Search

How much do financial constraints delay entrepreneurship and how much do they cost households in terms of foregone welfare in countries with large informal sectors and poorly developed financial markets? This paper uses a standard, partial equilibrium, dynamic model of occupational choice under financial constraints to derive the optimal time that households take to switch from subsistence work to entrepreneurship. Using data from the informal sector in Cameroon, the paper first tests the implications of the model with reduced-form, survival regression models and then structurally estimates key parameters using maximum likelihood. Prospective entrepreneurs would need to work for an average of 7 years as subsisters to accumulate the minimum savings required to start their micro-enterprise. Among the potential entrepreneurs about 53% are subsisters implying a missing proportion of entrepreneurs of 10.9% among which more than 32% have wealth below their estimated poverty traps. This means that despite their talent and ability to save a sizable fraction of the population will never start a profitable firm, given the tight collateral constraints in the economy. Finally, the paper conducts counterfactual simulations based on the estimated model. The results show substantial welfare gains from relaxing credit constraints, accruing mostly to high talent individuals. [ABSTRACT FROM AUTHOR]

This paper contributes to the debate about Africa's financial stability by focusing on the prevalence of systemic risk in 40 listed derivatives user-banks over 2011–2017, employing the systemic risk index (SRI), and subsequently exploring how it links to the continent's financial market development. The systemic risk buildup within the industry is mainly depicted as an unstable, diluted, and possibly depleting process. Gloomy predictions persist in the subsequent attempts to uncover plausible causes of systemic risk formation. Mainly, conventional defining factors for risk exhibit irrelevance toward systemic risk development, as threats to financial stability remain confined within the industry, supported by a lack of diversification in financial services. The investigated markets may be individually subject to internal vulnerabilities that could be exacerbated by financial shocks, feedback effects, and the likelihood of contagion and failure among financial institutions. [ABSTRACT FROM AUTHOR]

This study investigates the interdependent structure between Nigeria and developed stock market using directed acyclic graph with Bayesian estimation techniques for the period of March 11, 2020 to July 31, 2021. The dynamic Granger causal test of both the contemporaneous and autoregressive estimation were done using the Markov Chain Monte Carlo (MCMC) simulation method with Metropolis-Hasting (MH) sampling technique. The graphical structure of the instantaneous and autoregressive results shows returns correlation for Nigeria's stock market and both China and India's stock market while absence of return correlation are found for both US and UK stock market. The result implies that Nigeria's stock market has potential diversification benefit for the US and UK investors during pandemic crises. In addition, US stock market returns is found to depend on China stock market returns while volatility spill over runs from the US stock market to China's stock market. Last, UK stock market tends to depend on India's stock market. The implication of the study is that Nigeria's stock market is integrated with China and India's stock market while China's stock market is linked to the US stock market such that any financial shock from the lead stock markets is transmitted to the lagged stock markets. [ABSTRACT FROM AUTHOR]

The Athens Stock Exchange (ASE) is a dynamic financial market with complex interactions and inherent volatility. Traditional models often fall short in capturing the intricate dependencies and long memory effects observed in real-world financial data. In this study, we explore the application of fractional Brownian motion (fBm) to model stock price dynamics within the ASE, specifically utilizing the Athens General Composite (ATG) index. The ATG is considered a key barometer of the overall health of the Greek stock market. Investors and analysts monitor the index to gauge investor sentiment, economic trends, and potential investment opportunities in Greek companies. We find that the Hurst exponent falls outside the range typically associated with fractal Brownian motion. This, combined with the established non-normality of increments, disfavors both geometric Brownian motion and fractal Brownian motion models for the ATG index. [ABSTRACT FROM AUTHOR]

Against the background of price increases on the one hand and strong tenement regulations in Vienna's historic housing stock on the other, this article analyses the actors and strategies of commodification. Following a mixed-methods approach, this case study analyses 90 historic tenement buildings in nine neighbourhoods. Our analysis reveals the increasing relevance of institutional actors as (co-)owners, which goes hand in hand with declining holding periods. As local actors are highly diverse, we have identified five types of actors and four commodification strategies, pointing towards a spatially differentiated division of labour. This actor constellation, dominated by micro actors, suggests that the specific situation and regulation of a market brings different types of actors to the fore. Most of the identified actors, and particularly the funding of their projects, have no relation to financial markets and sources; from this perspective, the concept of commodification provides greater insight into the dynamic of this specific housing market segment than the lens of financialisation does. [ABSTRACT FROM AUTHOR]

