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Financial markets reveal information that firm managers can utilize when making equity value-enhancing investment decisions. However, for firms with risky debt, such investments are not necessarily socially efficient. Despite this friction, we show that learning from prices improves investment efficiency. This effect is asymmetric, however, as investors learn less about projects that decrease the riskiness of cash flows: efficiency is lower for diversifying investments than for focusing (risk-increasing) investments. This also implies that investors' endogenous learning further attenuates risk shifting but amplifies debt overhang. Our model provides a novel channel through which learning from financial markets affects agency frictions between stakeholders. [ABSTRACT FROM AUTHOR]

We examine the association between margin requirements and the market's efficiency in incorporating firm-specific and market-level public news. Combining the Fed's 22 changes in margin requirements with a hand-collected sample of earnings announcements between 1934 and 1975, we show that higher margin requirements induce greater delay in incorporating earnings information into prices. We draw similar conclusions when we analyze the Hou and Moskowitz (2005) price delay measure, as well as indirect measures of leverage constraints over recent years. Further tests suggest that, despite the Fed's expressed intent to curtail excess speculation, higher margin requirements restrict trading by arbitrageurs more than noise traders. [ABSTRACT FROM AUTHOR]

We develop a quantitative model with imperfections in domestic and international financial markets that generates strong effects of U.S. monetary policy on emerging markets (EMs). Financial imperfections prevent arbitrage both between local EM lending and borrowing rates, and between local-currency and dollar borrowing rates. An adverse feedback effect between financial health and external conditions amplifies the domestic "financial accelerator," leading to large cross-border spillovers of U.S. monetary policy shocks. The model implies a link between uncovered interest parity violations and local credit spreads, a prediction we show the data strongly supports. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

This article reflectively applies measurement tools to gage whether a renowned financier and champion of shareholder capitalism, in 20th century business history, might be categorized as a corporate psychopath. The article examines aspects of the career of the outstanding financial investment manager, Bernie Madoff. Psychopaths and corporate psychopaths are defined as background to the article. Gauges of corporate psychopathy and psychopathy are outlined which could be modified by market research companies to identify corporate psychopathy in organisations as a way of aiding investment decisions into such organisations. The current article concludes that insolvencies such as those at Madoff's investment company, have been distinguished by CEOs being present who were simultaneously the lauded agents of financial market capitalism and who embodied the traits of the corporate psychopath. The examination of potential corporate psychopaths using this historical methodology helps inform ideas about what the effects of psychopathic leadership may be within economies and gives new insights into the reasons for the greed, risk taking, and unethical practices found in financial markets. Findings support the accepted view that corporate psychopaths can be discovered in senior roles in the financial services sector. This current paper provides new avenues for research offerings from market research companies. For example, business to business researchers could undertake research to identify firms more likely to be longitudinally viable, sustainable and less likely to collapse (i.e., non-psychopathic firms). Investment companies like pension funds could use such research to identify firms that are less risky, more ethical, better led, and therefore safer to invest in. [ABSTRACT FROM AUTHOR]

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An assimilation bias occurs when people's evaluative judgment is positively influenced by a previously observed signal. We study this effect by examining investors' appraisal of M&A deals announced 1 day after other firms in the same 1-digit SIC as the merging parties release earnings surprises. Consistent with assimilation effects, acquirers' M&A announcement stock return initially correlates with the previous day's earnings surprises. This effect reverses after 1 week. Assimilation generates other distortions as more positive surprises are related to increases in bid competition, takeover premiums, and withdrawn M&As. Evidence from IPOs corroborates the presence of assimilation effects in financial markets. [ABSTRACT FROM AUTHOR]

This paper documents that U.S. equity returns are large and positive during the opening hours of European markets. These returns are pervasive and highly economically and statistically significant. Consistent with models of inventory risk, we demonstrate a strong relationship with order imbalances at the close of the preceding U.S. trading day. Rationalizing unconditionally positive "overnight drift" returns, we uncover an asymmetric reaction to demand shocks: market sell-offs generate robust positive overnight reversals, while reversals following market rallies are much more modest. We argue that demand shock asymmetry can arise in inventory management models with time-varying market maker risk-bearing capacity. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

We study the observability of investors' information-acquisition activities in financial markets. Improving observability leads to two strategic effects on information acquisition: (1) the pricing effect, which arises from interactions between investors and the market maker and can encourage or discourage information acquisition, and (2) the competition effect, which concerns interactions among investors and always encourages information acquisition. We apply our theory to study voluntary and mandatory disclosures of corporate site visits. When the competition effect dominates, investors voluntarily disclose their visits. When the pricing effect dominates, mandatory disclosure is effective. Our analysis sheds novel light on Regulation Fair Disclosure. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Sustainable finance is often discussed as a solution to the climate crisis, but its impacts are limited and its discourse focuses on mobilising private investments through public de-risking, without considering direct government action. We argue that this is due to an implicit reference to mainstream economic theory assuming that an active state leads to time inconsistency problems and crowding-out effects. However, these assumptions have been sufficiently refuted as public investments may actually crowd-in private capital. We therefore propose a paradigm shift towards what we call Public Sustainable Finance, aimed at empowering the role of the state in the green transition on the discursive, policy, and political economy levels. Studying the case of Germany, we show how Public Sustainable Finance can be introduced despite tight fiscal regimes. To this end, we propose that the Climate- and Transformation Fund be given its own borrowing powers. By borrowing an average of 23 billion euros annually from 2024 to 2030, the existing financing gap that has been exacerbated following the November 2023 constitutional court ruling can be closed, enabling a more rapid and effective green transition.

