changes can inform regular trading decisions. This finding highlights the importance of after-hours trading for shaping market expectations. Significant profit potential exists in after-hours trading open interest, which serves speculative and hedging purposes. While regular trading volatility influences after-hours trading, the reverse is not true. This suggests Taiwan market information poses a higher risk impact than European and American market data, emphasizing the unique position of the Taiwan market in the global financial ecosystem. After-hours trading volatility reflects the absorption of international market information and plays a crucial role in advance revelation of risks. This underscores the importance of after-hours trading in global risk management and strategy formulation. [ABSTRACT FROM AUTHOR]

Quantitative investment theory has emerged as a prominent and widely researched domain within the financial markets, where investors predominantly focus on discerning the intricate influences of market dynamics. In this paper, we proposed a short-term prediction-based trading strategy, which can equiponderate between return and risk, considerations while accounting for investor risk preferences. This strategy employs GM(1,1) to capture nuanced features of price dynamics in short-term intervals and update the GM(1,1) model with the latest data. Subsequently, a multi-objective planning equation is formulated to optimize asset allocations by determining the optimal holding that strikes between specific returns and risk mitigation. In the end, this work conducts a case study and sensitivity analysis using five years of gold and bitcoin price data spanning from 2016 to 2021. This empirical examination serves to affirm the efficacy and resilience of the proposed trading strategy. The case study reveals that proficient short-term price forecasting serves as a potent means to proactively mitigate risk, facilitating, judicious and objective trading practices. Moreover, it underscores the strategy's tangible utility as a guide for real-world investment decisions. [ABSTRACT FROM AUTHOR]

This study investigates the volatility and return spillovers between Private Equity Buyouts (PE) and Venture Capital (VC), the equity market, precious metals, real estate, and the Dollar index, which has been widely ignored in the literature. We analysed daily data from 2 January 2004 to 29 December 2022 using Diebold and Yılmaz's (2012) framework. Our data covers the global financial crisis and the COVID-19 period and is rich enough to examine the linkages between the mentioned financial markets thoroughly. PE's higher connectedness with the equity market than VC’s is the main finding of this research. Besides, PE and VC have low connectedness to precious metals, real estate, and the Dollar index. In the sample period, VC is a net receiver, while PE is a net transmitter of volatility and return spillover. An exciting finding is that during the COVID-19 pandemic, VC experienced lower volatility spillover than PE from the broad market proxied by S&P 500. Investors and portfolio managers can benefit from our results to better diversify their portfolios. [ABSTRACT FROM AUTHOR]

The present work aimed in highlighting the role of government departments that are authorized to determine and ensure the formation and monitoring of economic security indicators and quantify the impact of international sanctions on the country's economic security using Russia as a case study. The research authors analyzed the activities of state institutions, government bodies, on the basis of which it was possible to develop a system of indicators enabling one to perform a quantitative assessment of the level of economic security of the state. The analysis of these indicators for the time from 2007 to 2019 issued by the state monitoring services, revealed that the international sanctions imposed on Russia in 2014 produced no significant medium-term effect on its economic security level. The decreasing tendency of the indicator designating the share of innovative goods (works, services) in the total volume of goods (works, services) was the only exception from this inference. The present findings might be useful for the qualified persons of the Russian Federation and third-party specialists, public organizations and partners to assess the prospects for cooperation with relevant government agencies, namely for clear understanding of the advantages and disadvantages of working with them when elaborating strategies and tactics in unleashing sanctions and counter-sanctions. [ABSTRACT FROM AUTHOR]

The paper considers the statistical concept of causality in continuous time, which is based on Granger's definition of causality. We give necessary and sufficient conditions, in terms of statistical causality, for the preservation of the strong property of predictable representation for stopped martingales when filtration is decreased. This concept of causality is also connected to the preservation of the strong property of predictable representation under a change in measure. In addition, we give conditions, in terms of statistical causality, for martingales to have strong and weak properties of predictable representation. The results are applied to the problem of pricing claims in incomplete financial markets. [ABSTRACT FROM AUTHOR]