In the context of global economic digitalization, financial information is highly susceptible to internet financial public opinion due to the overwhelming and misleading nature of information on internet platforms. This paper delves into the core entities in the diffusion process of internet financial public opinions, including financial institutions, governments, media, and investors, and models the behavioral characteristics of these entities in the diffusion process. On this basis, we comprehensively use the multi-agent model and the SIR model to construct a dynamic evolution model of internet financial public opinion. We conduct a simulation analysis of the impact effects and interaction mechanisms of multi-agent behaviors in the financial market on the evolution of internet financial public opinion. The research results are as follows. Firstly, the financial institutions' digitalization levels, government guidance, and the media authority positively promote the diffusion of internet financial public opinion. Secondly, the improvement of investors' financial literacy can inhibit the diffusion of internet financial public opinion. Thirdly, under the interaction of multi-agent behaviors in the financial market, the effects of financial institutions' digitalization level and investors' financial literacy are more significant, while the effects of government guidance and media authority tend to converge. [ABSTRACT FROM AUTHOR]

This study seeks to investigate the extreme risk spillovers from crude oil to four major Asian stock markets from a market sentiment perspective. A copula-CoVaR (Conditional Value-at-Risk) approach is adopted to construct a dynamic network to capture the contributions of different types of market sentiment to the evolution of oil-stock risk nexus. The empirical evidence shows the presence of significant risk spillover effects from crude oil to equity markets. The dynamics of the oil-to-stock risk spillovers is shown to be significantly driven by market sentiments, mainly market fears in both stock markets and crude oil market triggered by prominent systemic events. As an extreme event unfolds, the oil-to-stock risk spillover dynamics is increasing susceptible to escalating market fears. [ABSTRACT FROM AUTHOR]

Concerns about socially uneven progress and inequality have regained public attention (including that of many populist politicians). The purpose of this paper is to identify the economic policies as well as economic factors that facilitate inclusive development. This paper is a first attempt to empirically estimate the drivers of inclusive development. For our empirical assessments, we apply the Multidimensional Inclusiveness Index suggested by Dörffel and Schuhmann (2022) in a panel OLS regression setup with fixed effects (FE) and GMM estimations for up to 178 countries and a time frame ranging from 1980 to 2018. In FE regressions, we find robust associations with inflation as well as financial sector development in the short and long-run, trade/GDP in the long-run. The GMM results point only to inflation and trade as significant drivers in the long-run and investment in the short run. These results suggest that accessible and well-functioning financial markets paired with low rates of inflation and high trade openness take on a more critical role than government spending. Our results suggest that rudiments of the Washington consensus could still guide the promotion of inclusive development. [ABSTRACT FROM AUTHOR]

Social finance, an arena of international finance comprising social banking, impact investing, and microfinance, offers a powerful conceptual lens through which to interrogate the interface of finance and labor in financial capitalism. This article investigates social finance's development at the crossroad of two processes. Social finance emerged in the context of a process of laboring finance, an often-coercive inclusion of workers and households into financial circuits of accumulation and expansion made possible by labor market changes that began in the 1970s. The global financial crisis occasioned the growth of social finance, which, by means of financing labor, placed the reproductive needs of labor—the needs of society's most disadvantaged agents, the poor, the homeless, the marginal, and unemployed workers—at the center of financial accumulation. By informing an oscillatory movement between laboring finance and financing labor, and, affectively, between trust and distrust, social finance primes and cultivates financial market participants' belief in purported solutions to the social problems financial capitalism has contributed to create. As a critical investigation of the complex interface between finance and labor that animates social finance's operations, this article aims to provide conceptual resources that can contribute to releasing labor's social cooperation from finance's hegemony. [ABSTRACT FROM AUTHOR]

Copyright of Taiwan Law Review is the property of Angle Publishing Co., Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Integration in financial markets offers opportunities for free flow of information and capital for international investments. However, this also poses challenges for maintaining effective international portfolio diversification due to heightened market correlations. This study aims to analyze the diversifying potential of Islamic financial assets and undertake a dynamic analysis of their correlation with volatility indices. In this context, the study explores the interaction between the Dow Jones Islamic Market Developed Markets Quality and Low Volatility Index (DJIDVI) and such volatility indices including the CBOE Volatility Index (VIX), CBOE Oil Volatility Index (OVX), CBOE Gold Volatility Index (GVZ), and Euro Currency Volatility Index (EVZ). The Dynamic Correlation-Multivariate Stochastic Volatility (DC-MSV) model is employed to assess of how volatility shocks in one asset influence the volatility of others. The findings reveal that DJIDVI demonstrates the highest volatility clustering among the considered series. Moreover, DJIDVI exhibits mutual interactions with VIX and EVZ, and shocks increasing DJIDVI volatility also contribute to heightened volatility in VIX and OVX. Notably, the correlation between DJIDVI and volatility indices is influenced by global events. The study emphasizes the enhanced predictability of DJIDVI and its negative correlation with other series establishing its potential as a diversifier. The findings of this study contribute to advancing the understanding of international portfolio diversification emphasizing the importance of incorporating Islamic financial assets. [ABSTRACT FROM AUTHOR]