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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 article examines the setting of the legal regime governing third country access to the UK financial market, in light of the political, market, and legal disruption associated with the UK withdrawal from the EU. It considers the UK reform context and the priority being given to securing UK financial market competitiveness, identifies a related and significant liberalization of the third country regime, and examines the implications for the UK, the EU, and for international financial market access. [ABSTRACT FROM AUTHOR]

This paper discusses the role of the third-country regime and equivalence from the Swiss perspective. It provides an analysis of the evolution of the Swiss approach. The various reactions to EU developments have ranged from the attempt to implement a reciprocity principle, to the resort to unilateral recognition. An overarching purpose of the Swiss equivalence framework has consisted of the relentless pursuit of a competitiveness objective. Yet the decline of equivalence as a market access mechanism has led to favouring other market access routes. This paper concludes that the recent reforms have initiated a roadmap towards an increasing autonomy of Swiss financial market law. [ABSTRACT FROM AUTHOR]

The future of equivalence in the EU financial sector does not look all that bright. Granting third-country entities access to the EU financial markets is increasingly less about reliance on equivalent third-country rules and equivalent third-country supervision. Considerations other than equivalence are increasingly given more relative weight, either explicitly or implicitly, in particular (i) EU financial stability risks, (ii) EU market integrity, (iii) EU retail investor protection, (iv) preserving or regaining EU autonomy in an increasingly complex world (e.g., Brexit and the economic rise of China), (v) protecting EU entities against competitors from third countries, (vi) trying to conquer market share in a lucrative business (e.g., derivatives clearing), and (vii) other EU policy considerations. These considerations are gaining ground not merely in relation to the question of whether an equivalence framework should even be in place, and if so, whether equivalence decisions should indeed be taken, but also with regard to the regulatory and supervisory framework that the EU applies to (1) entities established in third countries for which the Commission has adopted equivalence decisions, and (2) third-country branches established in the EU. In view of this, the Territorial Approach and the Extra-Territorial Approach are clearly on the rise, in each case to the detriment of the Equivalence Approach. At the same time, loopholes in the execution of the Territorial Approach continue to exist at the national level, at least for the time being. [ABSTRACT FROM AUTHOR]

Prior research hasn't provided convincing empirical support for two crucial issues: how investor sentiment can be measured, and can it be used to forecast stock market performance? To bridge this divide, we present a novel empirical technique based on investor sentiment and Bayesian vector autoregressive (BVAR) models to estimate stock returns. Sentiment has always had a substantial impact on the determination of stock prices in US financial markets, even during the subprime crisis. Impulse responses show that a positive shock to investor sentiment pushes equity returns above their equilibrium positions, with the maximum deviation being achieved around two months after the trigger event followed by a return to equilibrium after 6 months. Compared to BVAR models with priors commonly used in the literature, our model enables a very efficient restriction of the coefficients to be estimated without reducing the explanatory power of the model. Moreover, it provides better forecasting of stock returns than other BVAR models beyond 3 months. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has given rise to a spike in financial market volatility. In this paper, we attempt to assess the effects of financial & news-driven uncertainty shocks in growing Asian economies, using country-specific bond volatility shocks as a measure of local interest rate uncertainty. Also, we contrast the effects of local uncertainty with global stock market uncertainty. Using bond market data from nine Asian markets, we uncover a transmission mechanism of uncertainty shocks via the bond market. The mechanism works as a crowding-out effect due to government-led excessive market borrowing with supply-side consequences for the private sector, as opposed to economic policy or global stock market uncertainty which works more like a demand shock as in the literature. We conclude that countries with growing fiscal deficits that entail a larger government bond market or higher current account deficits, tend to experience an increase in the cost of borrowing due to this bond market volatility or interest rate uncertainty shocks. [ABSTRACT FROM AUTHOR]

In this paper, we utilize the Chicago Board Option Exchange (CBOE) implied volatility indices to estimate the time-frequency information transmission among financial markets from 01.08.2008 to 31.10.2019. In doing so, we utilize the rolling window wavelet correlation (RWWC), Diebold & Yilmaz (The Economic Journal 119: 158–171, 2012), and Barunik & Krehlik (Journal of Financial Econometrics 16: 271–296, 2018). Our empirical findings suggest short-term and long-term dynamic connectedness between implied volatility indices of alternative assets. The long-term analysis findings suggest potential hedging and diversification opportunities that can be exploited by taking offsetting positions across volatility indices. The findings confirm heterogeneity between short-term and long-term connectedness results. Our findings also show superior out of sample hedging effectiveness of GVZ. The implications of the findings are further discussed in the paper. [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]