We compare the effect of a domestic shock in China and the US on the real economy and financial markets of various commodity‐exporting countries. To obtain a reliable indicator for China's macroeconomic conditions, we estimate a Bayesian dynamic factor model using block‐exclusion restrictions to identify a China factor and a US factor from monthly macroeconomic data for China and the US. We, then, assess the implications of a negative shock to both factors on the macroeconomy of a commodity‐exporting nation using Bayesian FAVARs based on recursive identification. A negative China shock leads to output loss and a fall in stock prices in these countries. China shock affects the output of only a subset of countries in our sample compared to the US shock, which affects all countries. China shock has a larger, quicker and more persistent impact on the stock markets of commodity‐exporting countries compared to the US shock. Countries with weaker institutional or business environments experience a larger negative real effect of the China shock, whereas countries with less stable financial systems demonstrate stronger financial effects. Using historical decomposition, we establish a growing role of the China factor over time, in particular for large emerging economies such as Brazil and Russia. [ABSTRACT FROM AUTHOR]

We propose a model that forecasts market correlation structure from link- and node-based financial network features using machine learning. For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement of asset price returns across company constituents of major global market indices. We provide empirical evidence using three different network filtering methods to estimate market structure, namely Dynamic Asset Graph, Dynamic Minimal Spanning Tree and Dynamic Threshold Networks. Experimental results show that the proposed model can forecast market structure with high predictive performance with up to 40 % improvement over a time-invariant correlation-based benchmark. Non-pair-wise correlation features showed to be important compared to traditionally used pair-wise correlation measures for all markets studied, particularly in the long-term forecasting of stock market structure. Evidence is provided for stock constituents of the DAX30, EUROSTOXX50, FTSE100, HANGSENG50, NASDAQ100 and NIFTY50 market indices. Findings can be useful to improve portfolio selection and risk management methods, which commonly rely on a backward-looking covariance matrix to estimate portfolio risk. [ABSTRACT FROM AUTHOR]

The period from January 2020 to December 2022 was marked by a confluence of major events, including the COVID-19 pandemic, the Russia–Ukraine war, the energy crisis, surging inflation, Federal Reserve policy shifts, and banking turmoil, which collectively fueled heightened market volatility, risk management needs, and speculative trading opportunities, leading to volatile swings in trading volume concentration across financial markets, with periods of significant increases followed by rapid declines. This paper examines the variation in the concentration of trading volume across the full spectrum of S&P 500 companies, with a focus on explaining the reasons behind the stochastic changes in trading volume concentration. We analyze different concentration measurement methods, including the power law exponent, the Herfindahl–Hirschman Index, and the Gini-based Trading Concentration Index (TCI). The research employs a novel experimental design, comparing a concentration-driven portfolio, rebalanced daily based on the top 30 stocks by trading volume, against the S&P 500 benchmark. Our findings reveal that the Gini-based TCI fluctuated between 55.98% and 77.35% during the study period, with significant variations coinciding with major market events. The concentration-driven portfolio outperformed the S&P 500, achieving an annualized return of 10.66% compared to 5.89% for the index, with a superior Sharpe ratio of 0.325 versus 0.19. This performance suggests that following trading volume concentration can yield above-average results. However, this study also highlights the importance of understanding and managing the risks associated with concentrated portfolios. This study contributes to the literature on market dynamics and offers practical insights for investors and fund managers on optimizing portfolio strategies in response to evolving concentration patterns in financial markets. [ABSTRACT FROM AUTHOR]

Price prediction remains a crucial aspect of financial market research as it forms the basis for various trading strategies and portfolio management techniques. However, traditional models such as ARIMA are not effective for multi-horizon forecasting, and current deep learning approaches do not take into account the conditional heteroscedasticity of financial market time series. In this work, we introduce the similarity embedded temporal Transformer (SeTT) algorithms, which extend the state-of-the-art temporal Transformer architecture. These algorithms utilise historical trends in financial time series, as well as statistical principles, to enhance forecasting performance. We conducted a thorough analysis of various hyperparameters including learning rate, local window size, and the choice of similarity function in this extension of the study in a bid to get optimal model performance. We also experimented over an extended timeframe, which allowed us to more accurately assess the performance of the models in different market conditions and across different lengths of time. Overall, our results show that SeTT provides improved performance for financial market prediction, as it outperforms both classical financial models and state-of-the-art deep learning methods, across volatile and non-volatile extrapolation periods, with varying effects of historical volatility on the extrapolation. Despite the availability of a substantial amount of data spanning up to 13 years, optimal results were primarily attained through a historical window of 1–3 years for the extrapolation period under examination. [ABSTRACT FROM AUTHOR]

With the outbreak and evolution of the pandemic worldwide, the financial market has experienced unprecedented shocks and adjustments, and the volatility and correlation of the stock market, as an important indicator of economic activities, have shown new features and trends during the pandemic. Based on the CSI 300 Index, we construct a three-stage sequential network representing the pre-pandemic, pandemic, and post-relaxation phases. We investigate the evolving dynamics and resilience of the network, forecasting potential future connections, thus offering fresh insights into comprehending market recovery. Our findings unveil that the market adapts dynamically to the pandemic's progression, witnessing an overall augmentation in network interconnectedness. While the financial sector maintains its pivotal role, the influence of non-financial sectors experiences an upsurge. Despite the network demonstrating poor stability and heavy reliance on key nodes, there exists a positive recovery trajectory. Non-financial sectors such as energy and transportation emerge as pivotal catalysts for market rejuvenation. We provide suggestions for government regulators and investors, providing strong support for optimizing the market structure and promoting the long-term healthy development of the market. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Predicting the directions of financial markets has been performed using a variety of approaches, and the large volume of unstructured data generated by traders and other stakeholders on social media microblog platforms provides unique opportunities for analyzing financial markets using additional perspectives. Pretrained large language models (LLMs) have demonstrated very good performance on a variety of sentiment analysis tasks in different domains. However, it is known that sentiment analysis is a very domain-dependent NLP task that requires knowledge of the domain ontology, and this is particularly the case with the financial domain, which uses its own unique vocabulary. Recent developments in NLP and deep learning including LLMs have made it possible to generate actionable financial sentiments using multiple sources including financial news, company fundamentals, technical indicators, as well social media microblogs posted on platforms such as StockTwits and X (formerly Twitter). We developed a financial social media sentiment analyzer (FinSoSent), which is a domain-specific large language model for the financial domain that was pretrained on financial news articles and fine-tuned and tested using several financial social media corpora. We conducted a large number of experiments using different learning rates, epochs, and batch sizes to yield the best performing model. Our model outperforms current state-of-the-art FSA models based on over 860 experiments, demonstrating the efficacy and effectiveness of FinSoSent. We also conducted experiments using ensemble models comprising FinSoSent and the other current state-of-the-art FSA models used in this research, and a slight performance improvement was obtained based on majority voting. Based on the results obtained across all models in these experiments, the significance of this study is that it highlights the fact that, despite the recent advances of LLMs, sentiment analysis even in domain-specific contexts remains a difficult research problem. [ABSTRACT FROM AUTHOR]

Non-banking finance companies (NBFCs) have had a rather unpredictable presence, survival and growth in the Indian financial markets. It is rather well known by now that the risk-borne activities undertaken by a few NBFCs raised a few red flags and brought many stable heavyweight organizations in the finance sector to a screeching halt in their tracks. Due to the recent IL&FS and the DHFL crises, one might easily conclude to restrict, restrain and rein in the NBFC sector with increased regulations. Clamping down on the NBFC sector would not work, as they reach out to the nook and corner of the country, with many citizens seeking services from the NBFC sector, especially when they do not find a suitable solution to their credit needs from the formal banking sector. The authors find that some of the basic challenges for the NBFC sector have always been, and shall always remain, to find an ideal mix of capital from varied sources to fund the growing credit demand while being devoid of excessive risk from any one set of lenders. Towards the end of this paper, upcoming challenges for the NBFC sector domestically as well as globally along with its most suitable solutions from a practical implementation perspective have been suggested. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of AOS: ESTRATEGIA & INOVACAO and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There is a consensus that stock markets are procyclical. However, answers to some important questions remain unclear. Do stock markets lead or lag business cycles? More interestingly, what is the duration with which they lead or lag them? This study uses different time-series filters and time-difference analysis to answer these questions by examining the dynamic interactions between three major stock indices and key macroeconomic indicators in the United States. The findings show that stock markets have been strongly procyclical, lagging industrial production by one to three months in recent decades. There have been noteworthy changes in the relationship between inflation and stock market cycles. The correlations changed from negative in the 1980s and 1990s to positive in the 2000s and 2010s. The results also reveal close associations between the stock indices, offering new insights into the interplay between financial markets and economic cycles. [ABSTRACT FROM AUTHOR]

We examine the impact of foreign direct investment (FDI) on financial inclusion. To identify the causal effect of FDI on financial inclusion, we use plausibly exogenous source of variations in bilateral investment treaties (BITs) as a novel instrumental variable (IV) for net FDI inflows. Using annual data for a broad panel of 90 countries over the period 2004 to 2017, our results show that FDI improves financial inclusion for both "access to finance" and "use of financial services". This impact is more pronounced for relatively poor countries and developing countries compared to rich and developed countries. We also find that higher financial market development and quality institutions improve financial inclusion directly. Moreover, financial market development and institutional quality can serve as potential channels and moderating variables through which FDI affects financial inclusion. Our results are robust to various estimations and sample splitting and have important implications for policy on financial inclusion. [ABSTRACT FROM AUTHOR]

Summary: This paper examines the impact of data rescaling and measurement error on scoring rules for distribution forecast. First, I show that all commonly used scoring rules for distribution forecasts are robust to rescaling the data. Second, the forecast ranking based on the continuous ranked probability score is less sensitive to gross measurement error than the ranking based on the log score. The theoretical results are complemented by a simulation study aligned with frequently revised quarterly US gross domestic product (GDP) growth data, a simulation study aligned with financial market volatility, and an empirical application forecasting realized variances of S&P 100 constituents. [ABSTRACT FROM AUTHOR]

Environmental pollution and climatic change impel economies to discover new methods for sustainable economic development. Green growth is considered an effective way of environmental sustainability. From this perspective, this study evaluates the impact of fiscal policy shocks, financial institutions, and markets development on the green growth of high-polluting economies. The study employed a CS-NARDL approach for investigating the short-run and long-run estimates. The results show that positive shocks in government spending tend to significantly increase green growth in the long-run. In contrast, the negative shock is government spending does not report any significant influence on green growth in the long-run. Financial institution's and financial market's efficiency have a positive impact on green growth in the long-run. Thus, the findings of the study suggest that government spending should be increased with a particular focus on financial markets efficiency and financial institution's efficiency to enhance green growth. [ABSTRACT FROM AUTHOR]

We provide estimates of Treasury convenience premia across the entire term structure of Treasury bills, notes, and bonds over more than a quarter of a century and document a variety of key stylized facts about their time‐series and cross‐sectional patterns. These results raise concerns about the evolving nature of Treasury markets and suggest that investors may now place less weight on the traditional role of Treasury securities as liquid trading vehicles. These stylized facts provide empirical benchmarks that could help guide future theoretical and empirical work about the economics of safe assets in financial markets. [ABSTRACT FROM AUTHOR]

We study noisy aggregation of dispersed information in financial markets without imposing parametric restrictions on preferences, information, and return distributions. We provide a general characterization of asset returns by means of a risk‐neutral probability measure that features excess weight on tail risks. Moreover, we link excess weight on tail risks to observable moments such as forecast dispersion and accuracy, and argue that it provides a unified explanation for several prominent cross‐sectional return anomalies. Simple calibrations suggest the model can account for a significant fraction of empirical returns to skewness, returns to disagreement, and interaction effects between the two. [ABSTRACT FROM AUTHOR]

Generally, the nonlinear and non‐stationary financial time series becomes an obstacle in the process of stock movement prediction. But the recent theories of machine learning and deep learning have provided with some new solutions. Based on LSTM (long short‐term memory), we propose a hybrid model of wavelet transform (WT) and multi‐input LSTM to predict the trend of SSE composite index. It can mine valid data in time series and support different types of data as input. The whole model is divided into two stages. In the first stage, we adopt the level 1 decomposition with db4 mother wavelet to eliminate noise. In the second stage, combinative and qualitative analysis was made base on the data from Chinese stock market, US stock market, and technical indicators as input. According to the result, the proposed model, with the accuracy of 72.19%, performs better than single‐input LSTM, decision tree, random forest, Support Vector Machine (SVM), and XGBoost. [ABSTRACT FROM AUTHOR]

The zero-lower bound (ZLB) may constrain the ability of Federal Reserve (Fed) to influence financial markets and the economy. This note examines the effectiveness of the interest rate channel and the credit channel of conventional and unconventional monetary policy while accounting for business cycle fluctuations. We use intraday industry returns and a number of industry-specific and firm-specific indicators to capture the sensitivity of firms' demand to interest rates (interest rate channel) and firms' financial constraints (credit channel). Our results indicate a dramatic change in the effectiveness of the transmission channels across business cycles and across periods. We find that the interest rate channel operates equally well during recessions and expansions pre-ZLB, but that this channel has ceased to function during the ZLB regardless of the stage of the business cycle. In contrast, the credit channel operates only during recessions in the conventional period, while it has been remarkably effective during both recessions and expansions in the ZLB era.1 [ABSTRACT FROM AUTHOR]

We synthesize empirical studies on the determinants of the heterogeneity in the adjustment speed (speed of adjustment; SOA) of capital structure. These determinants are categorized into six groups, namely, (1) firm-specific characteristics, (2) financial reporting and managerial incentives, (3) corporate governance, (4) informal institutions, (5) financial market attributes and (6) economy-wide attributes. From this analysis, we perceive important potential research questions linked to identifying channels associated with the costs and frictions explaining SOA heterogeneity, including financial reporting quality aspects, firm internal and external monitoring mechanisms and institutional and cultural elements. Interesting avenues for future research also include considering SOA dynamics for comparable cross-country samples and the investigation of the subsequent consequences of capital structure adjustments. JEL Classification: G30, G32, G34, M41 [ABSTRACT FROM AUTHOR]

Analysis of the criticality in finance and economics is an important topic for scholars. The crises are conditions in which unpredictable behavior have been emerged. On the other hand, the contagion from one market to another one due to the complex nature of them is a crucial concept. Therefore, we should consider economies and financial markets as entangled systems that interact with each other, and studying one’s behavior without considering its interacting structures will not provide us with adequate information. In this paper, we have considered the critical states of some emerging and mature markets’ indices. S&P500 and Nikkei 225 as representatives of mature markets, and SSE Composite and TEDPIX as representatives of emerging markets have been analyzed. Coupled criticality emerges when two interacting systems reach their critical state due to their interactions. One of the efficient methods to study the coupled criticality is the bi-variate multifractal random walk model. This method is useful to assess the correlation of rare events and coupled criticality in entangled systems such as financial markets. The observations in this paper show that TEDPIX and S&P500 have higher critical states among others in most time lags by assessing criticality parameter explained in the model, and in longer time lags criticality parameter decreases in all indices. Coupled criticality parameter explained in the model is assessed between each index mentioned and TEDPIX. Results show that TEDPIX and S&P500 have the highest level of coupled criticality. To figure out how long-range time correlation between rare events in each index affect its critical behavior, the data of each index are shuffled, and the criticality parameter is calculated for the shuffled data. From the observations, it can be concluded that this matter has more substantial effect on mature markets. As it is known, shuffling removes the effect of the long-range time correlations. Therefore, the coupled criticality parameter is calculated for the shuffled S&P500 data and TEDPIX. Results show that the long-range correlation of S&P500 data has a significant effect on the coupled criticality behavior of S&P500 and TEDPIX. [ABSTRACT FROM AUTHOR]

In this paper, we apply Markov-modulated models to value continuous-installment options of the European style with a partial differential equation approach. Under regime-switching models and the opportunity for continuing or stopping to pay installments, the valuation problem can be formulated as coupled partial differential equations (CPDE) with free boundary features, which in many ways is similar to the free boundary problem for vanilla American options due to the possibility of early exercise. In this paper, to value the continuous-installment options under the proposed model with a numerical approach, we first express the truncated CPDE as a linear complementarity problem (LCP), and then a finite element method is applied to solve the resulting variational inequality. We studied the existence and uniqueness of the solution and analyzed the stability of the proposed method under some appropriate assumptions, then we illustrated the error estimates on the appropriate spaces. We presented some numerical results to examine the rate of convergence and accuracy of the proposed method for the pricing problem under the regime-switching model. [ABSTRACT FROM AUTHOR]

AbstractThis paper develops an inferential framework for matrix completion when missing is not at random and without the requirement of strong signals. Our development is based on the observation that if the number of missing entries is small enough compared to the panel size, then they can be estimated well even when missing is not at random. Taking advantage of this fact, we divide the missing entries into smaller groups and estimate each group via nuclear norm regularization. In addition, we show that with appropriate debiasing, our proposed estimate is asymptotically normal even for fairly weak signals. Our work is motivated by recent research on the Tick Size Pilot Program, an experiment conducted by the Security and Exchange Commission (SEC) to evaluate the impact of widening the tick size on the market quality of stocks from 2016 to 2018. While previous studies were based on traditional regression or difference-in-difference methods by assuming that the treatment effect is invariant with respect to time and unit, our analyses suggest significant heterogeneity across units and intriguing dynamics over time during the pilot program. [ABSTRACT FROM AUTHOR]

By constructing a complex network model grounded in time series analysis, this study delves into the intricate relationships between the stock markets of 18 countries worldwide. Utilizing 31 distinct time series distance functions to formulate the network, we employ Hamming distance to quantify the resemblance between networks derived from different distance functions. By modulating the network density through distance percentiles ( p = 0.1 , 0.3, 0.5), we demonstrate the similarity of various distance functions across multiple density levels. Our findings reveal that certain distance functions exhibit high degrees of similarity across varying network densities, suggesting their potential for mutual substitution in network construction. Furthermore, the centroid network identified via hierarchical cluster analysis highlights the similarities between the stock markets of different nations, mirroring the intricate interconnections within the global financial landscape. The insights gained from this study offer crucial perspectives for comprehending the intricate network structure of global financial time series data, paving the way for further analysis and prediction of global financial market dynamics. [ABSTRACT FROM AUTHOR]

Changing patterns of risk aversion may follow a non-linear counter-cyclical process. However, the evidence so far has not considered developing cryptocurrency markets. Given some unique features of cryptocurrencies, it is interesting to distinguish how these assets differ from traditional products. This paper investigates the time effects of periodicity on risk aversion for a selection of major cryptocurrencies compared to major financial assets. Significant periodic time-varying patterns are identified when analysing risk aversion. Further, bilateral and bidirectional Granger causalities are identified within cryptocurrencies, as well as between cryptocurrencies and traditional financial assets. Bitcoin is identified as a leading information transmitter of the spillover of risk aversion upon other cryptocurrencies, while estimated risk aversion of traditional financial markets plays a dominant role in the spillover processes upon the cryptocurrency cluster. The latter finding presents further evidence of developing cryptocurrency market maturity. The COVID-19 pandemic is found to have significantly influenced the connectedness of risk aversion among cryptocurrency and traditional financial markets. [ABSTRACT FROM AUTHOR]

Regime-switching models provide an efficient framework for capturing the dynamic behavior of data observed over time and are widely used in economic or financial time series analysis. In this paper, we propose a novel and robust hidden semi-Markovian regime-switching (rHSMS) method. This method uses a general ρ -based distribution to correct for data problems that contain atypical values, such as outliers, heavy-tailed or mixture distributions. Notably, the rHSMS method enhances not only the scalability of the distribution assumptions for all regimes, but also the scalability to accommodate arbitrary sojourn types. Furthermore, we develop a likelihood-based estimation procedure coupled with the use of the EM algorithm to facilitate practical implementation. To demonstrate the robust performance of the proposed rHSMS method, we conduct extensive simulations under different sojourns and scenarios involving atypical values. Finally, we validate the effectiveness of the rHSMS method using monthly returns of the S &P500 Index and the Hang Seng Index. These empirical applications demonstrate the utility of the rHSMS approach in capturing and understanding the complexity of financial market dynamics. [ABSTRACT FROM AUTHOR]

We quantify a production-based asset pricing model with fixed and variable capitals based on the GMM structural estimation. The estimation results match the stylized characteristics of the U.S. stock market return and help enlighten the equity premium puzzle. In addition, the model performance is reliable throughout the business cycle. [ABSTRACT FROM AUTHOR]

In this paper, I examine the joint role of the previous game's outcome with and without the spread on wagers' preferences and outcomes in the NFL gambling market. The results show that gamblers are more likely to wager on the home team if they covered the spread in their previous game, while they are less likely to bet on the home team if the visiting team won their last game (not considering the spread). Further results show that teams are more likely to cover the spread if they covered the spread but lost their previous game. Profitable betting strategies are then put forward based on this information. [ABSTRACT FROM AUTHOR]

This research explores the link between stock markets and banking deposits in South Asian (Pakistan, India, Sri Lanka, Nepal) countries. This study empirically examines the systemic risk potential of financial institutions in South Asia using current systemic risk statistics. Yearly data on stock prices and banking deposits from January 2000 to December 2020 were analyzed using a two-stage process. In the first phase, we measure VaR (value at risk), and in the second step, we measure the DCC GARCH model for our empirical analysis. The study findings reveal systemic risk spillover between the stock markets of South Asian countries and the relevant country's banking system deposits. The policymakers can use our study findings to create a more sustainable financial sector. [ABSTRACT FROM AUTHOR]

This paper employs an innovative event study methodology to demonstrate the impact of climate change on the NASDAQ index from the unique perspective of extreme weather events. This is achieved through the application of the event study methodology to a total of 526 biological, climatic, geological, hydrological, and meteorological disasters of climate change occurring in the U.S. during the period of 2000–2019. The results of the study demonstrate that: ① it can be generally observed that the five dimensions of climate change have a significant impact on stock returns. ② Empirical evidence indicates that the impact of different climate change dimensions on the return rate of stocks from NASDAQ stocks varies. In contrast, the biological and hydrological dimensions have a significantly negative impact on the return rate of stocks from the NASDAQ index, while the climate dimension has a significantly positive impact on the return rate of stocks from the NASDAQ index. ③ From the perspective of time, the impact of the five dimensions of climate change on the stock yield exhibits certain non-linear characteristics. This can be observed in the phenomenon of shock reversal, which occurs before and after the event. [ABSTRACT FROM AUTHOR]

Herd behavior, which causes market participants not to act rationally when making investment decisions, is a concept within the scope of behavioral finance. In this study, the existence of herd behavior in BRICS-T Countries was investigated using the Christie and Huang (1995) and Chang, Cheng and Khorana (2000) models, which are among the herd behavior measurement methods. The analysis was carried out using daily market closing data between 2011 and 2021. The regression analysis results for all rising and falling markets were examined separately. In the CH (1995) model, the presence of herding behavior was detected in the emerging market periods of Brazil, Russia, India, South Africa and Turkey, while no herding effect was observed in Chinese financial markets. While the CCK (2000) model found herd behavior in the Indian emerging market, no herd effect was found in this model applied in other BRICS-T countries. According to the analysis results, no asymmetric effect of volatility on herd behavior was observed. [ABSTRACT FROM AUTHOR]

Countries are experiencing a surge in political, economic, and financial integration, consequently shaping international market linkages. Financial crises can rapidly spread between countries, emphasizing the need to monitor and assess stock market connections. This paper investigates the degree of financial market connectedness using daily stock returns from January 1997 to August 2017 for 13 countries, both developed and developing. The connectedness measure of Diebold and Yilmaz (2009-2012) is applied to examine the connectedness of stock market returns and the direction of spillovers for all stock markets. This study also analyzes the dynamic connectedness from the U.S. stock market to all other stock markets. The results indicate that the U.S. stock market is the most influential stock market to the others. The results of the dynamic analysis show that connectedness changes over time, specifically during turmoil periods. Most developed countries are transmitters of return spillover shocks while developing countries are recipients. [ABSTRACT FROM AUTHOR]

The Financial Risk Meter (FRM) employs Quantile-LASSO regression to identify systemic financial risk and dependencies among tail events across financial assets. This paper establishes, both theoretically and empirically, a meaningful economic relationship between the FRM index, derived from the penalization parameter in quantile LASSO regression, and the volatility of assets' pricing kernels, the attainable maximal Sharpe ratio, and market volatility. Despite the rapid growth of the crypto market and its increasing integration with traditional financial markets, there remains a dearth of risk measures in this space. $ FRM@Crypto $ FRM @ Crypto exhibits robust predictive capabilities in anticipating future market risk, potentially filling a critical void in this market. [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]

Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student's t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices. [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]

This article brings back the debate on central bank independence from its historical origins and its relation to financialization. With financial dominance, States have less influence over central bank policies as they gain greater action capacity through monetary policy strengthening. Central bank independence is an expression of financialization as it makes the monetary authority more susceptible to financial market pressures and simultaneously limits the participation of the State and society in conducting monetary policy. [ABSTRACT FROM AUTHOR]

This article provides empirical evidence on the zero-leverage phenomenon for a sample of European listed firms for the period 1995-2016. It is shown that there are two types of firms with zero leverage: the financially constrained firms that face obstacles in obtaining external finance, as predicted by the financial constraints hypothesis; and the financially unconstrained firms that maintain zero leverage as a consequence of a financing decision, which supports the financial flexibility hypothesis. The zero-leverage phenomenon is also influenced by the financial system that prevails in each country, being boosted (inhibited) in market-based (bank-based) financial systems, and by the country's macroeconomic conditions, with the recent financial and sovereign debt crises increasing the propensity for zero leverage in marketbased countries. We also find that the financial flexibility hypothesis seems to be more important in market-based systems and that the financial constraints approach did not gain importance during the crisis period. Our results are robust to the use of alternative measures of debt conservatism, explanatory variables, and econometric methods and maintain their validity when we allow for endogeneity in firm size and dividend payments. [ABSTRACT FROM AUTHOR]

The establishment of a financial system should not only consider the current situation, but also need to refer to the past. Due to the memory of the fractional derivative, a fractional-order system can more effectively describe the historical significance of the financial system. Most scholars use the prediction–correction scheme to study fractional-order systems. This paper provides a higher-precision numerical method for the financial system, which more effectively simulate the system. Based on the definition of the Grünwald–Letnikov fractional derivative, the integer-order system with nonconstant demand elasticity is extended to the fractional-order setting, and its dynamic behavior is studied, with some novel chaotic attractors found. The research results are helpful for improving the understanding of the financial system and the financial market and for predicting financial risks. [ABSTRACT FROM AUTHOR]

Economic growth in South Africa over the last decade has been disappointing. Even before the emergence of the Covid-19 pandemic, the economy was underperforming. South Africa needs high and sustained growth to reduce the rate of unemployment, which has remained stubbornly high at over 30% for many years. Fund flows may provide capital to spur this needed growth. Fund flows are a critical issue in emerging markets like South Africa as sudden reversals have also been associated with severe destabilizing effects. These concerns and the substantial growth in fund flows to South Africa in the past two decades motivate this study. We examine the relationships among fund flows, uncertainty measures, economic indicators, and financial market variables in South Africa. We estimate several vector autoregressions to account for the inherent feedback among fund flows and economic and financial variables. We find that the term structure of interest rates (TERM) is negatively related to equity fund flows (EFF) while positively related to bond fund flows (BFF). While BFF and EFF are both negatively related to most uncertainty measures, BFF is more sensitive to downside risk indictors than EFF. We show that the economic and financial development of South Africa is impacted by fund flows with GDP being more strongly impacted by BFF while stock returns are impacted by both BFF and EFF. We also uncover evidence that commodity prices increase BFF. Next, we show that the volatility of the South African equity market immediately reduces equity prices which attracts EFF after a two-to-three-month lag. Finally, we uncover convincing evidence that illiquidity as measured by effective spread reduces EFF to South Africa. The ability to better predict the direction EFF and BFF will allow policy makers to impose circuit breakers to mitigate the adverse impacts of sudden investment reversals. This is particularly relevant to the current situation where additional financial uncertainty due to the energy crisis in South Africa coupled with political instability may increase the likelihood of such reversals. [ABSTRACT FROM AUTHOR]

We propose a portfolio optimization method with a multi-trend objective and an accelerated quasi-Newton method (MTO-AQNM). It leverages a BFGS-based quasi-Newton algorithm and incorporates an ℓ 1 regularization term and the self-funding constraint. The MTO is designed to identify multiple trend reversals. Different trend reversals are asymmetric, and we hoped to extract rich and effective information from them. The AQNM adopts the BFGS method with the Wolfe conditions, which reduces computational complexity and improves convergence speed. We wanted to evaluate the performance of our algorithm through financial markets that were asymmetric in all respects. To this end, we conducted comprehensive experimental approaches on six benchmark data sets of real-world financial markets that were asymmetric in time, frequency, and asset type. Our method demonstrated superior performance over other state-of-the-art competitors across several mainstream evaluation metrics. [ABSTRACT FROM AUTHOR]